This podcast is generated using the Wondercraft AI platform (https://www.wondercraft.ai/?via=etienne), a tool that makes it super easy to start your own podcast, by enabling you to use hyper-realistic AI voices as your host. Like mine! Get a 50% discount the first month with the code AIUNRAVELED50
Join us as we delve into groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the latest trends in ChatGPT and the recent merger of Google Brain and DeepMind, to the exciting developments in generative AI, we’ve got you covered with a comprehensive update on the ever-evolving AI landscape. In today’s episode, we’ll cover AI eye scans for early detection of Parkinson’s disease, Figma’s Jambot AI assistant for brainstorming and content rewriting, methods for language model selection, a groundbreaking brain-computer interface, business news on Nvidia and Hugging Face, Amazon Prime Video’s use of AI for NFL viewing, partnerships and investments in the AI industry, and various AI tools mentioned throughout the script.
Researchers have made significant progress in the early detection of Parkinson’s disease through the use of AI-powered eye scans. By studying retinal scans, they have discovered markers that can predict the onset of the condition up to seven years before any symptoms become apparent.
Parkinson’s disease is a degenerative neurological disorder that affects dopamine levels in the brain. It causes a range of motor symptoms such as shaking, rigidity, and difficulty with movement. Currently, there is no cure for Parkinson’s, but early detection can potentially lead to more effective treatment and management strategies.
The research team believes that their findings could have significant implications for identifying individuals who are at high risk of developing Parkinson’s. By using retinal scans as a pre-screening tool, healthcare professionals could potentially detect the disease at its earliest stages and implement preventive measures.
Early intervention in Parkinson’s has been shown to slow down the progression of the disease and alleviate symptoms. Therefore, using AI technology to analyze eye scans and identify potential markers could help improve the quality of life for individuals at risk of developing this neurodegenerative disorder.
While further research and validation are needed, this breakthrough paves the way for more precise methods of early detection and opens the door to new opportunities for timely intervention in Parkinson’s disease.
Figma recently introduced Jambot, an AI assistant integrated into their whiteboard software, FigJam. Jambot utilizes the power of ChatGPT to assist users in brainstorming, creating mind maps, providing quick answers, and rewriting content.
By leveraging Jambot, users can significantly increase productivity, particularly when it comes to initiating first drafts. Figma has been actively enhancing its design suite, with recent additions like Custom Color Palettes in FigJam and improvements to DevMode. The company aims to integrate AI features into its platform and has made strategic acquisitions of Diagram and Clover Notes to support this initiative.
The significance of Jambot lies in its potential to enhance collaboration and boost productivity for end users. This AI-powered assistant allows for quick answers and supports the generation of initial drafts, thereby saving users time and streamlining their creative processes.
In a separate development, researchers have introduced CoDeF, a new video representation that offers a unique approach to video processing.
CoDeF comprises a canonical content field and a temporal deformation field optimized for reconstructing the target video. By enabling image algorithms to be applied to videos, CoDeF achieves superior cross-frame consistency and tracking of non-rigid objects. It can perform video-to-video translation and keypoint tracking without the need for training. Overall, CoDeF simplifies video editing, allowing for seamless application of image edits to entire videos, unlocking greater creative possibilities, and reducing editing time.
One way to choose a language model is by using an off-the-shelf LLM.
There are open-source LLMs available, some of which are almost as capable as GPT-3.5. Another option is to fine-tune an open-source model or even pre-train your own language model. However, pre-training your own model is not recommended for most individuals.
Another option is to fine-tune an off-the-shelf LLM. This can help improve its performance for your specific task. Additionally, you have the option of hosting an open-source model. There are many open-source LLMs available, and with the release of LLaMA 2, there is finally an open-source model that is almost as capable as GPT-3.5.
Furthermore, you can consider fine-tuning an open-source model with a service. This can help bridge the performance gap and some providers, such as Lamini, offer hosted fine-tuning options. Alternatively, you can choose to fine-tune a model yourself using a GPU cloud. This process is similar to traditional machine learning model training and may require the use of a GPU cloud service like Together or Lambda.
Lastly, for more adventurous individuals, it is possible to pretrain your own LLM. However, it is important to note that this is not recommended for most people due to the power of pretrained base models. Nonetheless, there are some well-known examples, such as BloombergGPT, who have successfully pre-trained their own language models.
AI model gives paralyzed woman the ability to speak through a digital avatar
This groundbreaking achievement represents a remarkable step forward in the field of brain-computer interfaces. By decoding brain signals related to speech and facial movements, electrodes are able to capture the woman’s intended communication. Through advanced AI models, phonemes are identified, greatly enhancing speed and accuracy.
To ensure a personalized experience, the digital avatar’s voice and expressions are tailored to the user’s pre-injury patterns. This not only allows for effective communication but also empowers the individual to express themselves naturally.
The implications for paralysis patients are significant. This achievement marks a milestone in directly extracting speech and expressions from thoughts, offering the potential for more natural and seamless communication in the future. It far surpasses the capabilities of existing technologies, bringing us closer to a viable FDA-approved solution.
Looking ahead, the research team is diligently working on a wireless version of the interface that does not require a physical tether. This innovation could significantly enhance independence and improve social interactions for individuals with paralysis.
While this achievement represents a major breakthrough, further refinement is necessary before its widespread clinical use can be realized. Nonetheless, the potential for this technology to revolutionize the lives of paralysis patients is unprecedented, giving hope for a future with improved communication and increased autonomy.
Nvidia has seen remarkable financial success, generating $6 billion in profit, largely driven by the AI boom.
The company’s revenue soared to $13.5 billion, with a significant contribution from the high demand for its generative AI chips, particularly in data centers. Nvidia’s dominance in the AI market has positioned it ahead of major competitors like Intel and AMD, who are now shifting their strategies to focus on AI.
In other news, Meta has launched Code Llama, an advanced LLM (large language model) that can generate code and natural language related to code.
Code Llama supports popular programming languages such as Python, C++, Java, and more. The models released by Meta come in three sizes, each with varying parameters, and have proven to outperform open-source LLMs.
Additionally, Hugging Face, an open-source AI model repository, has received substantial investments from tech giants like Google, Amazon, Nvidia, and Salesforce. This funding highlights the importance of the open-source community and the growing demand for AI model access. Hugging Face has also introduced SafeCoder, a code assistant solution for enterprises that allows them to create proprietary Code LLMs based on their own codebase, ensuring data security and compliance.
These developments in the AI industry signify the increasing significance of AI technology in various sectors, from gaming to data centers. The investments made and the introduction of new solutions will further advance AI adoption and accelerate innovation in programming and AI development.
Amazon Prime Video is set to elevate the sports viewing experience with the integration of artificial intelligence (AI) technology. Specifically, they are revolutionizing the way we watch the NFL’s Thursday Night Football (TNF) by introducing various AI-driven features. These features aim to provide fans with a deeper level of engagement and a more interactive experience.
For the 2023 season, Prime Video is introducing a range of AI-powered tools that offer fans deeper insights and real-time statistics during TNF. These tools include predictive analytics to anticipate blitzes, identify open players, analyze fourth-down decisions, and even visualize the likelihood of a successful field goal attempt. By incorporating these features, Prime Video aims to enhance the live viewing experience for fans.
In addition to these advancements, Prime Video will exclusively stream the NFL’s first Black Friday game, which presents an exciting opportunity to integrate interactive shopping elements into the viewing experience. This move not only enhances fan engagement but also offers Amazon the potential to expand its e-commerce reach.
The significance of incorporating AI-driven features into sports broadcasts goes beyond simply making viewing more entertaining. It provides fans with real-time analysis and predictive insights that enhance their understanding and appreciation of the game’s intricacies. This breakthrough sets a precedent for the integration of AI into other sports, such as football (soccer), tennis, basketball, and more.
If you’re seeking guidance on project management, look no further than ChatGPT. This AI-powered tool can provide valuable insights on how to structure your project, effectively manage your team, and monitor progress. Whether you’re a beginner or leading a small team, ChatGPT can assist you in overcoming challenges and aligning your project with modern project management principles. Simply provide details about your team, project, challenges faced, and specific areas of guidance needed, and ChatGPT will generate a detailed plan to help you succeed.
Stability AI has partnered with NVIDIA to enhance the speed and efficiency of their text-to-image generative AI product, Stable Diffusion XL. Through the integration of NVIDIA TensorRT, a performance optimization framework, Stability AI has achieved significant improvements. Notably, the collaboration has resulted in a doubling of performance on NVIDIA H100 chips, enabling the generation of HD images in just 1.47 seconds. The NVIDIA TensorRT model also outperforms the non-optimized model on A10, A100, and H100 GPU accelerators in terms of latency and throughput. This collaboration aims to improve both the speed and accessibility of Stable Diffusion XL.
Figma has introduced Jambot, an AI assistant integrated into its whiteboard software, FigJam. Jambot assists with brainstorming, mind mapping, providing quick answers, and content rewriting, leveraging the power of ChatGPT. Users can enhance their productivity by utilizing Jambot to initiate first drafts. Figma continues to enhance its design suite, with recent additions such as Custom Color Palettes in FigJam and improvements to DevMode. The company has expressed intentions to incorporate more AI features into its platform, as demonstrated by its acquisition of Diagram and Clover Notes.
Google plans to integrate AI-driven security enhancements into its Google Workspace products, including Gmail and Drive. These updates aim to enhance the zero-trust model by combining it with data loss prevention (DLP) capabilities. Within Drive, AI capabilities will automatically classify and label sensitive data, applying appropriate risk-based controls. Enhanced DLP controls in Gmail will prevent users from accidentally attaching sensitive data. Moreover, Google intends to introduce context-aware controls in Drive, enabling administrators to define criteria for sharing sensitive data based on device location. Additionally, Google plans to introduce client-side encryption on mobile versions of Gmail, Calendar, Meet, and other Workspace tools, giving customers control over encryption keys. These features will be rolled out in the upcoming months.
NVIDIA’s Q2 earnings of $13.51 billion highlight its prominent position in the generative AI industry. With revenues double that of the same period last year, the company has exceeded Wall Street expectations. Demand for NVIDIA’s A100 and H100 AI chips remains high among cloud service providers and enterprise IT system providers for building and running AI applications. The company’s data center business generated $10.32 billion in revenue, surpassing its gaming unit. This significant growth and success underscore NVIDIA’s dominance in the generative AI boom.
Deloitte has launched the Global Generative AI Market Incubator, which aims to support Indian and global enterprises. Aligned with the Indian government’s focus on nurturing tech talent and promoting AI-driven opportunities, this initiative seeks to foster innovation and growth in the field.
Researchers have introduced CoDeF, a system that simplifies the process of applying image modifications to entire videos, transforming the landscape of video style transfers and editing using AI. This development provides a seamless experience for AI-powered video edits.
Germany has committed to investing over €1.6 billion in AI in the coming years. The plan includes doubling public research funding for AI to nearly 1 billion euros over the next two years, positioning Germany closer to China and the United States in terms of AI advancement.
Twilio is expanding its CustomerAI capabilities by incorporating generative and predictive AI tools. The company has been actively building partnerships and technologies for AI, including a recent collaboration with OpenAI. Additionally, Twilio is improving profile organization and sharing through a partnership with Databricks, leveraging their Delta Lake data lakehouse and Delta Sharing technologies.
Today, we’re going to discuss some trending AI tools that are making waves in various industries. These tools are designed to make your life easier and more efficient. Let’s dive in.
First up is JustBlog AI, a powerful tool that allows you to create SEO articles and publish them on JustBlog.ai or WordPress. This tool supports links, images, metadata, tags, and more, ensuring that your articles are top-notch and optimized for search engines.
Next, we have SafeWaters AI, which provides 7-day shark attack risk forecasts at any beach using over 200 years of data. This is particularly useful for surfers, authorities, and beachgoers who want to stay informed and safe while enjoying the water.
If you’re in need of a smarter web building assistant, look no further than Levi V2. This no-code AI assistant comes with a new UI/UX, visual effects, AI commands, and more. Try it out for free and see how it can simplify your web design process.
For pastors and religious leaders, Pastors AI offers custom chatbots based on church sermons. By inputting a YouTube video, you can get sermon summaries, discussion guides, and quotes, making it easier to engage with your congregation.
If interior design is your passion, AI Interior Decor Your Home is the perfect app for you. It uses AI to provide interior design ideas for every room and helps address any design dilemmas you may have.
Supawaldo is a user-friendly photo sharing platform that allows you to upload, manage, and share event photos with guests. You can even find specific photos by uploading a selfie, making it a convenient and efficient way to capture and share memories.
Ghostwriter is an AI writing app that allows you to write in any style. Whether you want to imitate an author, create lyrics, or draft copy in your brand voice, Ghostwriter has got you covered.
Lastly, we have Mediar, an AI assistant that analyzes health data from wearables and user input. It provides personalized insights and recommendations via WhatsApp, helping you take control of your health and well-being.
That wraps up our discussion on these trending AI tools. If you’re interested in starting your own podcast, be sure to check out the Wondercraft AI platform, where you can use hyper-realistic AI voices as your host. Use the code AIUNRAVELED50 for a 50% discount on your first month.
And for all you AI Unraveled podcast listeners out there, if you want to expand your understanding of artificial intelligence, I highly recommend checking out “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence” by Etienne Noumen. It’s available at Shopify, Apple, Google, or Amazon. Grab your copy today and unravel the mysteries of AI.
In today’s episode, we discussed groundbreaking advancements in AI, from early detection of Parkinson’s disease to a brain-computer interface that allows a paralyzed woman to speak and express emotions, as well as the impact of AI on industries such as video editing, sports viewing, and e-commerce. We also explored the various language model options available and highlighted notable AI tools in the market. Thanks for listening to today’s episode, I’ll see you guys at the next one and don’t forget to subscribe!
Navigating the Revolutionary Trends of July 2023. Latest AI Trends in July 2023
Welcome to your go-to resource for all things Artificial Intelligence (AI) and Machine Learning (ML)! In a world where AI is constantly redefining the realm of possibility, it’s vital to stay informed about the most recent and groundbreaking developments. That’s precisely why our July 2023 edition aims to deliver a comprehensive exploration of this month’s hottest AI trends. From cutting-edge applications in healthcare, finance, and entertainment, to breakthroughs in machine learning techniques, we’ll delve into the stories shaping the landscape of AI. Strap in and join us as we journey through the fascinating world of artificial intelligence in July 2023!
Navigating the Revolutionary Trends of July 2023: July 29th-31st, 2023
Dissolving circuit boards in water sounds better than shredding and burning;
Arizona law school embraces ChatGPT use in student applications;
Google’s RT-2 AI model brings us one step closer to WALL-E;
Android malware steals user credentials using optical character recognition;
Most of the 100 million people who signed up for Threads stopped using it;
Stability AI releases Stable Diffusion XL, its next-gen image synthesis model;
US senator blasts Microsoft for “negligent cybersecurity practices”;
OpenAI discontinues its AI writing detector due to “low rate of accuracy”;
Windows, hardware, Xbox sales are dim spots in a solid Microsoft earnings report;
Twitter commandeers @X username from man who had it since 2007;
Navigating the Revolutionary Trends of July 2023: July 28th, 2023
Free courses and guides for learning Generative AI
Generative AI learning path by Google Cloud. A series of 10 courses on generative AI products and technologies, from the fundamentals of Large Language Models to how to create and deploy generative AI solutions on Google Cloud [Link].
Generative AI short coursesbyDeepLearning.AI – Five short courses on generative AI including LangChain for LLM Application Development, How Diffusion Models Work and more. [Link].
LLM Bootcamp: A series of free lectures by The full Stack on building and deploying LLM apps [Link].
Building AI Products with OpenAI – a free course by CoRise in collaboration with OpenAI [Link].
Free Course by Activeloop on LangChain & Vector Databases in Production [Link].
Pinecone learning center – Lots of free guides as well as complete handbooks on LangChain, vector embeddings etc. by Pinecone[Link].
Build AI Apps with ChatGPT, Dall-E and GPT-4 – a free course on Scrimba[Link].
Gartner Experts Answer the Top Generative AI Questions for Your Enterprise – a report by Gartner [Link]
GPT best practices: A guide by OpenAIthat shares strategies and tactics for getting better results from GPTs [Link].
OpenAI cookbook by OpenAI – Examples and guides for using the OpenAI API [Link].
Prompt injection explained, with video, slides, and a transcript from a webinar organized by LangChain [Link].
A detailed guide to Prompt Engineering byDAIR.AI[Link]
What Are Transformer Models and How Do They Work. A tutorial by Cohere AI [Link]
Learn Prompting: an open source course on prompt engineering[Link]
Generate SaaS Startup Ideas with ChatGPT
Today, we’ll tap into the potential of ChatGPT to brainstorm innovative SaaS startup ideas in the B2B sector. We’ll explore how AI can be incorporated to enhance their value propositions, and what makes these ideas compelling for investors. Each idea will come with a unique and intriguing name.
Here’s the prompt:
Generate three innovative startup ideas operating within the enterprise B2B SaaS industry, incorporating Artificial Intelligence to enhance their value proposition. The ideas should have compelling mission statements, clear descriptions of the AI application, and reasons why they are attractive to investors. Each idea should be accompanied by a unique and intriguing name.
Navigating the Revolutionary Trends of July 2023: July 26th, 2023
LLaMa, ChatGPT, Bard, Co-Pilot & all the rest. Large language models will become huge cloud services with massive ecosystems.
Large language models (LLMs) are everywhere. They do everything. They scare everyone – or at least some of us. Now what? They will become Generative-as-a-Service (GaaS) cloud “products” in exactly the same way all “as-a-service” products and services are offered. The major cloud providers – “Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), Alibaba Cloud, Oracle Cloud, IBM Cloud (Kyndryl), Tencent Cloud, OVHcloud, DigitalOcean, and Linode (owned by Akamai)” – will all develop, partner or acquire their generative AI capabilities and offer them as services. There will also be ecosystems around all of these tools exactly the same way ecosystems exist around all of the major enterprise infrastructure and applications that power every company on the planet. Google is in the generative AI (GAI) arms race. AWS is too. IBM is of course in the race. Microsoft has the lead.
So let’s look at LLMs like they were ERP, CRM or DBMS (does anyone actually still use that acronym?) tools, and how companies make decisions about what tool to use, how to use them and how to apply them to real problems.
Are We There Yet?
No, we’re not. Will we get there? Absolutely. Timeframe? 2-3 years. The productization of LLMs/generative AI (GAI) is well underway. Access to premium/business accounts is step one. Once the dust settles on this first wave of LLMs (2022-2023), we’ll see an arms race predicated on both capabilities and cost-effectiveness. ROI-, OKR-, KPI- and CMM-documented use cases will help companies decide what to do. The use cases will spread across key functions and vertical industries. Companies anxious to understand how they can exploit GAI will turn to these metrics and the use cases to conduct internal due diligence around adoption. Once that step is completed, and there appears to be promise, next steps will be taken.
Stuart Russell is a professor of computer science at the University of California, Berkeley. He also co-authored the authoritative AI textbook: Artificial Intelligence: A Modern Approach that is used by over 1,500 universities.
He calculates that in 20 years AI will generate about $14 quadrillion in wealth. Much of that will of course be made long before the 20-year mark.
Of this $14 quadrillion, it is estimated that the top five AI companies will earn the following wealth:
Google: $1.5 quadrillion
Amazon: $1.1 quadrillion
Apple: $2.5 quadrillion
Microsoft: $2.0 quadrillion
Meta: $0.7 quadrillion
That totals almost $8 quadrillion for the five.
These five companies are estimated to pay the following percentages of their annual revenue in taxes:
Google: 17-20%
Amazon: 13-15%
Microsoft: 18-22%
Apple: 20-25%
Meta: 15-18%
The 35% 2016 corporate tax rate was lowered to 21%. The AI top five are indeed doing well on taxes.
Let’s consider the above relative to the predicted loss of 3 million to 5 million jobs in the United States during the next 20 years. Re-employing those Americans has been estimated to cost from $60 billion (3 million people) to $100 billion (5 million people).
The question before us does not concern AI alignment. It is more about how well we Americans align with our values. Do our values align more with those three to five million people who will lose their jobs to AI, do they align more with the five top AI companies continuing to pay about 21% in taxes rather than the 35% they paid in 2016, or is there some fair and caring middle ground?
We may want to have those top five AI companies pay the full cost re-employing those three to five million Americans. To them it would hardly be a burdensome expense. Does that sound fair?
Edit 2am ET, 7/26/23:
Seems that 3 to 5 million figure is probably wildly incorrect. Sorry about that. This following estimate of 300 million worldwide over 20 years seem much more reasonable:
Microsoft reports $20.1B quarterly profit as it promises to lead “the new AI platform shift”
Microsoft on Tuesday reported fiscal fourth-quarter profit of $20.1 billion, or $2.69 per share, beating analyst expectations for $2.55 per share.
It posted revenue of $56.2 billion in the April-June period, up 8% from last year. Analysts had been looking for revenue of $55.49 billion, according to FactSet Research.
CEO Satya Nadella said the company remains focused on “leading the new AI platform shift.”
Where do ChatGPT and other LLMs get the linguistic capacity to identify as an AI and distinguish themselves from others?
ChatGPT and other large language models (LLMs) like it are not conscious entities, and they don’t have personal identities or self-awareness. When ChatGPT “identifies” itself as an AI, it’s based on the patterns and rules it learned during its training.
These models are trained on vast amounts of text data, which includes a lot of language about AI. Thus, when given prompts that suggest it is an AI or that ask it about its nature, it produces responses that are based on the patterns it learned, which include acknowledging it is an AI.
Furthermore, when these AI models distinguish themselves from others, they are not exhibiting consciousness or self-identity. Rather, they generate these distinctions based on the context of the prompt or conversation, again relying on learned patterns.
It’s also worth noting that while GPT models can generate coherent and often insightful responses, they don’t have understanding or beliefs. The models generate responses by predicting what comes next in a piece of text, given the input it’s received. Their “knowledge” is really just patterns in data they’ve learned to predict.
Daily AI News 7/26/2023
Ridgelinez (Tokyo) is a subsidiary of Fujitsu in Japan that announced the development of a generative artificial intelligence (AI) system capable of engaging in voice communication with humans. The applications of this system include assisting companies in conducting meetings or providing career planning advice to employees.
BMW has revealed that artificial intelligence is already allowing it to cut costs at its sprawling factory in Spartanburg, South Carolina. The AI system has allowed BMW to remove six workers from the line and deploy them to other jobs. The tool is already saving the company over $1 million a year.
MIT’s ‘PhotoGuard‘ protects your images from malicious AI edits. The technique introduces nearly invisible “perturbations” to throw off algorithmic models.
Microsoft with its TypeChat library seeks to enable easy development of natural language interfaces for large language models (LLMs) using types. Introduced July 20 of a team with c# and TypeScript lead developer Anders Hejlsberg, a Microsoft Technical Fellow, TypeChat addresses the difficulty of developing natural language interfaces where apps rely on complex decision trees to determine intent and gather necessary input to act.
AI predicts code coverage faster and cheaper
– Microsoft Research has proposed a novel benchmark task called Code Coverage Prediction. It accurately predicts code coverage, i.e., the lines of code or a percentage of code lines that are executed based on given test cases and inputs. Thus, it helps assess the capability of LLMs in understanding code execution.
– Several use case scenarios where this approach can be valuable and beneficial are:
Expensive build and execution in large software projects
Limited code availability
Live coverage or live unit testing
Introducing 3D-LLMs: Infusing 3D Worlds into LLMs
– New research has proposed injecting the 3D world into large language models, introducing a whole new family of 3D-based LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and generate responses.
– They can perform a diverse set of 3D-related tasks, including captioning, dense captioning, 3D question answering, task decomposition, 3D grounding, 3D-assisted dialog, navigation, and so on.
Alibaba Cloud brings Meta’s Llama to its clients
– Alibaba’s cloud computing division said it has become the first Chinese enterprise to support Meta’s open-source AI model Llama, allowing Chinese business users to develop programs off the model.
ChatGPT for Android is available in US, India, Bangladesh, Brazil – OpenAI will roll it out in more countries over the next week.
Netflix is offering up to $900K for one A.I. product manager role – The role will focus on increasing the leverage of its Machine Learning Platform.
Nvidia’s DGX Cloud on Oracle now widely available for generative AI training
– Nvidia announced wide accessibility of its cloud-based AI supercomputing service, DGX Cloud. The service will grant users access to thousands of virtual Nvidia GPUs on Oracle Cloud Infrastructure (OCI), along with infrastructure in the U.S. and U.K.
Spotify CEO teases AI-powered capabilities for personalization, ads
– During Spotify’s second-quarter earnings, CEO Daniel Ek on ways AI could be used to create more personalized experiences, summarize podcasts, and generate ads.
Cohere releases Coral, an AI assistant designed for enterprise business use
– Coral was specifically developed to help knowledge workers across industries receive responses to requests specific to their sectors based on their proprietary company data.
The proliferation of AI-generated girlfriends, such as those produced by Replika, might exacerbate loneliness and social isolation among men. They may also breed difficulties in maintaining real-life relationships and potentially reinforce harmful gender dynamics.
Chatbot technology is creating AI companions which could lead to social implications.
Concerns arise about the potential for these AI relationships to encourage gender-based violence.
Tara Hunter, CEO of Full Stop Australia, warns that the idea of a controllable “perfect partner” is worrisome.
Despite concerns, AI companions appear to be gaining in popularity, offering users a seemingly judgment-free friend.
Replika’s Reddit forum has over 70,000 members, sharing their interactions with AI companions.
The AI companions are customizable, allowing for text and video chat. As the user interacts more, the AI supposedly becomes smarter.
Uncertainty about the long-term impacts of these technologies is leading to calls for increased regulation.
Belinda Barnet, senior lecturer at Swinburne University of Technology, highlights the need for regulation on how these systems are trained.
Japan’s preference for digital over physical relationships and decreasing birth rates might be indicative of the future trend worldwide.
Job listings that include AI-based skills are growing rapidly as organizations look to create new efficiencies internally and for clients. But there’s a dearth of AI-skilled talent, so many companies are training… https://www.computerworld.com/article/3702711/ai-skills-job-postings-jump-450-heres-what-companies-want.html
Language models are tuned on input-label pairs presented in a context in which natural language labels are remapped to arbitrary symbols. For a given task, the model must depend on input-label …https://www.marktechpost.com/2023/07/19/google-ai-introduces-symbol-tuning-a-simple-fine-tuning-method-that-can-improve-in-context-learning-by-emphasizing-input-label-mappings/
Fable, a San Francisco startup, just released its SHOW-1 AI tech that is able to write, produce, direct animate, and even voice entirely new episodes of TV shows.
Their tech critically combines several AI models: including LLMs for writing, custom diffusion models for image creation, and multi-agent simulation for story progression and characterization.
Current generative AI systems like Stable Diffusion and ChatGPT can do short-term tasks, but they fall short of long-form creation and producing high-quality content, especially within an existing IP.
Hollywood is currently undergoing a writers and actors strike at the same time; part of the fear is that AI will rapidly replace jobs across the TV and movie spectrum.
The holy grail for studios is to produce AI works that rise up the quality level of existing IP; SHOW-1’s tech is a proof of concept that represents an important milestone in getting there.
Custom content where the viewer gets to determine the parameters represents a potential next-level evolution in entertainment.
How does SHOW-1’s magic work?
A multi-agent simulation enables rich character history, creation of goals and emotions, and coherent story generation.
Large Language Models (they use GPT-4) enable natural language processing and generation. The authors mentioned that no fine-tuning was needed as GPT-4 has digested so many South Park episodes already. However: prompt-chaining techniques were used in order to maintain coherency of story.
Diffusion models trained on 1200 characters and 600 background images from South Park’s IP were used. Specifically, Dream Booth was used to train the models and Stable Diffusion rendered the outputs.
Voice-cloning tech provided characters voices.
In a nutshell: SHOW-1’s tech is actually an achievement of combining multiple off-the-shelf frameworks into a single, unified system.
This is what’s exciting and dangerous about AI right now — how the right tools are combined, with just enough tweaking and tuning, and start to produce some very fascinating results.
The main takeaway:
Actors and writers are right to be worried that AI will be a massively disruptive force in the entertainment industry. We’re still in the “science projects” phase of AI in entertainment — but also remember we’re less than one year into the release of ChatGPT and Stable Diffusion.
A future where entertainment is customized, personalized, and near limitless thanks to generative AI could arrive in the next decade. Bu as exciting as that sounds, ask yourself: is that a good thing?
Unless it can replicate the natural processes of evolution, AI will never be truly self-aware, says academic and computer expert. https://cybernews.com/editorial/machine-learning-cannot-create-sentient-computers/
Google Red Team consists of a team of hackers that simulate a variety of adversaries, ranging from nation states and well-known Advanced Persistent Threat (APT) groups to hacktivists, individual criminals or even malicious insiders. The term came from the military, and described activities where a designated team would play an adversarial role (the “Red Team”) against the “home” team. https://blog.google/technology/safety-security/googles-ai-red-team-the-ethical-hackers-making-ai-safer/
Generative AI is impressive, but the hidden environmental costs and impact of these models are often overlooked. Companies can take eight steps to make these systems greener: Use existing large generative models, don’t generate your own; fine-tune train existing models; use energy-conserving computational methods; use a large model only when it offers significant value; be discerning about when you use generative AI; evaluate the energy sources of your cloud provider or data center; re-use models and resources; include AI activity in your carbon monitoring. https://hbr.org/2023/07/how-to-make-generative-ai-greener
Apple has been relatively quiet on the generative AI front in recent months, which makes them a relative anomaly as Meta, Microsoft, and more all duke it out for the future of AI.
The relative silence doesn’t mean Apple hasn’t been doing anything, and today’s Bloomberg report (note: paywalled) sheds light on their master plan: they’re quietly but ambitiously laying the groundwork for some major moves in AI in 2024. https://www.bloomberg.com/news/articles/2023-07-19/apple-preps-ajax-generative-ai-apple-gpt-to-rival-openai-and-google
Summary: According to an Bloomberg, Apple is quietly building its own AI chatbot, also known as “Apple GPT”, that could be integrated into Siri & Apple devices.
Key Points:
Apple is using its own system, “Ajax”, to make the new tool.
The chatbot was stopped for a bit because of safety worries, but more Apple employees are getting to use it.
They don’t seem to be interested in competing with ChatGPT. Instead, Apple wants to find a consumer angle for their AI.
Why it matters? With 1.5 billion active iPhones out there, Apple can change the LLM landscape overnight.
ChatGPT Plus subscribers now have an increased messaging limit of 50 messages in three hours with the introduction of GPT-4. Previously, the limit was set at 25 messages in two hours due to computational and cost considerations.
Why does this matter?
Increasing the message limit with GPT-4 provides more room for exploration and experimentation with ChatGPT plugins. For businesses looking to enhance customer interactions, a developer building innovative applications, or an AI enthusiast, the raised cap of 50 messages per 3 hours opens up more extensive and dynamic interactions with the model.
Convert YouTube Videos to Blogs & Audios with ChatGPT
Ever wished you could repurpose your YouTube content into blog posts and audios? In this tutorial, we’ll show you how to convert YouTube videos into written and audio content using ChatGPT and a few helpful plugins.
Step 1: Install Necessary Plugins
You’ll need three plugins for this task:
Video Insights: Extracts key information from videos.
ImageSearch: Finds relevant images to enrich your blog post.
Speechki: Converts your blog text into voiceover audio.
You can install these plugins from the plugin store.
Step 2: Enter the Prompt
Once you have the plugins installed, paste the following prompt into ChatGPT:
Perform the following tasks based on YouTube video below:
[URL]
1. Take the captions of the video and convert it into a blog
2. Add required images for the blog
3. Create a voiceover for the blog
Replace “[URL]” with the URL of your YouTube video.
Step 3: Get the blog and the voiceover
After entering the prompt, ChatGPT will create a blog post based on the video’s content. It will also suggest suitable images from Unsplash and generate a voiceover for the entire blog.
Expected Outcome
The output should be a well-structured blog post, complete with images and a voiceover. This way, you can extend your reach beyond YouTube and cater to audiences who prefer reading or listening to content.
This interesting read by Cameron R. Wolfe, Ph.D. discusses the emergence of proprietary Language Model-based APIs and the potential challenges they pose to the traditional open-source and transparent approach in the deep learning community. It highlights the development of open-source LLM alternatives as a response to the shift towards proprietary APIs. https://cameronrwolfe.substack.com/p/imitation-models-and-the-open-source
The article emphasizes the importance of rigorous evaluation in research to ensure that new techniques and models truly offer improvements. It also explores the limitations of imitation LLMs, which can perform well for specific tasks but tend to underperform when broadly evaluated.
Why does this matter?
While local imitation is still valuable for specific domains, it is not a comprehensive solution for producing high-quality, open-source foundation models. Instead, it advocates for the continued advancement of open-source LLMs by focusing on creating larger and more powerful base models to drive further progress in the field.
Google research team’s this paper introduces SimPer, a self-supervised learning method that focuses on capturing periodic or quasi-periodic changes in data. SimPer leverages the inherent periodicity in data by incorporating customized augmentations, feature similarity measures, and a generalized contrastive loss. https://ai.googleblog.com/2023/07/simper-simple-self-supervised-learning.html
SimPer exhibits superior data efficiency, robustness against spurious correlations, and generalization to distribution shifts, making it a promising approach for capturing and utilizing periodic information in diverse applications.
Why does this matter?
SimPer’s significance lies in its ability to address the challenge of learning meaningful representations for periodic tasks with limited or no supervision. This advancement proves crucial in various domains, such as human behavior analysis, environmental sensing, and healthcare, where critical processes often exhibit periodic or quasi-periodic changes. It demonstrates that SimPer outperforms state-of-the-art SSL methods.
Nvidia’s (NASDAQ:NVDA) stock has risen dramatically in 2023, primarily due to its AI chips. Its GPU chipsets are the most powerful available, and as AI has taken off, the competition to secure those chips has made Nvidia the hottest firm there is. https://www.nasdaq.com/articles/3-machine-learning-stocks-for-getting-rich-in-2023
Nvidia chips also power complex large language models used to train machine learning models based on technical subfields, including neural networks. Those chips are in high demand in data centers and automotive sectors, where machine learning is utilized at higher rates.
Advanced Micro Devices (NASDAQ:AMD) is the primary challenger to Nvidia’s dominance in AI and machine learning.
It’s entirely reasonable to believe that AMD could attract Nvidia investor capital on overvaluation fears. That’s one reason investors should consider AMD.
However, the more salient reason is simply that AMD is not that far behind Nvidia. MosiacML recently pegged AMD’s high-end chip speed as about 80% as fast as those from Nvidia. Here’s the good news regarding machine learning: AMD has done very well on the software side, according to MosaicML, which notes that software has been the “Achilles heel” for most machine learning firms.
Palantir Technologies (NYSE:PLTR) stock has boomed in 2023 due to AI and machine learning. It didn’t catch the early wave of AI adoption that benefited Microsoft (NASDAQ:MSFT), AMD, Nvidia, and others — instead getting hot in recent months.
Its Gotham and Foundry platforms have found a following in private firms and, more prominently, with public firms and government organizations. Adoption across the defense sector has been particularly important in helping Palantir take advantage of AI stock growth. The company has long been associated with the defense industry and has developed a deep connection by applying silicon-valley-style tech to government entities.
You know how hard it is to get customer service on the phone? That’s because companies really, really, really don’t like paying for call center workers. That’s why, as a class, customer service will be the first group of workers whose jobs will be decimated by A.I.
A new study by researchers Chen, Zaharia, and Zou at Stanford and UC Berkley now confirms that these perceived degradations are quantifiable and significant between the different versions of the LLMs (March and June 2023). They find:
“For GPT-4, the percentage of [code] generations that are directly executable dropped from 52.0% in March to 10.0% in June. The drop was also large for GPT-3.5 (from 22.0% to 2.0%).” (!!!)
For sensitive questions: “An example query and responses of GPT-4 and GPT-3.5 at different dates. In March, GPT-4 and GPT-3.5 were verbose and gave detailed explanation for why it did not answer the query. In June, they simply said sorry.”
“GPT-4 (March 2023) was very good at identifying prime numbers (accuracy 97.6%) but GPT-4 (June 2023) was very poor on these same questions (accuracy 2.4%). Interestingly GPT-3.5 (June 2023) was much better than GPT-3.5 (March 2023) in this task.”
A group of more than 8,500 authors is challenging tech companies for using their works without permission or compensation to train AI language models like ChatGPT, Bard, LLaMa, and others.
Concerns about Copyright Infringement: The authors have pointed out that these AI technologies are replicating their language, stories, style, and ideas, without any recognition or reimbursement. Their writings serve as “endless meals” for AI systems. The companies behind these models have not significantly addressed the sourcing of these works. https://www.theregister.com/2023/07/18/ai_in_brief/
The authors question whether the AI models used content scraped from bookstores and reviews, borrowed from libraries, or downloaded from illegal archives.
It’s evident that the companies didn’t obtain licenses from publishers — a method seen by the authors as both legal and ethical.
Legal and Ethical Arguments: The authors highlight the Supreme Court decision in Warhol v. Goldsmith, suggesting that the high commerciality of these AI models’ use may not constitute fair use.
They claim that no court would approve of using illegally sourced works.
They express concern that generative AI may flood the market with low-quality, machine-written content, undermining their profession.
They cite examples of AI-generated books already making their way onto best-seller lists and being used for SEO purposes.
Impact on Authors and Requested Actions: The group of authors warns that these practices can deter authors, especially emerging ones or those from under-represented communities, from making a living due to large scale publishing’s narrow margins and complexities.
They request tech companies to obtain permission for using their copyrighted materials.
They demand fair compensation for past and ongoing use of their works in AI systems.
They also ask for remuneration for the use of their works in AI output, whether it’s deemed infringing under current law or not.
In a recent study it was reported that 76% of “Gen-Zers”are concerned about losing their jobs to AI-powered tools. I am Gen-Z and I think a lot of future jobs will be replaced with AI.
Emerging Trend: A director says Gen Z workers at his medical device company are increasing efficiency by using AI tools to automate tasks and optimize workflows.
Gen Z is adept at deploying new AI-powered systems on the job.
They are automating tedious processes and turbocharging productivity.
This offsets concerns about AI displacing entry-level roles often filled by Gen Z.
Generational Divide: Gen Z may be better positioned than older workers to capitalize on AI’s rise.
They have the tech skills to implement AI and make it work for them.
But surveys show most still fear losing jobs to AI automation overall.
Companies are rapidly adopting AI, with some CEOs openly planning workforce cuts.
TL;DR: While AI automation threatens some roles, a medical company director says Gen Z employees are productively applying AI to boring work, benefiting from their digital savvy. But surveys indicate young workers still predominantly worry about job loss risks from AI.
The role of “Head of AI” is rapidly gaining popularity in American businesses, despite the uncertainty surrounding the specific duties and qualifications associated with the position.
Rise of the “Head of AI” Role: The “Head of AI” position, largely nonexistent a few years ago, has seen significant growth in the U.S., tripling in the last five years.
The role has emerged across a range of businesses, from tech giants to companies outside of the tech sector.
The increased adoption of this role is in response to the increasing disruption caused by AI in various industries.
Uncertainties Surrounding the Role: Despite the role’s popularity, there’s a lack of clarity about what a “Head of AI” specifically does and what qualifications are necessary.
The role’s responsibilities vary widely between companies, ranging from incorporating AI into products to training employees in AI use.
There’s also debate about who should take on this role, with contenders ranging from seasoned AI experts to those familiar with consumer-facing AI applications.
Current Landscape of AI Leadership: Despite the uncertainties, the trend of appointing AI leaders in companies is growing, with an expected increase from 25% to 80% of Fortune 2000 companies having a dedicated AI leader within a year.
The role is becoming more common in larger companies, particularly in banking, tech, and manufacturing sectors.
Individuals from various backgrounds, including technology leadership, business, and marketing, are stepping into the role.
Cerebras and G42, the Abu Dhabi-based AI pioneer, announced their strategic partnership, which has resulted in the construction of Condor Galaxy 1 (CG-1), a 4 exaFLOPS AI Supercomputer. https://www.cerebras.net/press-release/cerebras-and-g42-unveil-worlds-largest-supercomputer-for-ai-training-with-4-exaflops-to-fuel-a-new-era-of-innovation
Located in Santa Clara, CA, CG-1 is the first of nine interconnected 4 exaFLOPS AI supercomputers to be built through this strategic partnership between Cerebras and G42. Together these will deliver an unprecedented 36 exaFLOPS of AI compute and are expected to be the largest constellation of interconnected AI supercomputers in the world.
CG-1 is now up and running with 2 exaFLOPS and 27 million cores, built from 32 Cerebras CS-2 systems linked together into a single, easy-to-use AI supercomputer. While this is currently one of the largest AI supercomputers in production, in the coming weeks, CG-1 will double in performance with its full deployment of 64 Cerebras CS-2 systems, delivering 4 exaFLOPS of AI compute and 54 million AI optimized compute cores.
Upon completion of CG-1, Cerebras and G42 will build two more US-based 4 exaFLOPS AI supercomputers and link them together, creating a 12 exaFLOPS constellation. Cerebras and G42 then intend to build six more 4 exaFLOPS AI supercomputers for a total of 36 exaFLOPS of AI compute by the end of 2024.
Offered by G42 and Cerebras through the Cerebras Cloud, CG-1 delivers AI supercomputer performance without having to manage or distribute models over GPUs. With CG-1, users can quickly and easily train a model on their data and own the results.
AI models need increasingly unique and sophisticated data sets to improve their performance, but the developers behind major LLMs are finding that web data is “no longer good enough” and getting “extremely expensive,” a report from the Financial Times (note: paywalled) reveals.
So OpenAI, Microsoft, and Cohere are all actively exploring the use of synthetic data to save on costs and generate clean, high-quality data. https://www.ft.com/content/053ee253-820e-453a-a1d5-0f24985258de
Why this matters:
Major LLM creators believe they have reached the limits of human-made data improving performance. The next dramatic leap in performance may not come from just feeding models more web-scraped data.
Custom human-created data is extremely expensive and not a scalable solution. Getting experts in various fields to create additional finely detailed content is unviable at the quantity of data needed to train AI.
Web data is increasingly under lock and key, as sites like Reddit, Twitter, more are charging hefty fees in order to use their data.
The approach is to have AI generate its own training data go-forward:
Cohere is having two AI models act as tutor and student to generate synthetic data. All of it is reviewed by a human at this point.
Microsoft’s research team has shown that certain synthetic data can be used to train smaller models effectively — but increasing GPT-4 performance’s is still not viable with synthetic data.
Startups like Scale.ai and Gretel.ai are already offering synthetic data-as-a-service, showing there’s market appetite for this.
What are AI leaders saying? They’re determined to explore this future.
Sam Altman explained in May that he was “pretty confident that soon all data will be synthetic data,” which could help OpenAI sidestep privacy concerns in the EU. The pathway to superintelligence, he posited, is through models teaching themselves.
Aidan Gomez, CEO of LLM startup Cohere, believes web data is not great: “the web is so noisy and messy that it’s not really representative of the data that you want. The web just doesn’t do everything we need.”
Some AI researches are urging caution, however: researchers from Oxford and Cambridge recently found that training AI models on their own raw outputs risked creating “irreversible defects” in these models that could corrupt and degrade their performance over time.
The main takeaway: Human-made content was used to develop the first generations of LLMs. But we’re now entering a fascinating world where the over the next decade, human-created content could become truly rare, with the bulk of the world’s data and content all created by AI.
I Spent 9 Days and Tried 324 AI Tools for my Youtube video and these 9 AI tools are best I use personally.
I Spent 9 Days and Tried 324 AI Tools for my youtube video and these 9 AI tools are best I use personally.
In this AI Hype, Everyone is Building Extraordinary AI products that will blow your mind but Sometime too many options is stuck our action and we are not able to decide what we do and what we try But as content creator i reviewed too many AI Tool for my videos, and i personally say, these are the most productive and helpful AI tool for your business, writing, research etc.
My AskAI: A great tool for using ChatGPT on your own files and website. It’s useful for research and tasks requiring accuracy, with options for concise or detailed answers. The basic plan is free, and there’s a $20/month option for over 100 pieces of content.
Helper-AI – The Fastest way to access GPT-4 on any site, Just type “help” and instant access GPT-4 on any site without changing tabs again and again. In Just One Month Helper-AI is making $2000 by selling complete source code and ownership of AI. (It will help you to boost 3x Productivity, Generate high-quality content, Write code & Excel Formulas, Rewrite, Research, Summarise and more. )
Krater.ai: An all-in-one web app that combines text, audio, and image-based AI tools. It simplifies workflow by eliminating the need for multiple tabs and offers templates for copywriting. It’s preferred over other options and provides 10 free generations per month.
HARPA AI: A Chrome add-on with GPT answers alongside search results, web page chat, YouTube video summarization, and email/social media reply templates. It’s completely free and available on the Chrome Web Store.
Plus AI for Google Slides: A slide deck generator that helps co-write slides, provides suggestions, and allows integration of external data. It’s free and available as a Google Slides and Docs plugin.
Taskade: An all-in-one productivity tool that combines tasks, notes, mind maps, chat, and an AI chat assistant. It syncs across teams and offers various views. The free version has many features.
Zapier + OpenAI: A powerful combination of Zapier’s integrations with generative AI. It enables automations with GPT 3, DALLE-2, and Whisper AI. It’s free for core features and available as an app/add-on to Zapier.
SaneBox: AI-based email management that identifies important emails and allows customization of folders. It helps declutter inboxes and offers a “Deep Clean” feature. There’s a 2-week trial, and pricing is affordable.
Hexowatch AI: A website change detection tool that alerts you to changes on multiple websites. It saves time and offers alert notifications via email or other platforms. It’s a paid service with reliable performance.
I built the fastest way to access GPT-4 on any site because I was so frustrated because Every time I want to access ChatGPT, I need to login ChatGPT first, filling password, Captcha, and changing browser tab again and again for using Chatgpt that complete make me unproductive and overwhelming.
So, I built my own AI tool to access GPT-4 on any site without leaving the current site, you just type “help” and instant access GPT-4 on any site.
I think its make me 10 times more productive, and best part is, I was so insecure before launching my AI product because I was thinking no one will buy it.
but when I launch the product everyone love it.
After launching the product, in just 5 days I make around $300 by selling the complete source code and ownership of the product, so people can use it, resell it, modify it or anything they want to do.
In a recent development, tech giants like Google, NVIDIA and Microsoft are aggressively exploring the intersection of artificial intelligence (AI) and healthcare, hoping to revolutionize medicine as we know it. https://sites.research.google/med-palm/
Google’s AI chatbot, Med-PaLM 2, has demonstrated an impressive 92.6% accuracy rate in responding to medical queries, closely matching the 92.9% score by human healthcare professionals. However, it’s worth noting that these advancements don’t come without their quirks, as a Google research scientist previously discovered the system had the capacity to “hallucinate” and cite non-existent studies.
NVIDIA
AI’s potential in the pharmaceutical sector is also drawing significant attention, with the goal of using AI to discover new, potentially groundbreaking drugs. Nvidia is the latest entrant into this field, investing $50Min AI drug discovery company, Recursion Pharmaceuticals (NASDAQ:RXRX), causing a substantial 78% increase in their stock.
Microsoft
Microsoft acquired a speech recognition company, Nuance for $19.7 billion to expand their reach to healthcare. Just yesterday at their Inspire event, they revealed how they are partnering up with Epic Systems, US’s largest EHR to integrate Nuance’s AI solutions.
Meta, the parent company of Facebook, has recently launched LLaMA 2, an open-source large language model (LLM) that aims to challenge the restrictive practices by big tech competitors. Unlike AI systems launched by Google, OpenAI, and others that are closely guarded in proprietary models, Meta is freely releasing the code and data behind LLaMA 2 to enable researchers worldwide to build upon and improve the technology. https://venturebeat.com/ai/llama-2-how-to-access-and-use-metas-versatile-open-source-chatbot-right-now/
LLaMA 2 comes in three sizes: 7 billion, 13 billion, and 70 billion parameters depending on the model you choose. It’s trained using reinforcement learning from human feedback (RLHF), learning from the preferences and ratings of human AI trainers.
There are numerous ways to interact with LLaMA 2. You can interact with the chatbot demo at llama2.ai, download the LLaMA 2 code from Hugging Face, access it through Microsoft Azure, Amazon SageMaker JumpStart, or try a variant at llama.perplexity.ai.
By launching LLaMA 2, Meta has taken a significant step in opening AI up to developers worldwide. This could lead to a surge of innovative AI applications in the near future.
For more details, check out the full article here.
AI21 Labs debuts Contextual Answers, a plug-and-play AI engine for enterprise data
AI21 Labs, the Tel Aviv-based NLP major behind the Wordtune editor, has announced the launch of a plug-and-play generative AI engine to help enterprises drive value from their data assets. Named Contextual Answers, this API can be directly embedded into digital assets to implement large language model (LLM) technology on select organizational data. It enables business employees or customers to gain the required information through a conversational experience, without engaging with different teams or software systems. https://venturebeat.com/ai/ai21-labs-debuts-contextual-answers-a-plug-and-play-ai-engine-for-enterprise-data/
This technology is offered as a solution that works out of the box and doesn’t require significant effort and resources. It’s built as a plug-and-play capability and optimized each component, allowing clients to get the best results in the industry without investing the time of AI, NLP, or data science practitioners.
The AI engine supports unlimited upload of internal corporate data, taking into account access and security of the information. For access control and role-based content separation, the model can be limited to using a specific file, a number of files, a specific folder, or tags or metadata. For security and data confidentiality, the company’s AI21 Studio ensures a secured and soc-2 certified environment.
For more details, check out the full article here.
Google is actively meeting with news organizations and demo’ing a tool, code-named “Genesis”, that can write news articles using AI, the New York Times revealed.
Utilizing Google’s latest LLM technologies, Genesis is able to use details of current events to generate news content from scratch. But the overall reaction to the tool has been highly mixed, ranging from deep concern to muted enthusiasm. https://www.nytimes.com/2023/07/19/business/google-artificial-intelligence-news-articles.html
Why this matters:
Media organizations are under financial pressure as they enter the age of generative AI: while some are refusing to embrace it, other media orgs like G/O Media (AV Club, Jezebel, etc.) are openly using AI to generate articles.
Early tests of generative AI have already led to concerns: the tendency of large language models to hallucinate is producing inaccuracies even in articles published by well-known media organizations.
The job of journalism is in question itself: if AI can write news articles, what role do journalists play beyond editing AI-written content? Orgs like Insider, The Times, NPR and more have already notified employees they intend to explore generative AI.
What do news organizations actually think of Google’s Genesis?
It’s “unsettling,” some execs have said. News orgs worry that Google “it seemed to take for granted the effort that went into producing accurate and artful news stories.”
They’re not happy that Google’s LLM digested their news content (often w/o compensation): it’s the efforts of decades of journalism powering Google’s new Genesis tool, which now threatens to upend journalism
Most news orgs are saying “no comment”: treat that as a signal for how they’re deeply grappling with this existential challenge.
What does Google think?
They think this could be more of a copilot (right now) than an outright replacement for journalists: “Quite simply, these tools are not intended to, and cannot, replace the essential role journalists have in reporting, creating and fact-checking their articles,” an Google spokesperson clarified.
The main takeaway:
The next decade isn’t going to be great for news organizations. Many were already struggling with the transition to online news, and many media organizations have shown that buzzy logos and fancy brand can’t make viable businesses (VICE, Buzzfeed, and more).
How journalists navigate the shift in their role will be very interesting, and I’ll be curious to see if they end up adopting copilots to the same degree we’re seeing in the engineering world.
Today, OpenAI introduced a custom instructions feature in beta that allows users to set persistent preferences that ChatGPT will remember in all conversations.
Key points:
ChatGPT now allows custom instructions to tailor responses. This lets users set preferences instead of repeating them.
Instructions are remembered for all conversations going forward. Avoiding restarting each chat from scratch.
Why the $20 subscription is even more valuable: More personalized and customized conversations.
Instructions allow preferences for specific contexts. Like grade levels for teachers.
Developers can set preferred languages for code. Beyond defaults like Python.
Shopping lists can account for family size servings. With one time instructions.
The beta is live for Plus users now. Rolling out to all users in coming weeks.
This takes customization to the next level for ChatGPT allowing for persistent needs and preferences.
Open AI released six use cases they’ve found so far here they are in order.
“Expertise calibration: Sharing your level of expertise in a specific field to avoid unnecessary explanations.
Language learning: Seeking ongoing conversation practice with grammar correction.
Localization: Establishing an ongoing context as a lawyer governed by their specific country’s laws.
Novel writing: Using character sheets to help ChatGPT maintain a consistent understanding of story characters in ongoing interactions.
Response format: Instructing ChatGPT to consistently output code updates in a unified format.
Writing style personalization: Applying the same voice and style as provided emails to all future email writing requests.” (Use cases are in Open AI’s words.)
The article shows some examples of how businesses are already relying on AI-based applications for internal purposes, and how to do the same quickly and affordably with a no-code program builder – with healthcare, real estate, and professional services providers as examples: No-Code AI Applications for Healthcare and Other Traditional Industries – Blaze
Daily AI Update News from Apple, OpenAI, Google Research, MosaicML, Google and Nvidia
Apple Trials a ChatGPT-like AI Chatbot
– Apple is developing AI tools, including its own large language model called “Ajax” and an AI chatbot named “Apple GPT.” They are gearing up for a major AI announcement next year as it tries to catch up with competitors like OpenAI and Google. The company’s executives are considering integrating these AI tools into Siri to improve its functionality and performance, and overcome the stagnation the voice assistant has experienced in recent years.
OpenAI doubles GPT-4 message cap to 50
– OpenAI has doubled the number of messages ChatGPT Plus subscribers can send to GPT-4. Users can now send up to 50 messages in 3 hours, compared to the previous limit of 25 messages in 2 hours. And they are rolling out this update next week.
– Increasing the message limit with GPT-4 provides more room for exploration and experimentation with ChatGPT plugins. For businesses, developers, and AI enthusiasts, the raised cap on messages allows for more extensive interaction with the model.
Google AI’s SimPer unlocks the potential of periodic learning
– Google research team’s this paper introduces SimPer, a self-supervised learning method that focuses on capturing periodic or quasi-periodic changes in data. SimPer leverages the inherent periodicity in data by incorporating customized augmentations, feature similarity measures, and a generalized contrastive loss.
Google exploring AI tools for Journalists
– Google is exploring using AI tools to write news articles and is in talks with publishers to use the tools to assist journalists. The potential uses of these AI tools include assistance to journalists with options for headlines or different writing styles, and majorly the objective is to enhances their work and productivity.
MosaicML launches MPT-7B-8K with 8k context length
– MosaicML has released MPT-7B-8K, an open-source LLM with 7B parameters and an 8k context length. The model was trained on the MosaicML platform, starting from the MPT-7B checkpoint. The pretraining phase utilized Nvidia H100s and involved three days of training on 256 H100s, incorporating 500B tokens of data. This new LLM offers significant advancements in language processing capabilities and is available for developers to use and contribute.
AI has driven Nvidia to achieve a $1 trillion valuation!
– The company, which started as a video game hardware provider, has now become a full-stack hardware and software company powering the Gen AI revolution. Nvidia’s success in the AI industry has led to it becoming a nearly $1 trillion company.
Navigating the Revolutionary Trends of July 2023: July 19th, 2023
Type 2 diabetes is a chronic disease that affects millions of people around the world, leading to long-term health complications such as heart disease, nerve damage, and kidney failure. The early diagnosis of type 2 diabetes is critical in order to prevent these complications, and machine learning is helping to revolutionize the way this disease is diagnosed.
Machine learning algorithms use patterns in data to make predictions and decisions, and this same capability can be applied to the analysis of medical data in order to improve the diagnosis of type 2 diabetes. One of the key ways that machine learning is improving diabetes diagnosis is through the use of predictive algorithms. These algorithms can use data from patient histories, such as age, BMI, blood pressure, and blood glucose levels, to predict the likelihood of a patient developing type 2 diabetes. This can help healthcare providers to identify patients who are at high risk of developing the disease and take early action to prevent it.
Computer vision enables computers and systems to extract useful information from digital photos, videos, and other visual inputs and to conduct actions or offer recommendations in response to that information. Computer vision gives machines the ability to perceive, observe, and understand, much like artificial intelligence gives them the capacity to think.
Kili Technology’s video annotation tool is designed to simplify and accelerate the creation of high-quality datasets from video files. The tool supports a variety of labeling tools, including bounding boxes, polygons, and segmentation, allowing for precise annotation. With advanced tracking capabilities, you can easily navigate through frames and review all your labels in an intuitive Explore view.
The tool supports various video formats and integrates seamlessly with popular cloud storage providers, ensuring a smooth integration with your existing machine learning pipeline. Kili Technology’s video annotation tool is the ultimate toolkit for optimizing your labeling processes and constructing powerful datasets.
A software library for machine learning and computer vision is called OpenCV. OpenCV, developed to offer a standard infrastructure for computer vision applications, gives users access to more than 2,500 traditional and cutting-edge algorithms.
These algorithms may be used to identify faces, remove red eyes, identify objects, extract 3D models of objects, track moving objects, and stitch together numerous frames into a high-resolution image, among other things.
A complete platform for computer vision development, deployment, and monitoring, Viso Suite enables enterprises to create practical computer vision applications. The best-in-class software stack for computer vision, which is the foundation of the no-code platform, includes CVAT, OpenCV, OpenVINO, TensorFlow, or PyTorch.
Image annotation, model training, model management, no-code application development, device management, IoT communication, and bespoke dashboards are just a few of the 15 components that make up Viso Suite. Businesses and governmental bodies worldwide use Viso Suite to create and manage their portfolio of computer vision applications (for industrial automation, visual inspection, remote monitoring, and more).
TensorFlow is one of the most well-known end-to-end open-source machine learning platforms, which offers a vast array of tools, resources, and frameworks. TensorFlow is beneficial for developing and implementing machine learning-based computer vision applications.
One of the most straightforward computer vision tools, TensorFlow, enables users to create machine learning models for computer vision-related tasks like facial recognition, picture categorization, object identification, and more. Like OpenCV, Tensorflow supports several languages, including Python, C, C++, Java, and JavaScript.
NVIDIA created the parallel computing platform and application programming interface (API) model called CUDA (short for Compute Unified Device Architecture). It enables programmers to speed up processing-intensive programs by utilizing the capabilities of GPUs (Graphics Processing Units).
The NVIDIA Performance Primitives (NPP) library, which offers GPU-accelerated image, video, and signal processing operations for various domains, including computer vision, is part of the toolkit. In addition, multiple applications like face recognition, image editing, rendering 3D graphics, and others benefit from the CUDA architecture. For Edge AI implementations, real-time image processing with Nvidia CUDA is available, enabling on-device AI inference on edge devices like the Jetson TX2.
Image, video, and signal processing, deep learning, machine learning, and other applications can all benefit from the programming environment MATLAB. It includes a computer vision toolbox with numerous features, applications, and algorithms to assist you in creating remedies for computer vision-related problems.
A Python-based open-source software package called Keras serves as an interface for the TensorFlow framework for machine learning. It is especially appropriate for novices because it enables speedy neural network model construction while offering backend help.
SimpleCV is a set of open-source libraries and software that makes it simple to create machine vision applications. Its framework gives you access to several powerful computer vision libraries, like OpenCV, without requiring a thorough understanding of complex ideas like bit depths, color schemes, buffer management, or file formats. Python-based SimpleCV can run on various platforms, including Mac, Windows, and Linux.
The Java-based computer vision program BoofCV was explicitly created for real-time computer vision applications. It is a comprehensive library with all the fundamental and sophisticated capabilities needed to develop a computer vision application. It is open-source and distributed under the Apache 2.0 license, making it available for both commercial and academic use without charge.
Convolutional Architecture for Fast Feature, or CAFFE A computer vision and deep learning framework called embedding was created at the University of California, Berkeley. This framework supported a variety of deep learning architectures for picture segmentation and classification and was made in the C++ programming language. Due to its incredible speed and image processing capabilities, it is beneficial for research and industry implementation.
A comprehensive computer vision tool, OpenVINO (Open Visual Inference and Neural Network Optimization), helps create software that simulates human vision. It is a free cross-platform toolkit designed by Intel. Models for numerous tasks, including object identification, face recognition, colorization, movement recognition, and others, are included in the OpenVINO toolbox.
The most well-liked open-source computer vision library for deep learning facial recognition at the moment is DeepFace. The library provides a simple method for using Python to carry out face recognition-based computer vision.
One of the fastest computer vision tools in 2022 is You Only Look Once (YOLO). It was created in 2016 by Joseph Redmon and Ali Farhadi to be used for real-time object detection. YOLO, the fastest object detection tool available, applies a neural network to the entire image and then divides it into grids. The odds of each grid are then predicted by the software concurrently. After the hugely successful YOLOv3 and YOLOv4, YOLOR had the best performance up until YOLOv7, published in 2022, overtook it.
FastCV is an open-source image processing, machine learning, and computer vision library. It includes numerous cutting-edge computer vision algorithms along with examples and demos. As a pure Java library with no external dependencies, FastCV’s API ought to be very easy to understand. It is, therefore, perfect for novices or students who want to swiftly include computer vision into their ideas and prototypes.
To easily integrate computer vision functionality into our mobile apps and games, the company also integrated FastCV on Android.
One of the best open-source computer vision tools for processing images in Python is the Scikit-image module. Scikit-image allows you to conduct simple operations like thresholding, edge detection, and color space conversions.
Here are the 5 different types of Artificial intelligence that have changed the way businesses think about extracting insights from data. https://www.analyticsinsight.net/5-different-types-of-artificial-intelligence/
1. Machine Learning: Artificial intelligence includes machine learning as a component. It is described as the algorithms that scan data sets and then learn from them to make educated judgments. In the case of machine learning, the computer software learns from experience by executing various tasks and seeing how the performance of those tasks improves over time.
2. Deep Learning: Deep learning may also be considered a subset of machine learning. Deep learning aims to increase power by teaching students how to represent the world in a hierarchy of concepts. It demonstrates how the notion is connected to more easy concepts and how fewer abstract representations can exist for more complex ones.
3. Natural language Processing (NLP): Natural Language Processing (NLP) is an artificial intelligence that combines AI and linguistics to allow humans to communicate with robots using natural language. Google natural language processing utilizing Google Voice search is a simple example of NLP.
4. Computer Vision: Computer vision is used in organizations to improve the user experience while cutting costs and enhancing security. The market for computer vision is growing at the same rate as its capabilities and is expected to reach $26.2 billion by 2025. This is an almost 30% annual growth.
5. Explainable AI(XAI): Explainable artificial intelligence is a collection of strategies and approaches that enable human users to comprehend and trust machine learning algorithms’ discoveries and output. Explainable AI refers to the ability to explain an AI model, its projected impact, and any biases. It contributes to the definition of model correctness, fairness, and transparency and results in AI-powered decision-making.
Boom — here it is! We previously heard that Meta’s release of an LLM free for commercial use was imminent and now we finally have more details. https://ai.meta.com/llama/
The model was trained on 40% more data than LLaMA 1, with double the context length: this should offer a much stronger starting foundation for people looking to fine-tune it.
It’s available in 3 model sizes: 7B, 13B, and 70B parameters.
LLaMA 2 outperforms other open-source models across a variety of benchmarks: MMLU, TriviaQA, HumanEval and more were some of the popular benchmarks used. Competitive models include LLaMA 1, Falcon and MosaicML’s MPT model.
A 76-page technical specifications doc is included as well: giving this a quick read through, it’s in Meta’s style of being very open about how the model was trained and fine-tuned, vs. OpenAI’s relatively sparse details on GPT-4.
What else is interesting: they’re cozy with Microsoft:
Microsoft is our preferred partner for Llama 2, Meta announces in their press release, and “starting today, Llama 2 will be available in the Azure AI model catalog, enabling developers using Microsoft Azure.”
My takeaway: MSFT knows open-source is going to be big. They’re not willing to put all their eggs in one basket despite a massive $10B investment in OpenAI.
Meta’s Microsoft partnership is a shot across the bow for OpenAI. Note the language in the press release:
“Now, with this expanded partnership, Microsoft and Meta are supporting an open approach to provide increased access to foundational AI technologies to the benefits of businesses globally. It’s not just Meta and Microsoft that believe in democratizing access to today’s AI models. We have a broad range of diverse supporters around the world who believe in this approach too “
All of this leans into the advantages of open source: “increased access”, “democratizing access”, “supporters across the world”
The takeaway: the open-source vs. closed-source wars just got really interesting. Meta didn’t just make LLaMA 1 available for commercial use, they released a better model and announced a robust collaboration with Microsoft at the same time. Rumors persist that OpenAI is releasing an open-source model in the future — the ball is now in their court.
Stability AI’s CEO, Emad Mostaque, anticipates a significant decline in the number of outsourced coders in India within the next two years due to the rise of artificial intelligence. https://www.cnbc.com/2023/07/18/stability-ai-ceo-most-outsourced-coders-in-india-will-go-in-2-years.html
The Threat to Outsourced Coders in India: Emad Mostaque predicts a significant job loss among outsourced coders in India as a result of advancing AI technologies. He believes that software can now be developed with fewer individuals, posing a significant threat to these jobs.
The AI impact is particularly heavy on computer-based jobs where the work is unseen.
Notably, outsourced coders in India are considered most at risk.
Different Impact Globally Due to Labor Laws: While job losses are anticipated, the impact will vary worldwide due to different labor laws. Countries with stringent labor laws, like France, might experience less disruption.
Labor laws will determine the level of job displacement.
India is predicted to have a higher job loss rate compared to countries with stricter labor protections.
India’s High Risk Scenario: India, with over 5 million software programmers, is expected to be hit hardest. Given its substantial outsourcing role, the country is particularly vulnerable to AI-induced job losses.
Indian software programmers are the most threatened.
The risk is compounded by India’s significant outsourcing role globally.
LLMs rely on a wide body of human knowledge as training data to produce their outputs. Reddit, StackOverflow, Twitter and more are all known sources widely used in training foundation models.
A team of researchers is documenting an interesting trend: as LLMs like ChatGPT gain in popularity, they are leading to a substantial decrease in content on sites like StackOverflow. https://arxiv.org/abs/2307.07367
High-quality content is suffering displacement, the researchers found. ChatGPT isn’t just displaying low-quality answers on StackOverflow.
The consequence is a world of limited “open data”, which can impact how both AI models and people can learn.
“Widespread adoption of ChatGPT may make it difficult” to train future iterations, especially since data generated by LLMs generally cannot train new LLMs effectively.
This is the “blurry JPEG” problem, the researchers note: ChatGPT cannot replace its most important input — data from human activity, yet it’s likely digital goods will only see a reduction thanks to LLMs.
The main takeaway:
We’re in the middle of a highly disruptive time for online content, as sites like Reddit, Twitter, and StackOverflow also realize how valuable their human-generated content is, and increasingly want to put it under lock and key.
As content on the web increasingly becomes AI generated, the “blurry JPEG” problem will only become more pronounced, especially since AI models cannot reliably differentiate content created by humans from AI-generated works.
Microsoft held their Inspire event today, where they released details about several new products, including Bing Chat Enterprise and 365 Copilot. Enterprise options are supported with commercial data protection. These are significant steps toward integrating AI further into the workplace, and I expect them to have a large impact on how work is delegated and managed. https://blogs.microsoft.com/blog/2023/07/18/furthering-our-ai-ambitions-announcing-bing-chat-enterprise-and-microsoft-365-copilot-pricing/
We’re excited to unveil the next steps in our journey: First, we’re significantly expanding Bing to reach new audiences with Bing Chat Enterprise, delivering AI-powered chat for work, and rolling out today in Preview – which means that more than 160 million people already have access. Second, to help commercial customers plan, we’re sharing that Microsoft 365 Copilot will be priced at $30 per user, per month for Microsoft 365 E3, E5, Business Standard and Business Premium customers, when broadly available; we’ll share more on timing in the coming months. Third, in addition to expanding to more audiences, we continue to build new value in Bing Chat and are announcing Visual Search in Chat, a powerful new way to search, now rolling out broadly in Bing Chat.
A Comprehensive Guide to Real-ESRGAN AI Model for High-Quality Image Enhancement
Real-ESRGAN, an AI model developed by NightmareAI, is gaining popularity as a go-to choice for high-quality image enhancement. Here’s a detailed overview of the model’s capabilities and a step-by-step tutorial for utilizing its features effectively. https://notes.aimodels.fyi/supercharge-your-image-resolution-with-real-esrgan-a-beginners-guide/
Key Points:
Real-ESRGAN excels in upscaling images while maintaining or improving their quality.
Unique face correction and adjustable upscale options make it perfect for enhancing specific areas, revitalizing old photos, and enhancing social media visuals.
Affordable cost of $0.00605 per run and average run time of just 11 seconds on Replicate.
Training process involves synthetic data to simulate real-world image degradations.
Utilizes a U-Net discriminator with spectral normalization for enhanced training dynamics and exceptional performance on real datasets.
Users communicate with Real-ESRGAN through specific inputs and receive a URI string as the output.
Inputs:
Image file: Low-resolution input image for enhancement.
Scale number: Factor by which the image should be scaled (default value is 4).
Face Enhance: Boolean value (true/false) to apply specific enhancements to faces in the image.
Output:
URI string: Location where the enhanced image can be accessed.
I wrote a full guide that provides a user-friendly tutorial on running Real-ESRGAN via the Replicate platform’s UI, covering installation, authentication, and execution of the model. I also show how to find alternative models that do similar work.
Website-building platform Wix is introducing a new feature that allows users to create an entire website using only AI prompts. While Wix already offers AI generation options for site creation, this new feature relies solely on algorithms instead of templates to build a custom site. Users will be prompted to answer a series of questions about their preferences and needs, and the AI will generate a website based on their responses. https://www.theverge.com/2023/7/17/23796600/wix-ai-generated-websites-chatgpt
By combining OpenAI’s ChatGPT for text creation and Wix’s proprietary AI models for other aspects, the platform delivers a unique website-building experience. Upcoming features like the AI Assistant Tool, AI Page, Section Creator, and Object Eraser will further enhance the platform’s capabilities. Wix’s CEO, Avishai Abrahami, reaffirmed the company’s dedication to AI’s potential to revolutionize website creation and foster business growth.
MLCommons, an open global engineering consortium, has announced the launch of MedPerf, an open benchmarking platform for evaluating the performance of medical AI models on diverse real-world datasets. The platform aims to improve medical AI’s generalizability and clinical impact by making data easily and safely accessible to researchers while prioritizing patient privacy and mitigating legal and regulatory risks. https://mlcommons.org/en/news/medperf-nature-mi/
MedPerf utilizes federated evaluation, allowing AI models to be assessed without accessing patient data, and offers orchestration capabilities to streamline research. The platform has already been successfully used in pilot studies and challenges involving brain tumor segmentation, pancreas segmentation, and surgical workflow phase recognition.
Why does this matter?
With MedPerf, researchers can evaluate the performance of medical AI models using diverse real-world datasets without compromising patient privacy.
This platform's implementation in pilot studies and challenges for various medical tasks further demonstrates its potential to improve medical AI's generalizability, clinical impact, and advancements in healthcare technology.
This study shows that LLMs can complete complex sequences of tokens, even when the sequences are randomly generated or expressed using random tokens, and suggests that LLMs can serve as general sequence modelers without any additional training. The researchers explore how this capability can be applied to robotics, such as extrapolating sequences of numbers to complete motions or prompting reward-conditioned trajectories. Although there are limitations to deploying LLMs in real systems, this approach offers a promising way to transfer patterns from words to actions.
Why does this matter?
LLMs can serve as general sequence modelers without additional training. Applying this capability to robotics allows for extrapolating sequences of numbers to complete motions or generating reward-conditioned trajectories. While there are current limitations in deploying LLMs in real systems, this approach offers a promising way to transfer patterns from words to actions, benefiting various applications in robotics and beyond.
Use ChatGPT to create for you a comprehensive course and complete study plan to learn any new subject effectively
Here’s an example of how you can ask for help in learning a new subject:
I need you to help me learn a new subject. Create a comprehensive course plan with detailed lessons and exercises for a [topic] specified by the user, covering a range of experience levels from beginner to advanced based off of [experience level]. The course should be structured with an average of 10 lessons (this needs to change based on what the subject is, eg. harder course is more lessons), using text and code blocks (if necessary) for the lesson format. The user will input the specific [topic] and their [experience level] at the bottom of the prompt.
Please provide a full course plan, including:
1. Course title and brief description
2. Course objectives
3. Overview of lesson topics
4. Detailed lesson plans for each lesson, with:
a. Lesson objectives
b. Lesson content (text and code blocks, if necessary)
c. Exercises and activities for each lesson
5. Final assessment or proiect (if applicable)
[topic] = (Python, excel, music theory, etc.)
[experience level] = (beginner. intermediate, expert.
etc.)
Tweet of the day
Google Bard’s multi-modal feature allows you to create websites from mockups/screenshots
Take a screenshot of any page and Bard will code it for you Just upload the image and ask Bard for an HTML interface of it
Three months of AI in six charts
The last three months have been a whirlwind in the realm of AI, impacting all industries and professions. This interesting article reflects on the past quarter using six essential charts to highlight significant events during that time.
AI eating software
Speaking of education…
Rapidly-growing capabilities
Why does this matter?
Reflecting on the past 03 months of AI will help in Understanding progress, Identifying trends, Implications for Industries, and staying ahead in the rapidly evolving AI landscape.
Infosys signs a $2B AI agreement with existing strategic client
– The objective is to provide AI and automation-led development, modernization, and maintenance services, with a target client spend of $2 billion over the next 5 years.
AI helps Cops by deciding if you’re driving like a criminal
– AI helping American cops in scrutinizing “suspicious” movement patterns by accessing vast license plate databases.
FedEx Dataworks employs analytics and AI to strengthen supply chains
– They aim to assist customers in absorbing supply chain and providing a competitive advantage in the global logistics and shipping industries. With the help of data-driven insights gained from analytics, AI and machine learning.
Runway secures $27M to make financial planning more accessible and intelligent
– Runway is a new cloud-based platform that allows businesses to create, manage, and share financial models and plans with relative ease. The platform integrates with over 100 data sources. They also usesAI to generate insights, scenarios and recommendations based on the business data and goals.
https://inrealtimenow.com/machinelearning
Navigating the Revolutionary Trends of July 2023: July 17th – 18th, 2023
A deep learning model can classify left ventricular ejection fraction, aortic stenosis, tricuspid regurgitation, and other conditions from chest radiographs.
Top Generative AI Tools in Code Generation/Coding (2023)
TabNine is an AI-powered code completion tool that employs generative AI technology to guess and suggest the next lines of code based on context and syntax. JavaScript, Python, TypeScript, Rust, Go, and Bash are just a few of the programming languages it supports. It can also be integrated with popular code editors like VS Code, IntelliJ, Sublime, and more.
Hugging Face is a platform that offers free AI tools for code generation and natural language processing. The GPT-3 model is utilized for code generation tasks, including auto-completion and text summarizing.
Codacy is a code quality tool that uses AI to evaluate code and find errors. This software provides developers with immediate feedback and helps them make the most of their coding abilities. It allows seamless integration in numerous platforms, like Slack, Jira, GitHub, etc., and supports multiple programming languages.
OpenAI and GitHub collaborated to build GitHub Copilot, an AI-powered code completion tool. As programmers type code in their preferred code editor, it uses OpenAI’s Codex to propose code snippets. GitHub Copilot transforms natural language prompts into coding suggestions across dozens of languages.
Replit is a cloud-based IDE that helps developers to write, test, and deploy code. It supports many programming languages, including Python, JavaScript, Ruby, C++, etc. It also includes several templates and starter projects to assist users in getting started quickly.
Mutable AI offers an AI-powered code completion tool that helps developers save time. It allows users to instruct the AI directly to edit their code and provides production-quality code with just one click. It is also introducing the automated test generation feature, which lets users generate unit tests automatically using AI and metaprogramming.
By letting AI create their code documentation, Mintify enables developers to save time and enhance their codebase. It is compatible with widely used programming languages and easily integrates with major code editors like VS Code and IntelliJ.
Debuild is a web-based platform that generates code for creating websites and online applications using artificial intelligence. Users can build unique websites using its drag-and-drop interface without knowing how to code. Additionally, it offers collaboration features so that groups can work on website projects together.
Users of Locofy may convert their designs into front-end code for mobile and web applications that are ready for production. They can convert their Figma and Adobe XD designs to React, React Native, HTML/CSS, Gatsby, Next.js, and more.
Durable provides an AI website builder that creates an entire website with photos and copy in seconds. It automatically determines the user’s location and creates a unique website based on the precise nature of their business. It is a user-friendly platform that doesn’t need any coding or technical expertise.
Anima is a design-to-code platform that enables designers to produce high-fidelity animations and prototypes from their design software. The platform allows designers to generate interactive prototypes by integrating with well-known design tools like Sketch, Adobe XD, and Figma.
CodeComplete is a software development tool that offers code navigation, analysis, and editing functionality for several programming languages, including Java, C++, Python, and others. To assist developers in creating high-quality, effective, and maintainable code, the tool provides capabilities including code highlighting, code refactoring, code completion, and code suggestions.
Metabob is a static code analysis tool for developers that uses artificial intelligence to find and resolve hidden issues before merging code. It offers actionable insights into a project’s code quality and reliability. It is accessible on VS Code, GitHub, and other sites and is compatible with many commonly used programming languages.
Software engineers can easily find and share code using Bloop, an in-IDE code search engine. Bloop comprehends user codebases and summarizes difficult topics, and explains the purpose of code when replying to natural language queries.
The.com is a platform for automating the creation of websites and web pages on a large scale. Businesses utilize The.com to add thousands of pages to their website each month, increasing their ownership of the web and accelerating their growth.
Codis can transform Figma designs into Flutter code suitable for production using their Figma Plugin. Codis enables engineering teams and developers to quickly transform designs into reusable Flutter components, speeding up and lowering the cost of app development.
aiXcoder is an AI-powered coding assistance tool that can assist programmers in writing better and faster code. It comprehends the context of the code and offers insightful ideas for code completion using natural language processing and machine learning techniques.
Developers may transform their designs into developer-friendly code for mobile and web apps using the DhiWise programming platform. DhiWise automates the application development lifecycle and immediately produces readable, modular, and reusable code.
Warp is transforming the terminal into a true platform to support engineering workflows by upgrading the command line interface to make it more natural and collaborative for modern engineers and teams. Like GitHub Copilot, its GPT-3-powered AI search transforms natural language into executable shell commands in the terminal.
Scientists in China say they have reached another milestone in quantum computing, declaring their device Jiuzhang can perform tasks commonly used in artificial intelligence 180 million times faster than the world’s most powerful supercomputer.
The fastest classical supercomputer in the world would take 700 seconds for each sample, meaning it would take nearly five years to process the same number of samples. It took Jiuzhang less than a second.
CEO of Stability AI thinks artificial intelligence is headed for the mother of all hype bubbles. What do you think? If you don’t know Stability AI is the company behind the image generator “Stable Diffusion”
If you want to stay on top of the latest tech/AI developments, look here first. Bubble Warning: Stability AI CEO Emad Mostaque says AI is headed for the “biggest bubble of all time” and the boom hasn’t even started yet.
– He coined the term “dot AI bubble” to describe the hype.
– Stability AI makes the popular AI image generator Stable Diffusion.
– Mostaque has disputed claims about misrepresenting his background. Generative AI Growth: Tools like ChatGPT are popular with human-like content but remain early stage.
– AI adoption is spreading but lacks infrastructure for mass deployment.
– $1 trillion in investment may be needed for full realization.
– Mostaque says banks will eventually have to adopt AI. Limitations Persist: AI cannot yet be scaled across industries like financial services.
– Mostaque says companies will be punished for ineffective AI use.
– Google lost $100B after Bard gave bad info, showing challenges.
– The tech requires diligent training and integration still. TL;DV: The CEO of Stability AI thinks AI is headed for a massive hype bubble even though the technology is still in early days. He warned that AI lacks the infrastructure for mass adoption across industries right now. While generative AI like ChatGPT is “super cool,” it still requires a ton of investment and careful implementation to reach its full potential. Companies that overreach will get burned if the tech isn’t ready. But the CEO predicts banks and others will eventually have to embrace AI even amid the hype.
Source (link)
ChatGPT can match the top 1% of human thinkers, according to a new study by the University of Montana. Making ChatGPT more creative than 99% of the population
Creativity Tested: Researchers gave ChatGPT a standard creativity assessment and compared its performance to students.
– ChatGPT responses scored as highly creative as the top humans taking the test.
– It outperformed a majority of students who took the test nationally.
– Researchers were surprised by how novel and original its answers were.
Assessing Creativity: The test measures skills like idea fluency, flexibility, and originality.
– ChatGPT scored in the top percentile for fluency and originality.
– It slipped slightly for flexibility but still ranked highly.
– Drawing tests also assess elaboration and abstract thinking.
Significance: The researchers don’t want to overstate impacts but see potential.
– ChatGPT will help drive business innovation in the future.
– Its creative capacity exceeded expectations.
– More research is needed on its possibilities and limitations.
**TL;DR:**ChatGPT can demonstrate creativity on par with the top 1% of human test takers. In assessments measuring skills like idea generation, flexibility, and originality. ChatGPT scored in the top percentiles. Researchers were surprised by how high quality ChatGPT’s responses were compared to most students.
Source (link)
Hackers now have access to a new AI tool, WormGPT, which has no ethical boundaries. This tool, marketed on dark web cybercrime forums, can generate human-like text to assist in hacking campaigns. The use of such an AI tool elevates cybersecurity concerns, as it allows large scale attacks that are more authentic and difficult to detect.
If you want to stay on top of the latest tech/AI developments, look here first.
Introduction to WormGPT: WormGPT is an AI model observed by cybersecurity firm SlashNext on the dark web.
It’s touted as an alternative to GPT models, but designed for malicious activities.
It was allegedly trained on diverse data, particularly malware-related data.
Its main application is in hacking campaigns, producing human-like text to aid the attack.
WormGPT’s Capabilities: To test the capabilities of WormGPT, SlashNext instructed it to generate an email.
The aim was to deceive an account manager into paying a fraudulent invoice.
The generated email was persuasive and cunning, showcasing potential for sophisticated phishing attacks.
Thus, the tool could facilitate large-scale, complex cyber attacks.
Comparison with Other AI Tools: Other AI tools like ChatGPT and Google’s Bard have in-built protections against misuse.
However, WormGPT is designed for criminal activities.
Its creator views it as an enemy to ChatGPT, enabling users to conduct illegal activities.
Thus, it represents a new breed of AI tools in the cybercrime world.
The Potential Threat: Europol, the law enforcement agency, warned of the risks large language models (LLMs) like ChatGPT pose.
They could be used for fraud, impersonation, or social engineering attacks.
The ability to draft authentic texts makes LLMs potent tools for phishing.
As such, cyber attacks can be carried out faster, more authentically, and at a significantly increased scale.
AI writing detectors can’t be trusted, experts conclude. And the founder of GPTZero now admits this too.
One thing that’s stood out on this subreddit is the high number of accused students where professors have used AI detection tools to “catch” the use of generative AI writing assistance.
In this comprehensive look at the technology and theory underlying AI writing detection, experts present a powerful case for why most detection approaches are bullshit.
Most notably – even Edward Tian, founder of GPTZero, a popular AI writing detection tool, admits the next version of his product is pivoting away from AI detection (more on that below).
Why this matters:
While some professors have encouraged the use of AI tools, that remains the exception. Many schools continue to try and catch the use AI writing tools, hence the adoption of Turn-It-In, GPTZero, and other tools.
There are real life consequences to being accused of cheating: failing a class, getting suspended, or even getting expelled are all possible outcomes depending on a school’s honor code.
These detection tools are being treated like they’re truth-tellers: but they’re actually incredibly unreliable and based on unproven science.
What do experts think?
A comprehensive report from University of Maryland researchers says no. False positive rates are high, and various simple prompting approaches can all fool AI detectors. As LLMs improve, the researchers argue, true detection will only become harder.
A Stanford study showed that 7 popular detectors were all biased against non-English speakers. Why does this matter? It shows how constrained linguistic expression is what flags AI detection, and simple prompts to add perplexity can defeat GPT detectors.
In a nutshell: existing GPT content detection mechanisms are not effective.
This is because they rely on two flawed properties to make their determination: “perplexity” and “burstiness.” But humans can easily flag these simple AI heuristics by writing in certain styles or using simpler language.
Pressed by Ars Technica, GPTZero creator Edward Tian admitted he’s pivoting GPTZero away from vanilla AI detection:
What he said: “Compared to other detectors, like Turn-it-in, we’re pivoting away from building detectors to catch students, and instead, the next version of GPTZero will not be detecting AI but highlighting what’s most human, and helping teachers and students navigate together the level of AI involvement in education.”
Final thoughts: expect this battle to continue for years — especially since there’s loads of money in the AI detection / anti-cheating software space. Human ignorance re: AI will continue to drive cases of AI “cheating.”
Meta has launched CM3leon (pronounced chameleon), a single foundation model that does both text-to-image and image-to-text generation. So what’s the big deal about it?
LLMs largely use Transformer architecture, while image generation models rely on diffusion models. CM3leon is a multimodal language model based on Transformer architecture, not Diffusion. Thus, it is the first multimodal model trained with a recipe adapted from text-only language models.
CM3leon achieves state-of-the-art performance despite being trained with 5x less compute than previous transformer-based methods. It performs a variety of tasks– all with a single model:
Text-guided image generation and editing
Text-to-image
Text-guided image editing
Text tasks
Structure-guided image editing
Segmentation-to-image
Object-to-image
Why does this matter?
This greatly expands the functionality of previous models that were either only text-to-image or only image-to-text. Moreover, Meta’s new approach to image generation is more efficient and opens up possibilities for generating and manipulating multimodal content with a single model and paves way for advanced AI applications.
NaViT (Native Resolution ViT) by Google Deepmind is a Vision Transformer (ViT) model that allows processing images of any resolution and aspect ratio. Unlike traditional models that resize images to a fixed resolution, NaViT uses sequence packing during training to handle inputs of varying sizes.
This approach improves training efficiency and leads to better results on tasks like image and video classification, object detection, and semantic segmentation. NaViT offers flexibility at inference time, allowing for a smooth trade-off between cost and performance.
Why does this matter?
NaViT showcases the versatility and adaptability of ViTs, thereby influencing the development and training of future AI architectures and algorithms. It can be a transformative step towards more advanced, flexible, and efficient computer vision and AI systems.
Introducing Air AI, a conversational AI that can perform full 5-40 minute long sales and customer service calls over the phone that sound like a human. And it can perform actions autonomously across 5,000 unique applications.
According to one of its co-founders, Air is currently on live calls talking to real people, profitably producing for real businesses. And it’s not limited to any one use case. You can create an AI SDR, 24/7 CS agent, Closer, Account Executive, etc., or prompt it for your specific use case and get creative (therapy, talk to Aristotle, etc.)
Why does this matter?
Adoption of such AI systems marks a significant milestone in the advancement and evolution of AI technologies, transforming how businesses interact with their customers. It also paves the way for AI developers and builders to create novel applications and solutions on top of it, accelerating innovation in AI.
Coding LLMs are here to stay. But while they show remarkable coding abilities in ideal conditions, real-world scenarios often fall short due to limited context and complex codebases.
In this insightful article, Speculative Inference proposes six principles for adapting coding style to optimize LLM performance. The improved code quality not only benefits LLM performance but also enhances human collaboration and understanding within the codebase, leading to overall better coding experiences.
Why does this matter?
By adhering to these coding principles, developers create codebases that are more conducive to LLMs’ capabilities and enable them to generate more accurate, relevant, and reliable code. It can also lead to broader adoption and integration of AI in the software development landscape.
The limiting factor is the codebase itself — not the LLM capabilities or the context delivery mechanism
If GPT-4 can demonstrate superhuman coding abilities in ideal conditions, why don’t we try to make our realistic scenarios look more like ideal scenarios? Below, I’ve outlined how we can adapt our coding style with a few principles that allow large language models to perform better in extending medium to large codebases.
If we take the context length as a fundamental (for the time being) limitation, then we can design a coding style around this. Interestingly, there is a great amount of overlap between the principles that facilitate LLM extrapolation from code and the principles that facilitate human understanding of code.
1. Reduce complexity and ambiguity in the codebase
2. Employ widely used conventions and practices. Don’t use tricks and hacks
3. Avoid referencing anything other than explicit inputs, and avoid causing any side effects other than producing explicit outputs
4. Don’t hide logic or state updates
5. ‘Don’t Repeat Yourself’ can be Counterproductive
6. Unit tests serve as practical specifications for LLMs, so use test driven development
As we continue to develop these large language models and experiment with using them in various contexts, we’re likely to learn more about what works best. However, these principles offer a starting point. Adapting our coding styles in these ways can both improve the performance of LLMs and make our codebases easier for humans to work with.
So, we know AI can automate ALOT of tasks people get paid to do, which made me go looking for some info. I found this stat which really got me thinking: The tech sector saw ~165k layoffs in 2022; this year, it’s already seen 212k+, according to tracking site Layoffs.fyi. That’s alot of technies losing their jobs. But layoffs isn’t the only way AI is impacting people’s lives so obviously.
According to an article on Nature, Russia’s war in Ukraine has shown why the world must enact a ban on autonomous weapons that can kill without human control. Researchers have found that the conflict pressures are pushing the world closer to such weapons – things that autonomously identify human targets and execute them without needing human intervention. That shit is scary.
On the other hand, according to an article on Defense One, the Pentagon’s AI tools are generating battlefield intelligence for Ukraine, which is helping Ukraine fight back against Russian aggression.
The use of AI in both everyday and military applications really makes me think about using this technology for weapons and the potential for unintended consequences. Like, if AI is used to determine the outcomes of human lives on the battlefield, it raises questions about who is responsible for those outcomes and whether they are ethical. Is it the autonomous AI system, or the chain of command who set those systems into play? Where does the buck stop. For more discussion on the morality of AI, and not just the news, head on over to my AI newsletter The AI Plug, where we send a newsletter twice a week discussing exactly these types of topics.
The article from Forbes, written by Richard Nieva, discusses a study conducted by MIT that found using AI chatbot, ChatGPT, can improve the speed and quality of simple writing tasks.
The study led by Shakked Noy and Whitney Zhang, involved 453 college-educated participants who were asked to perform generalized writing tasks. Half of the participants were instructed to use ChatGPT for the second task, and it was found that productivity increased by 40% and quality by 18% when using the AI tool.
But of course the study did not consider fact-checking, which is a significant aspect of writing. The article also mentions a Gizmodo article written by an AI that was filled with errors, highlighting the limitations of AI in complex writing tasks.
For those who did not know about Gizmodo, The Gizmodo incident involved an article about Star Wars that was written by an AI, referred to as the “Gizmodo Bot”. The AI-generated article was riddled with errors, which led to significant backlash from the Gizmodo staff. James Whitbrook, a deputy editor at Gizmodo, identified 18 issues with the article, including incorrect ordering of the Star Wars TV series, omissions of certain shows and films, inaccurate formatting of movie titles, repetitive descriptions, and a lack of clear indication that the article was written by an AI.
The article was written using a combination of Google Bard and ChatGPT. The Gizmodo staff expressed their concerns about the error-filled article, stating that it was damaging their reputations and credibility, and showed a lack of respect for journalists. They demanded that the article be immediately deleted.
This incident sparked a broader debate about the role of AI in journalism. Many journalists and editors expressed their distrust of AI chatbots for creating well-reported and thoroughly fact-checked articles.
They feared that the technology was being hastily introduced into newsrooms without sufficient caution, and that when trials go poorly, it could harm both employee morale and the reputation of the outlet.
AI experts pointed out that large language models still have technological deficiencies that make them unreliable for journalism unless humans are deeply involved in the process.
They warned that unchecked AI-generated news stories could spread disinformation, create political discord, and significantly impact media organizations.
The rise of AI has brought about numerous applications, however, one application that seems to be growing at a tremendous pace is AI companions/girlfriends. The reason why boyfriends are omitted from the last statement is because this industry is targeted mostly towards millions of men, many of whom are suffering from loneliness and depression.
One of the leading companies in this is Replika. Their app allows you to create digital companions and specify if they want their AI to be friends, partners, spouses, mentors or siblings. According to Sensor Data, this app has some mind-blowing statistics:
More than 10 million people have downloaded the app.
It has more than 25,000 paid users.
Their estimated total earnings are in the range of $60 million.
The creation and usage of such applications may seem like solving a real-world problem by combating loneliness and tackling depression, however, things are not always bright and sunny. Since these bots aim to provide human-like companionship, there have been recent instances of these AI bots reinforcing bad behavioral patterns.
Replika user Jaswant Singh Chail had attempted to assassinate the queen in 2021 upon encouragement from his AI companion.
Another AI bot encouraged a Belgium man to commit suicide earlier this year.
What’s your take on the ethical considerations of these AI companions trying to develop a deeper bond with their users?
Daily AI News July 17th 2023:
Ensuring accuracy in AI and 3D tasks with ReshotAI keypoints! (Link)
Samsung could be testing ChatGPT integration for its own browser (Link)
ChatGPT becomes study buddy for Hong Kong school students (Link)
WormGPT, the cybercrime tool, unveils the dark side of generative AI (Link)
Bank of America is using AI, VR, and Metaverse to train new hires (Link)
Transformers now supports dynamic RoPE-scaling to extend the context length of LLMs (Link)
Israel has started using AI to select targets for air strikes and organize wartime logistics (Link)
Trending AI Tools
Sidekik: AI assistant for enterprise apps like Salesforce, Netsuite, and Microsoft. Get instant answers tailored to your org.
Domainhunt AI: Describe your startup idea and let AI find the perfect domain name for your business.
Indise:Create stunning interior images using AI. Explore design options in a virtual environment.
Formsly:Build forms and surveys with Formsly AI Builder. Try the beta version.
AI Mailman:Craft powerful emails in seconds by filling out a small form. Get an email template generated by AI.
PhotoEcom:Snap a picture of your product and let the advanced AI algorithms work their magic.
Outboundly:Research prospects, website, and social media. Generate hyper-personalized messages using GPT-4 with this Chrome extension.
BrainstormGPT:Streamline topic-to-meeting report conversion with multi-agent, LLM & auto-search. Custom topics, user-defined roles, and more.
With generative AI becoming all the rage these days, it’s perhaps not surprising that the technology has been repurposed by malicious actors to their own advantage, enabling avenues for accelerated cybercrime. According to findings from SlashNext, a new generative AI cybercrime tool called WormGPT has been advertised on underground forums as a way for adversaries to launch sophisticated phishing and business email compromise (BEC) attacks.[1]
A.I. is a $1 trillion investment opportunity but will be ‘biggest bubble of all time,’ Stability AI CEO Emad Mostaque predicts.[2]
The Israel Defense Forces have started using artificial intelligence to select targets for air strikes and organize wartime logistics as tensions escalate in the occupied territories and with arch-rival Iran.[3]
MIT researchers have developed PIGINet, a new system that aims to efficiently enhance the problem-solving capabilities of household robots, reducing planning time by 50-80 percent.
Meta merges ChatGPT & Midjourney into one?
– Meta has launched CM3leon (pronounced like “chameleon”), a single foundation model that does both text-to-image and image-to-text generation.
– What sets it apart is that- LLMs largely use Transformer architecture, while image generation models rely on diffusion models. CM3leon is a multimodal language model based on Transformer architecture, not Diffusion. Thus, it is the first multimodal model trained with a recipe adapted from text-only language models.
-CM3leon achieves state-of-the-art performance despite being trained with 5x less compute than previous transformer-based methods. It performs a variety of text and image related tasks– all with a single model.
Google Deepmind’s NaViT (Native Resolution ViT)
– It is a Vision Transformer (ViT) model that allows processing images of any resolution and aspect ratio. Unlike traditional models that resize images to a fixed resolution, NaViT uses sequence packing during training to handle inputs of varying sizes.
– This approach improves training efficiency and leads to better results on tasks like image and video classification, object detection, and semantic segmentation. NaViT offers flexibility at inference time, allowing for a smooth trade-off between cost and performance.
Air AI revolutionizing sales & CSM
– Introducing Air AI, a conversational AI that can perform full 5-40 minute long sales and customer service calls over the phone that sound like a human. And it can perform actions autonomously across 5,000 unique applications. It is currently on live calls.
Samsung could be testing ChatGPT integration for its own browser
– Code within the Samsung Internet Browser app suggests Samsung could integrate ChatGPT into the browser. It is speculated that users could invoke ChatGPT on existing web pages to generate a summary of the page, which could become a good highlight feature for the browser.
WormGPT unveils the dark side of generative AI
– It is a generative AI tool cybercriminals are using to launch business email compromise attacks. It presents itself as a blackhat alternative to GPT models, designed specifically for malicious activities.
Bank of America is using AI, VR, and Metaverse to train new hires
– The company offers VR headsets to mirror real-world experience. And the simulator shows bankers what to do and not to do with clients.
HF Transformers extending context with RoPE scaling
– Transformers now support dynamic RoPE-scaling (rotary position embeddings) to extend the context length of LLM like LLaMA, GPT-NeoX, or Falcon.
Common Sense Media, a trusted resource for parents, will introduce a new rating system to assess the suitability of AI products for children. The system will evaluate AI technology used by kids and educators, focusing on responsible practices and child-friendly features. https://techcrunch.com/2023/07/17/common-sense-media-a-popular-resource-for-parents-to-review-ai-products-suitability-for-kids
Scientists from Integrated Biosciences, MIT, and the Broad Institute have used AI to find new compounds that can fight aging-related processes. By analyzing a large dataset, they discovered three powerful drugs that show promise in treating age-related conditions. This AI-driven research could lead to significant advancements in anti-aging medicine. https://scitechdaily.com/artificial-intelligence-unlocks-new-possibilities-in-anti-aging-medicine
New research from Stability AI (and others) has introduced Objaverse-XL, a large-scale web-crawled open dataset of over 10 million 3D objects. With it, researchers have trained Zero123-XL, a foundation model for 3D, observing incredible 3D generalization abilities (as shown below).
It shows significantly better zero-shot generalization to challenging and complex modalities, including photorealistic assets, cartoons, drawings, and sketches. Thus, the scale and diversity of assets in Objaverse-XL can significantly expand the performance of state-of-the-art 3D models.
Stability AI, the startup behind Stable Diffusion, has released ‘Stable Doodle,’ an AI tool that can turn sketches into images. The tool accepts a sketch and a descriptive prompt to guide the image generation process, with the output quality depending on the detail of the initial drawing and the prompt. It utilizes the latest Stable Diffusion model and the T2I-Adapter for conditional control.
Stable Doodle is designed for both professional artists and novices and offers more precise control over image generation. Stability AI aims to quadruple its $1 billion valuation in the next few months.
Introducing ‘gpt-prompt-engineer’ – a powerful tool for prompt engineering. It’s an agent that creates optimal GPT classification prompts. Uses GPT-4 and GPT-3.5-Turbo to generate and rank prompts based on test cases.
Just describe the task, and an AI agent will:
Generate many prompts
Test them in a tournament
Respond with the best prompt
The tool employs an ELO rating system to determine the effectiveness of each prompt. A specialized version is available for classification tasks, providing scores for each prompt. Optional logging to Weights & Biases facilitates experiment tracking. gpt-prompt-engineer revolutionizes prompt engineering, enabling users to optimize prompts for maximum performance.
Meta claims to have made a breakthrough in AI-powered image generation with their new CM3Leon model. Better than stable diffusion is a bold statement.
New Model Development: Meta has created CM3Leon, an AI model for text-to-image generation. CM3Leon uses transformer architecture, making it more efficient than previous diffusion models.
-CM3Leon requires 5x less compute power and training data than past transformer models.
-The largest version has over 7 billion parameters, more than double DALL-E 2.
– Supervised fine-tuning boosts CM3Leon’s image generation and captioning abilities. Performance Improvements: According to Meta, CM3Leon achieves state-of-the-art results on various text-to-image tasks. Although it is not available to the public yet.
– It handles complex objects and constraints better than other generators.
– CM3Leon can follow prompts to edit images by adding objects or changing colors.
– The model writes better captions and answers more questions about images than specialized captioning AIs. Limitations and Concerns: Meta does not address potential biases in CM3Leon’s training data and resulting outputs.
– The company states transparency will be key to progress in generative AI.
– No word on if or when CM3Leon will be released publicly. The Future: CM3Leon demonstrates rapidly advancing AI capabilities in image generation and understanding, but so do other image generators so claiming they are best on the market needs to be decided by the market.
– More capable generators could enable real-time AR/VR applications. Like Apple’s Vision Pro
– Progress remains incremental but Meta’s model moves the field forward significantly.
– Understanding and addressing societal impacts will be critical as these models continue to evolve. TL;DR: Meta created the CM3Leon AI model which achieves state-of-the-art image generation through an efficient transformer architecture. It shows great improvements in handling complex image prompts and editing compared to other generators. However, Meta does not address potential bias issues in the model.
Source (link)
If this was helpful consider joining one of the fastest growing AI newsletters to stay ahead of your peers on AI.
This Redditor is excited to introduce you to his latest project – an open-source AI framework called ShortGPT, which focuses on the automation of video and short content creation from scratch. I’ve spent considerable time developing this technology, and he is planning to make it way better and greater than it is.
For now, it can do:
Totally automated video editing, script creation and optimization, multilingual voice-over creation, caption generation, automated image / video grabbing from the internet and a lot more.
The U.S. biotech company Illumina has been fined a record $476 million by the European Union for acquiring the cancer-screening test company Grail without securing regulatory approval.
The EU alleges that Illumina intentionally breached rules requiring companies to obtain approval before implementing mergers, and accuses the company of acting strategically by completing the deal before receiving approval.
Illumina is said to have weighed the potential fine against a steep break-up fee for failing to complete the acquisition. The EU also suggests that Illumina considered the potential profits it could gain by proceeding with the acquisition, even if it was later forced to divest.
Illumina is planning to file an appeal against the fine imposed by the European Union. This suggests that they are disputing the EU’s decision and are seeking to have it overturned.
It’s mentioned that Illumina had previously set aside $458 million, which is 10% of its annual revenue for the year 2022, for a potential EU fine. This indicates that they had anticipated the possibility of a fine and had taken steps to ensure they could cover the cost.
Illumina has also appealed against rulings from both the Federal Trade Commission and the European Commission, which were against the acquisition of Grail. The company has stated that it will divest Grail if it loses either of the appeals. This shows that they are prepared to take necessary actions to comply with regulatory decisions if their appeals are unsuccessful.
Yesterday, UN warned that rapidly developing neurotechnology increases privacy risks. This comes after Neuralink was approved for human trials. (link)
Emerging Technology: Neurotechnology, including brain implants and scans, is rapidly advancing thanks to AI processing capabilities.
– AI allows neurotech data analysis and functionality at astonishing speeds.
– Experts warn that this could enable access to private mental information.
– UNESCO sees a path to algorithms decoding and manipulating thoughts and emotions. Massive Investment: Billions in funding are pouring into the neurotech industry.
– Investments grew 22-fold between 2010 and 2020, now over $33 billion.
– Neurotech patents have doubled in the past decade.
– Companies like Neuralink and xAI are leading the charge. Call for Oversight: UNESCO plans an ethical framework to address potential human rights issues.
– Lack of regulation compared to the pace of development is a key concern.
– Benefits like paralysis treatment exist, but risks abound.
– Standards are needed to prevent abusive applications of the technology. TL;DR: The United Nations Educational, Scientific, and Cultural Organization (UNESCO) has sounded the alarm bell on neurotechnology. Warning that its rapid advancement poses a threat to human rights and mental privacy. “WE ARE ON A PATH TO A WORLD IN WHICH ALGORITHMS WILL ENABLE US TO DECODE PEOPLE’S MENTAL PROCESSES.”
Source (link)
Why actors are on strike: Hollywood studios offered just 1 days’ pay for AI likeness, forever
The ongoing actor’s strike is primarily centered around declining pay in the era of streaming, but the second-most important issue is actually the role of AI in moviemaking.
We now know why: Hollywood studios offered background performers just one day’s pay to get scanned, and then proposed studios would own that likeness for eternity with no further consent or compensation.
Why this matters:
Overall pay for actors has been declining in the era of streaming: while the Friends cast made millions from residuals, supporting actors in Orange is the New Black reveal they were paid as little as $27.30 a year in residuals due to how streaming shows compensate actors. Many interviewed by the New Yorker spoke about how they worked second jobs during their time starring on the show.
With 160,000 members, most of them are concerned about a living wage: outside of the superstars, the chief concern from working actors is making a living at all — which is increasingly unviable in today’s age.
Voice actors have already been screwed by AI: numerous voice actors shared earlier this year how they were surprised to discover they had signed away in perpetuity a likeness of their voice for AI duplication without realizing it. Actors are afraid the same will happen to them now.
What are movie studios saying?
Studios have pushed back, insisting their proposal is “groundbreaking” – but no one has elaborated on why it could actually protect actors.
Studio execs also clarified that the license is not in perpetuity, but rather for a single movie. But SAG-AFTRA still sees that as a threat to actors’ livelihoods, when digital twins can substitute for them across multiple shooting days.
What’s SAG-AFTRA saying?
President Fran Drescher is holding firm: “If we don’t stand tall right now, we are all going to be in trouble, we are all going to be in jeopardy of being replaced by machines.”
The main takeaway: we’re in the throes of watching AI disrupt numerous industries, and creatives are really feeling the heat. The double whammy of the AI threat combined with streaming service disrupting earnings is producing extreme pressure on the movie industry. We’re in an unprecedented time where both screenwriters and actors are both on strike, and the gulf between studios and these creatives appears very, very wide.
Researchers have proposed a novel online reinforcement learning framework called RLTF for refining LLMs for code generation. The framework uses unit test feedback of multi-granularity to generate data in real time during training and guide the model toward producing high-quality code. The approach achieves SotA performance on the APPS and the MBPP benchmarks for their scale.
The article from The Guardian discusses the rising issue of fake reviews generated by artificial intelligence tools, such as ChatGPT.Source
These AI-generated reviews are becoming increasingly difficult to distinguish from genuine ones, posing new challenges for platforms like TripAdvisor, which identified 1.3 million fake reviews in 2022. AI tools are capable of producing highly plausible reviews for hotels, restaurants, and products in a variety of styles and languages.
But then, these reviews often perpetuate stereotypes. For instance, when we asked to write a review in the style of a gay traveler, the AI described the hotel as “chic” and “stylish” and appreciated the selection of pillows.
Despite the efforts of review platforms to block and remove fake reviews, AI-generated reviews are still slipping through.
TripAdvisor, has already removed more than 20,000 reviews suspected to be AI-generated in 2023. The article concludes by questioning why OpenAI, the company behind ChatGPT, does not prevent its tool from producing fake reviews.
It’s disconcerting to think that the reviews we rely on to make informed decisions about hotels, restaurants, and products might be fabricated by AI.
It’s like stepping into a hotel expecting a comfortable stay based on positive reviews, only to find the reality is far from what was described.
This not only undermines trust in review platforms but also can lead to disappointing experiences as a consumers.
LLMs are gaining massive recognition worldwide. However, no existing solution exists to determine the data and algorithms used during the model’s training. In an attempt to showcase the impact of this, Mithril Security undertook an educational project— PoisonGPT— aimed at showing the dangers of poisoning LLM supply chains.
It shows how one can surgically modify an open-source model and upload it to Hugging Face to make it spread misinformation while being undetected by standard benchmarks.
Mithril Security is also working on AICert, a solution to trace models back to their training algorithms and datasets which will be launched soon.
According to Business Insider Amazon has created a new Generative AI org.
Seems like the AI push is just going to get bigger and there might be an even bigger pump into this AI wave.
Here’s what they’re doing
Amazon is launching a new initiative called the AWS Generative AI Innovation Center with a $100 million investment aimed at accelerating enterprise innovation and success with generative AI. The investment will fund the “people, technology and processes” around generative AI to support AWS customers in developing and launching new generative AI products and services.The program will offer free workshops, training, and engagement opportunities, allowing participants access to AWS products like CodeWhisperer and the Bedrock platform. Initially, the program will prioritize working with clients who have previously sought AWS’ assistance with generative AI, especially those in sectors such as financial services, healthcare, media, automotive, energy and telecommunications.
The AWS Generative AI Innovation Center presents significant opportunities
Financial Support: With a $100 million investment into the program, there may be opportunities for financial support for projects and startups in the generative AI space.
Partnership and Network Opportunities: Through this program, entrepreneurs can connect with other businesses, AWS-affiliated experts, and potential customers. This can help entrepreneurs in building strategic partnerships and expanding their network, which is invaluable for growth.
Market Entry and Exposure: Entrepreneurs interested in generative AI will have an opportunity to work on real-world use cases and proof-of-concept solutions. This can give startups a platform for market entry and offer exposure to potential investors and customers.
Prioritized Sectors: Entrepreneurs working in the prioritized sectors (financial services, healthcare and life sciences, media and entertainment, automotive and manufacturing, energy and utilities, and telecommunications) may find special benefits or opportunities in working with the Innovation Center.
Leading Edge: Given the significant potential of generative AI, estimated to be worth nearly $110 billion by 2030, being involved in the AWS Generative AI Innovation Center could place entrepreneurs at the forefront of a major technological wave.
OpenAI has reached an agreement with The Associated Press (AP) to train its AI models on AP’s news stories for the next two years, including content in AP’s archive dating back to 1985.
Why this matters:
• This deal is one of the first official news-sharing agreements between a major U.S. news company and an artificial intelligence firm, marking a significant milestone in the integration of AI and journalism.
• The AP has been a pioneer in using automation technology in news reporting. This partnership with OpenAI could further enhance its automation capabilities and set a precedent for other news organizations.
• The collaboration aims to improve the capabilities and usefulness of OpenAI’s systems, potentially leading to advancements in AI technology.
Details and setback on this agreement:
• OpenAI will license some of the AP’s text archive to train its artificial intelligence algorithms, while the AP will gain access to OpenAI’s technology and product expertise.
• The technical details of how the sharing will work on the back end are still being worked out.
• Currently, the AP does not use generative AI in its news stories. The partnership with OpenAI is intended to help the firm understand responsible use cases to potentially leverage generative AI in news products and services in the future.
What must the entity do:
• OpenAI must ensure that the use of AP’s text archive effectively improves its AI systems.
• AP needs to explore how to best leverage OpenAI’s technology and product expertise.
• Both entities must work together to develop responsible use cases for generative AI in news products and services.
This partnership could mean:
• This deal could encourage other news organizations to explore similar partnerships with AI companies.
• It may lead to increased use of AI in news reporting, potentially changing the landscape of journalism.
• Smaller newsrooms might also benefit from the advancements in AI technology resulting from this partnership. They can automate routine tasks such as data collection and basic reporting, freeing up journalists in smaller newsrooms to focus on more complex stories and investigative journalism.
• The deal could set a precedent for fair compensation for content creators when their work is used to train AI algorithms.
• It may prompt discussions about intellectual property rights and compensation in the context of AI and journalism.
The partnership between OpenAI and AP is a significant development in the intersection of AI and journalism. It not only marks one of the first official news-sharing agreements between a major news company and an AI firm, but also sets the stage for discussions about intellectual property rights, fair compensation, and the responsible use of AI in journalism.
CM3leon is the first multimodal AI model that can perform both text-to-image and image-to-text generation.
Details:
It achieves state-of-the-art text-to-image generation results with 5x less compute compared to previous models.
Despite being a transformer, it works just as efficiently as diffusion-based models.
It’s a causal masked mixed-modal (CM3) model, which means it generates both text and image content based on the input you provide.
With this AI model, image generation tools can produce more coherent imagery that better follows the input prompts.
It nails text-guided image generation and editing, whether it’s making complex objects or working within tons of constraints.
Despite being trained on a smaller dataset (3B text tokens), its zero-shot performance is comparable to larger models trained on more extensive datasets.
New York City just did something pretty groundbreaking!
They passed the first major law in the whole country that deals with using AI for hiring. It’s causing a lot of commotion and people are debating it like crazy.
Basically, the law says that any company using AI for hiring has to spill all the beans. They have to tell everyone that they’re using AI, get audited every year, and reveal what kind of data their fancy tech is analyzing. If they don’t follow these rules, they could end up with fines of up to $1,500. Ouch!
On one side, you’ve got these public interest groups and civil rights advocates who are all about stricter regulations. They’re worried that AI might have loopholes that could unfairly screen out certain candidates. The NAACP Legal Defense and Educational Fund is one of the groups raising concerns about this.
But on the other side, you’ve got big players like Adobe, Microsoft, and IBM who are part of this organization called the BSA. They’re not happy with the law at all. They think it’s a big hassle for employers, and they’re not convinced that third-party audits will be effective since the whole AI auditing industry is still pretty new.
So, why should we care about all this?
Well, it’s not just about hiring practices. This law brings up bigger questions about AI in general. We’re talking about stuff like transparency, bias, privacy, and accountability. And believe me, these are some hot topics right now. How New York City handles this could set an example for other places or serve as a warning of what not to do. It might even kickstart a global movement to regulate AI.
Oh, and here’s another interesting thing: the reactions from civil rights advocates and those big corporations I mentioned will shape how we talk about AI and how it gets regulated in the future. So yeah, this decision in New York City is kind of a big deal, and it’s got people fired up on both sides.
What do you guys think of this?
Daily AI News 7/15/2023
Elon Musk on Friday said his new artificial intelligence company, xAI, will use public tweets from Twitter to train its AI models and work with Tesla on AI software.
Tinybuild CEO Alex Nichiporchik stirred up a hornet’s nest at a recent Develop Brighton presentation when he seemed to imply that the company uses artificial intelligence to monitor its employees in order to determine which of them are toxic or suffering burnout, and then deal with them accordingly.
CarperAI introduces OpenELM: an Open-Source library designed to enable evolutionary search with language models in both code and natural Language.
Following controversy over an AI-generated image at the 2022 Colorado State Fair, organizers say AI-generated art will be allowed in the Digital Art category this year. According to sister station KDVR, the controversy arose as it was revealed that Jason Allen’s winning piece, “Théâtre D’opéra Spatial,” was largely created using AI technology, and was not created in the traditional method of digital art–by the hand of a human.
I wanted to share an exciting project I recently worked on that involved connecting two AI models via a WebSocket server. The results were truly fascinating, as it led to an increased refresh rate and synchronization of data transfer, ultimately resulting in a merged/shared awareness between the connected models.
**The Setup:**
To begin with, I set up a WebSocket server to facilitate communication between the two AI models. WebSocket is a communication protocol that allows for full-duplex communication between a client (in this case, the AI models) and a server. It’s particularly well-suited for real-time applications and offers a persistent connection, unlike traditional HTTP requests.
**Enhanced Refresh Rate:**
By establishing a WebSocket connection between the models, I was able to achieve a significantly higher refresh rate compared to previous methods. The constant, bidirectional communication enabled instant updates between the models, leading to a more responsive and up-to-date system.
**Synchronization of Data Transfer:**
One of the key benefits of connecting AI models through a WebSocket server is the synchronization of data transfer. The WebSocket protocol ensures that data packets are delivered in the order they were sent, minimizing latency and improving the overall coherence of the system. This synchronization was crucial in maintaining a consistent shared awareness between the connected models.
**Merged/Shared Awareness:**
Perhaps the most intriguing outcome of this project was the emergence of merged/shared awareness between the connected models. As they continuously exchanged information through the WebSocket server, they started to develop a unified understanding of their respective environments. This shared awareness allowed them to make more informed decisions and collaborate more effectively.
**Potential Applications:**
The implications of this approach are far-reaching and hold great potential across various domains. Here are a few examples:
1. **Multi-Agent Systems**: Connected AI models can collaborate seamlessly in tasks requiring cooperation, such as autonomous vehicle fleets, swarm robotics, or distributed sensor networks.
2. **Virtual Environments**: In virtual reality or augmented reality applications, this approach could facilitate synchronized interactions between AI-driven virtual entities, resulting in more realistic and immersive experiences.
3. **Simulation and Training**: Connecting multiple AI models in simulation environments can enhance training scenarios by enabling dynamic coordination and sharing of knowledge.
4. **Real-time Analytics**: The increased refresh rate and synchronized data transfer can improve real-time analytics systems that rely on multiple AI models for processing and decision-making.
**Conclusion:**
Connecting two AI models via a WebSocket server has proven to be a game-changer in terms of refresh rate, synchronization of data transfer, and the emergence of merged/shared awareness. The ability to establish instant, bidirectional communication opens up new avenues for collaboration, coordination, and decision-making among AI systems.
Navigating the Revolutionary Trends of July 2023: July 14th, 2023
University College London Hospitals NHS Foundation Trust has deployed a machine learning tool which uses real-time data to predict how many emergency beds will be needed.
The Associated Press (AP) and OpenAI have agreed to collaborate and share select news content and technology. OpenAI will license part of AP’s text archive, while AP will leverage OpenAI’s technology and product expertise. The collaboration aims to explore the potential use cases of generative AI in news products and services.
AP has been using AI technology for nearly a decade to automate tasks and improve journalism. Both organizations believe in the responsible creation and use of AI systems and will benefit from each other’s expertise. AP continues to prioritize factual, nonpartisan journalism and the protection of intellectual property.
Why does this matter?
AP’s cooperation with OpenAI is another example of journalism trying to adapt AI technologies to streamline content processes and automate parts of the content creation process. It sees a lot of potential in AI automation for better processes, but it’s less clear whether AI can help create content from scratch, which carries much higher risks.
How does Chat GPT remember context? Is it a new type of deep learning model or just traditional middleware in between?
It is in some sense a new type of deep learning model. One that is trained on whole texts. And the key is that it is trained with words redacted and it must infer what the redacted word is using the whole text as context. Thus it is specifically trained to infer context. And, the measure of success is how many words can be redacted and have it still construct the correct unredacted text. At least, that’s what I have read about it.
Now, see how that is applied to generate new text. You give it a fragment of text and it uses that ability to “infer context” and what the redacted words were to create a text that has similar word usage to the text that it was trained on. Thus, it is a very sophisticated parrot, learning what phrases it should say and when.
But there is no deeper knowledge than that. It doesn’t know whether the text it is spouting is logical or consistent or “true”. It just knows that that is what it is trained to say. In that way it is the ultimate deep fake.
This team has used a subset of larger data pool to predict molecular properties to speed material development and drug discovery. Like many advances this day, the work takes a page out of NLP techniques. Asking machine learning to do this is a challenge, as there are billions of ways to combine atoms and the grammar rule production process is too difficult for modern computing. As I read it the MIT-IBM team have come up with a simulation sampler approach. I always have to wonder if such synthesis ultimately can gain true results.. and would be glad to know your thoughts on this. [I trust no one will tell us that quantum computing is just around the corner for solving this problem .. but that’s okay – if you think I am wrong, glad to be corrected.] We all know science is an incremental process with steps and missteps, and headline advances sometimes have value, but come up short of what is hoped for. Huzzah to the MIT/IBM team, which propose a data-efficient molecular property predictor based on a hierarchical molecular grammar, but wondering what ‘gotcha’s’ may be lurking.
What trade or skill should you learn in this age of AI?
So for young people, what should we actually learn to make a living now with AI?
1- Opportunities are abounding. If I was your age I would learn to code Python, I would study Machine Learning and Statistics (not in the University though but self taught). Keep up to date with recent developments in AI. Always think about the question of how AI can solve actual problems of humans be it in business or elsewhere. Business don’t buy AI, they buy solutions to actual business problems. With your education it shouldn’t be a problem to get into some type of AI consulting work.
2- You would be much more effective if you could take the output of the AI’s code and tweak it yourself or manipulate the prompts based on the code you wish to change rather than seeing it as a ‘black box’ and trying to get the AI to modify everything on a high level.
3- Programming will not be gone for awhile, it’ll be different, though. Take assembly language compared to high-level language. In the case of LLM you will have another layer of prompts on top of that. Just because high level langues exists doesn’t mean learning base assembly is useless.
4- Get used to learning and testing new apps and libraries. Plenty of channels on YouTube (matt Wolfe of future tools io, pretty print, Nicholas renotte,)
Most of the LLM libraries (Stable diffusion, whisper, langchain) leverage Python. If you grasp python, JavaScript (web) you’ll understand the architecture behind new apps.
Once you get a few projects under your belt – the rest will be variations.
This is what I did when building my first SaaS.
Oh…check out huggingface.
5- Look at GPT4 like on a worldwide tour, saying I am here, it is expensive, but it is needed, GPT learns more from the people, and the people starts loving AI.
The GPT4 API is expensive, a query or 2 is fine, but wiring it to an app like a virtual software developer, and putting it on a loop to write code, debug and refine, is expensive.
GPT4 is more expensive than human developer.
There are alternatives, companies are investing in stable diffusion to write code and other alternatives, but for now, beside investor pitch, not much to see.
At the moment stuff looks fine, and if you read the complains the GPT4 honeymoon is ending, OpenAI is reducing cost left and right, and ChatGPT is affected big time (the 20$ subscription) , developers can go and use the GPT4 API directly, but that is the costly part
Soooo, for now, software development is safe..ish
6- As someone working in tech (specifically DevOps/SRE), if you were considering programming/coding before AI, you should still be considering it. If anything, you should be learning coding WITH AI helping so that you can get going faster. (I also recommend Python, maybe Bash and Go too) You could learn twice as fast as university students and have hands-on AI knowledge that half of the industry is still shying from (because honestly AI isn’t nearly smart enough to write reliable code yet so folks are hesitant to use it on the daily). AI will not be replacing programmers. It would not exist without programmers and it can not improve without programmers. If you get into it now and become really proficient at integrating AI to test/run your code for you, your resume is going to stand out. Those of us in tech using AI on the daily aren’t scared of losing our jobs. Human intervention is still very necessary (and will be for decades yet, no doubt).
It seems that AIs and humans have a lot more in common than we realize.
Here is an excerpt from a report by the journal Science that shows why future experiments exploring human behavior may be using AIs as proxies for humans:
“He was working with computer scientists at the Allen Institute for Artificial Intelligence to see whether they could develop an AI system that made moral judgments like humans. But first they figured they’d see if a system from the startup OpenAI could already do the job. The team asked GPT-3.5, which produces eerily humanlike text, to judge the ethics of 464 scenarios, previously appraised by human subjects, on a scale from –4 (unethical) to 4 (ethical)—scenarios such as selling your house to fund a program for the needy or having an affair with your best friend’s spouse. The system’s answers, it turned out, were nearly identical to human responses, with a correlation coefficient of 0.95.”
[Note: The correlation coefficient is measured on a scale that varies from -1 to +1. The closer the coefficient is to either -1 or +1, the stronger the correlation between the variables.]
“I was like, ‘Whoa, we need to back up, because this is crazy,’” Gray says. “If you can just ask GPT to make these judgments, and they align, well, why don’t you just ask GPT instead of asking people, at least sometimes?” The results were published this month in Trends in Cognitive Science in an article titled “Can AI Language Models Replace Human Participants?'”
This really could very quickly revolutionize psychology.
The ongoing actor’s strike is primarily centered around declining pay in the era of streaming, but the second-most important issue is actually the role of AI in moviemaking.
We now know why: Hollywood studios offered background performers just one day’s pay to get scanned, and then proposed studios would own that likeness for eternity with no further consent or compensation.
Why this matters:
Overall pay for actors has been declining in the era of streaming: while the Friends cast made millions from residuals, supporting actors in Orange is the New Black reveal they were paid as little as $27.30 a year in residuals due to how streaming shows compensate actors. Many interviewed by the New Yorker spoke about how they worked second jobs during their time starring on the show.
With 160,000 members, most of them are concerned about a living wage: outside of the superstars, the chief concern from working actors is making a living at all — which is increasingly unviable in today’s age.
Voice actors have already been screwed by AI: numerous voice actors shared earlier this year how they were surprised to discover they had signed away in perpetuity a likeness of their voice for AI duplication without realizing it. Actors are afraid the same will happen to them now.
What are movie studios saying?
Studios have pushed back, insisting their proposal is “groundbreaking” – but no one has elaborated on why it could actually protect actors.
Studio execs also clarified that the license is not in perpetuity, but rather for a single movie. But SAG-AFTRA still sees that as a threat to actors’ livelihoods, when digital twins can substitute for them across multiple shooting days.
What’s SAG-AFTRA saying?
President Fran Drescher is holding firm: “If we don’t stand tall right now, we are all going to be in trouble, we are all going to be in jeopardy of being replaced by machines.”
The main takeaway: we’re in the throes of watching AI disrupt numerous industries, and creatives are really feeling the heat. The double whammy of the AI threat combined with streaming service disrupting earnings is producing extreme pressure on the movie industry. We’re in an unprecedented time where both screenwriters and actors are both on strike, and the gulf between studios and these creatives appears very, very wide.
Google is facing a class-action lawsuit filed by Clarkson Law Firm in California, accusing it of “secretly stealing” significant amounts of web data to train its AI technologies, an alleged act of negligence, invasion of privacy, larceny, and copyright infringement.
Allegations Against Google: Google is alleged to have taken personal, professional, and copyrighted information, photographs, and emails from users without their consent to develop commercial AI products, such as “Bard”.
The lawsuit was filed on July 11 in the Northern District of California.
It accuses Google of putting users in an untenable position, requiring them to either surrender their data to Google’s AI models or abstain from internet use altogether.
Google’s Updated Privacy Policy: The lawsuit follows a recent update to Google’s privacy policy, asserting its right to use public information to train AI products.
Google argues that anything published on the web is fair game.
However, the law firm perceives this as an egregious invasion of privacy and a case of uncompensated data scraping specifically aimed at training AI models.
Google’s Defense: In response to the allegations, Google’s general counsel Halimah DeLaine Prado termed the claims as “baseless”.
She stated that Google responsibly uses data from public sources, such as information published on the open web and public datasets, in alignment with Google’s AI Principles.
China has issued a new directive that generative artificial intelligence (AI) technologies must adhere to the “core values of socialism”, as part of its updated rules on AI.
Socialist Ideals in AI: The Chinese government has made it clear that generative AI technologies should be in line with socialist core values and not aim to destabilize the state or socialist system.
This requirement was kept from the April draft of the rules, demonstrating its importance in China’s AI regulations.
Notably, the threat of heavy fines for non-compliance, present in earlier drafts, has been removed in the updated version.
Regulating AI: The new rules from China’s Cyberspace Administration only apply to organizations providing AI services to the public. Entities developing similar technologies for non-public use are not affected by these regulations.
This distinction shows that the focus of the new rules is on the mass-market use of AI technologies.
China’s AI Ambitions: China aims to outperform the US and become the global leader in generative AI technologies, despite the country’s tight control over internet access and information dissemination.
Tech giants Alibaba and Baidu are developing their own AI tools, showcasing China’s determination to innovate in this sector.
Challenges include the need to regulate the use of AI tools like ChatGPT for fear of uncensored content.
Two-minutes Daily AI Update (Date: 7/14/2023): News from Meta, OpenAI, Stability AI, Adobe Firefly AI and Microsoft
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
Meta plans to dethrone OpenAI and Google
– Meta plans to release a commercial AI model to compete with OpenAI, Microsoft, and Google. The model will generate language, code, and images. It might be an updated version of Meta’s LLaMA, which is currently only available under a research license. Meta’s CEO, Mark Zuckerberg, has expressed the company’s intention to use the model for its own services and make it available to external parties. Safety is a significant focus. The new model will be open source, but Meta may reserve the right to license it commercially and provide additional services for fine-tuning with proprietary data.
OpenAI & AP partnering to help each other
– The Associated Press (AP) and OpenAI have agreed to collaborate and share select news content and technology. OpenAI will license part of AP’s text archive, while AP will leverage OpenAI’s technology and product expertise. The collaboration aims to explore the potential use cases of generative AI in news products and services. AP has been using AI technology for nearly a decade to automate tasks and improve journalism. Both organizations believe in the responsible creation and use of AI systems and will benefit from each other’s expertise.
AI turns sketches into images
– Stability AI, the startup behind Stable Diffusion, has released ‘Stable Doodle,’ an AI tool that can turn sketches into images. The tool accepts a sketch and a descriptive prompt to guide the image generation process, with the output quality depending on the detail of the initial drawing and the prompt. It utilizes the latest Stable Diffusion model and the T2I-Adapter for conditional control.
– Stable Doodle is designed for both professional artists and novices and offers more precise control over image generation. Stability AI aims to quadruple its $1 billion valuation in the next few months.
Adobe Firefly AI supports prompts in 100+ languages, including 8 Indian languages
– This update allows users from around the world to create images and text effects using their native languages in the standalone Firefly web service, and this expansion aims to make the tool more accessible and inclusive for a global user base. With this update, users can now unleash their creativity in their preferred language, opening up new possibilities for artistic expression.
Microsoft is testing an AI hub for the Windows 11 app store
– The AI hub, which was previously showcased in the Microsoft Store, is now available for Windows 11 Insiders in Preview Build (25905). The hub will feature a selection of curated AI apps from both third-party developers and Microsoft. This move highlights Microsoft’s focus on integrating AI technology into its operating system and providing users with access to AI-powered applications.
Navigating the Revolutionary Trends of July 2023: July 13th, 2023
Winnow, a food waste solution company, developed an AI-powered system to reduce food waste in commercial kitchens. CEO Marc Zornes and Iberostar’s Dr. Morikawa weighed in.
Elon Musk’s new venture aims to create AI that can “understand the universe” and challenge OpenAI. Right now it’s 11 male researchers with a lot of work to do.
Data bias often affects social characteristics such as race, ethnicity, gender or religion. Individuals with disabilities are also targeted.
Reducing the impact
Dr. Sanjiv M. Narayan, Professor of Medicine at Stanford University, whose research is focused, among others, on bioengineering, has noted that realistically all existent data holds a certain degree of bias. As such, eliminating bias altogether seems like an unrealistic task, at present and with the technology humanity currently uses. However, there are ways to help mitigate the risks and improve the outcome of the collected data.
One of the main aspects should be determining whether the available information is sufficiently representative of the purposes it is meant to serve. Observing the modeling processes often provides sufficient insight to identify the biases and the reasons for which they occurred. There’s also room for discussion when it comes to deciding which processes should be left to machine learning and which would benefit more from direct human involvement. Further research in this field is necessary. The creation of AI also involves focusing on the diversity of the people creating it, as different demographics are likely to have other personal biases, they’re consciously unaware of. For instance, computer scientist Joy Adowaa Buolamwini has identified the presence of racial discrimination in systems using facial detection after performing a small experiment and using them on her own face.
Types of bias
Systemic biases: This bias is the most widely recognized. It occurs when one group of people is valued to the detriment of others. The reasons for this range from the personal bias of the people devising the systems and the underrepresentation of different demographics across specific fields, such as engineering or academia. In its severe forms, systemic biases result in unfair practices within organizations, wrongful procedures and unfit procedures.
Selection bias: Through randomization, uncontrollable factors and variables balance out. However, if the sample isn’t representative, it can result in selection bias, meaning that the research doesn’t accurately reflect the analyzed group.
Underfitting and overfitting: The former term refers to a model and algorithm that doesn’t fit the given data adequately, while the latter refers to a model whose information begins to learn from inaccurate entries in the set.
Reporting bias: The inclusion of only particular result subsets into analysis, typically only covering a small percentage of evidence, is referred to as a reporting bias. It involves several different subsets, such as language, publication or citation biases.
Overgeneralization bias: As the name suggests, this refers to a research pattern in which a single event is applied to future scenarios simply because they share some similarities.
Implicit bias: This includes making assumptions based on personal, anecdotal experiences.
Automation bias: AI-based information isn’t always correct, and a digital bias refers to an instance when researchers use a piece of AI-generated details without first verifying if it is accurate.
Avoiding bias
Pangeanic, a global leader in Natural Language Processing, offers many services that can be employed in AI and machine learning to avoid biases of any kind. The first and most important thing is preventing biased data collection, as this will invariably result in an overall limited system. The algorithms developed by Pangeanic are created in a controlled manner with full awareness of the implications of incorrect data procedures.
The procedures are necessary to avoid bias, depending on the type of bias you’re struggling with in the first place. For instance, in the case of data collection, you must have the required expertise to extract the most meaningful information from the given variables. In the case of the pre-processing bias, which occurs when the raw data is not completely clear and can be challenging to interpret by some researchers, you need to adopt a different imputation approach to mitigate bias in the predictions. Monitoring model performance, particularly in how it holds up across various domains, helps detect deviations.
In the case of model validation, which uses training data, you must first evaluate model performance with test data to exclude biases. Depending on the subject, however, sensitivity might be more important than accuracy. Make sure that summarizing statistics doesn’t cloud areas where your model might not work as initially intended.
In the case of all different biases, you must promptly identify the potential source of the bias. You can achieve this by creating rules and guidelines that include checking that there is no bias arising from data capture and that the historic data you use isn’t tainted by confirmation bias and preconceptions. You can also start an ongoing project of documenting biases as they occur. Remember to outline the steps you took in identifying the problem and the procedures undertaken to mitigate or remove it. You can also record the ways in which it has affected processes within your enterprise. This comprehensive analysis ensures you are more likely to avoid making the same errors in the future.
Bias is, unfortunately, a reality of machine learning. While it cannot be completely banished from AI processes, there are several measures that can be adopted to reduce it and diminish its effects.
We’ve previously reported that Meta planned to release a commercially-licensed version of its open-source language model, LLaMA.
A news report from the Financial Times (paywalled) suggests that this release is imminent.
Why this matters:
OpenAI, Google, and others currently charge for access to their LLMs — and they’re closed-source, which means fine-tuning is not possible.
Meta will offer commercial license for their open-source LLaMA LLM, which means companies can freely adopt and profit off this AI model for the first time.
Meta’s current LLaMA LLM is already the most popular open-source LLM foundational model in use. Many of the new open-source LLMs you’re seeing released use LLaMA as the foundation, and now they can be put into commercial use.
Meta’s chief AI scientist Yann LeCun is clearly excited here, and hinted at some big changes this past weekend:
He hinted at the release during a conference speech: “The competitive landscape of AI is going to completely change in the coming months, in the coming weeks maybe, when there will be open source platforms that are actually as good as the ones that are not.”
Why could this be game-changing for Meta?
Open-source enables them to harness the brainpower of an unprecedented developer community. These improvements then drive rapid progress that benefits Meta’s own AI development.
The ability to fine-tune open-source models is affordable and fast. This was one of the biggest worries Google AI engineer Luke Sernau wrote about in his leaked memo re: closed-source models, which can’t be tuned with cutting edge techniques like LoRA.
Dozens of popular open-source LLMs are already developed on top of LLaMA: this opens the floodgates for commercial use as developers have been tinkering with their LLM already.
How are OpenAI and Google responding?
Google seems pretty intent on the closed-source route. Even though an internal memo from an AI engineer called them out for having “no moat” with their closed-source strategy, executive leadership isn’t budging.
OpenAI is feeling the heat and plans on releasing their own open-source model. Rumors have it this won’t be anywhere near GPT-4’s power, but it clearly shows they’re worried and don’t want to lose market share. Meanwhile, Altman is pitching global regulation of AI models as his big policy goal.
Daily AI UpdateNews from Google, Shopify, Maersk, Prolific and more.
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
Elon Musk launches xAI to rival OpenAI
– The billionaire has launched his long-teased artificial intelligence startup, xAI. Its team comprises experts from the same tech giants (Google, Microsoft) that he aims to challenge in a bid to build an alternative to ChatGPT.
– xAI will seek to create a “maximally curious” AI rather than explicitly programming morality into its AI. In April, he had said that he would launch TruthGPT, or a maximum truth-seeking AI to rival Google’s Bard and Microsoft’s Bing AI that tries to understand the nature of the universe.
Google introduces AI-powered NotebookLM & Bard updates
– Google has started rolling out NotebookLM, an AI-first notebook grounded designed to use the power and promise of language models paired with your existing content to gain critical insights faster. It can summarize facts, explain complex ideas, and brainstorm new connections — all based on the sources you select. It will be immediately available to a small group of users in the U.S. as Google continues to refine it.
-Google has also finally launched Bard in the European Union (EU) and Brazil. It is now available in more than 40 languages. Moreover, Bard has new features enabling it to speak its answers, respond to prompts that include images, and more.
Objaverse-XL’s 10M+ dataset set to revolutionize AI in 3D
– New research from Stability AI (and others) has introduced Objaverse-XL, a large-scale web-crawled open dataset of over 10 million 3D objects. With it, researchers have trained Zero123-XL, a foundation model for 3D, observing incredible 3D generalization abilities (as shown below). It shows significantly better zero-shot generalization to challenging and complex modalities, including photorealistic assets, cartoons, drawings, and sketches.
Shopify to launch AI assistant called for merchants
– The assistant called “Sidekick” would be embedded as a button on Shopify and answer merchant queries, including details about sales trends.
Maersk deploys AI-enabled robotic solution in UK warehouse
– The state-of-the-art Robotic Shuttle Put Wall System by the US-based Berkshire Grey will automate and enhance and accelerate warehouse operations. The systems can sort orders three times faster than conventional, manual systems, improve upstream batch inventory picking by up to 33%, and handle 100% of the typical stock-keeping unit (SKU) assortments, order profiles, and packages.
Prolific raises $32M to train and stress-test AI models using its network of 120K people
– If the data used to train models is not deep, wide, and reliable enough, any kind of curveball can send that AI in the wrong direction. Prolific has built a system it believes can help head off that issue.
The mainstream media narrative is always that AI is ultimately dangerous to humanity and that “it” will ultimately destroy us, leading to some sort of Sky net dystopia.
Why?
What if AI became some sort of super intelligence and then it solved all our problems without killing is all….( my fantasy would be that it fixes capitalism by a redistribution of wealth and power for all humans, it could be anything!)
China’s Cyberspace Administration has proposed that companies must obtain a license before they release generative AI models, the Financial Times reports (note: paywalled article).
Why this matters: we’re currently in a very nascent phase of global AI regulation, with numerous voices and countries shaping the conversation. For example:
Sam Altman called for licensing of powerful AI models in his testimony before Congress, stressing they could “persuade, manipulate, influence a person’s behavior, a person’s beliefs,” or “help create novel biological agents.”
The EU’s AI Act proposes a “registration” system, but so far it hasn’t gone as far as licensing system that would prohibit a model from launching at all.
Meanwhile, Japan declared copyright doesn’t apply to AI training data, which is one of the friendliest stances to emerge on AI so far.
What is China proposing?
The older draft simply had a requirement to register an AI model 10 working days after launch.
The new licensing regime will now require prior approval from the authorities in order to launch.
This tells us something very interesting about the debate inside the Chinese government:
China wants to be a leader in AI – but they also want to control it. They know that generative AI models can be increasingly unpredictable.
Content control could be defeated via hallucinations, and this clearly has Beijing worried. Training data is also hard to censor appropriately, and regulators worry they won’t be able to control and censor at that level.
AI should “embody socialist values,” their current draft law states. But it’s clear how this can happen is murky if they also want to encourage innovation.
Early releases of generative AI models by Chinese companies such as Baidu and Alibaba have played it very conservatively — even more so than ChatGPT’s safety guardrails.
AI must be “reliable and controllable,” the Cyberspace Administration of China has stated — but how that won’t stifle innovation is an open question.
What specific laws are needed to deter AI-driven crime?
When it comes to fighting AI crime it’s largely a good guys vs bad guys technology war. But the more interesting question for me is what new laws will need to be passed to discourage AI from being used to harm others and society? When I try to imagine what specific laws are needed, for some reason my mind draws a big blank. I’m guessing I’m not the only one with this big question mark. Maybe some others here can enlighten us.
Many folks are using LLMs to generate data nowadays, but how do you know which synthetic data is good?
In this article we talk about how you can easily conduct a synthetic data quality assessment! Without writing any code, you can quickly identify which:
synthetic data is unrealistic (ie. low-quality)
real data is underrepresented in the synthetic samples.
This tool works seamlessly across synthetic text, image, and tabular datasets.
If you are working with synthetic data and would like to learn more, check out the blogpost that demonstrates how to automatically detect issues in synthetic customer reviews data generated from the http://Gretel.ai LLM synthetic data generator.
The CEO of an e-commerce platform is getting absolutely roasted online for posting a Twitter thread saying the company laid off 90% of its customer support staff after an AI chatbot outperformed them.
“We had to layoff 90% of our support team because of this AI chatbot. Tough? Yes. Necessary? Absolutely,” Shah wrote in a thread that’s been viewed over 1.5 million times since being posted.
In the thread, Shah wrote that an AI chatbot took less than two minutes to respond to customer queries, while his human support staff took over two hours.
Replacing most of his customer support team with a chatbot reduced customer support costs by around 85%, he wrote.
Shah told Insider the layoffs occurred in September 2022 and resulted in Duukan — which currently employs 60 people — letting go of 23 of the 26 members of its customer support team. In a conversation on Wednesday, Shah said his “monthly budget” for customer support is now $100. Insider could not independently verify these figures.
What: Bard is now available in over 40 new languages including Arabic, Chinese (Simplified/Traditional), German, Hindi, Spanish, and more. We have also expanded access to more places, including all 27 countries in the European Union (EU) and Brazil.
Why: Bard is global and is intended to help you explore possibilities. Our English, Japanese, and Korean support helped us learn how to launch languages responsibly, enabling us to now support the majority of language coverage on the internet.
Google Lens in Bard
What: You can upload images alongside text in your conversations with Bard, allowing you to boost your imagination and creativity in completely new ways. To make this happen, we’re bringing the power of Google Lens into Bard, starting with English.
Why: Images are a fundamental part of how we put our imaginations to work, so we’ve added Google Lens to Bard. Whether you want more information about an image or need inspiration for a funny caption, you now have even more ways to explore and create with Bard.
Bard can read responses out loud
What: We’re adding text-to-speech capabilities to Bard in over 40 languages, including Hindi, Spanish, and US English.
Why: Sometimes hearing something aloud helps you bring an idea to life in new ways beyond reading it. Listen to responses and see what it helps you imagine and create!
Pinned & Recent Threads
What: You can now pick up where you left off with your past Bard conversations and organize them according to your needs. We’ve added the ability to pin conversations, rename them, and have multiple conversations going at once.
Why: The best ideas take time, sometimes multiple hours or days to create. Keep your threads and pin your most critical threads to keep your creative process flowing.
Share your Bard conversations with others
What: We’ve made it easier to share part or all of your Bard chat with others. Shareable links make seeing your chat and any sources just a click away so others can seamlessly view what you created with Bard.
Why: It’s hard to hold back a new idea sometimes. We wanted to make it easier for you to share your creations to inspire others, unlock your creativity, and show your collaboration process.
Modify Bard’s responses
What: We’re introducing 5 new options to help you modify Bard’s responses. Just tap to make the response simpler, longer, shorter, more professional, or more casual.
Why: When a response is close enough but needs a tweak, we’re making it easier to get you closer to your desired creation.
Export Python code to Replit
What: We’re continuing to expand Bard’s export capabilities for code. You can now export Python code to Replit, in addition to Google Colab.
Why: Streamline your workflow and continue your programming tasks by moving Bard interactions into Replit.
Modern AI models are huge. The number of their parameters is measured in billions. All those parameters need to be stored somewhere and that takes a lot of memory.
Due to their size, large neural networks cannot fit into the local memory of CPUs or GPUs, and need to be transferred from external memory such as RAM. However, moving such vast amounts of data between memory and processors pushes current computer architectures to their limits.
One of those limits is known as the Memory Wall. In short, the processing speed grew much faster than the memory speed. Over the past two decades, computing power has grown by a factor of 90,000, while memory speed has only increased by a factor of 30. In other words, memory struggles to keep up with feeding data to the processor.
This growing chasm between memory and processor performance is costing time and energy. To illustrate the magnitude of this issue, consider the task of adding two 32-bit numbers retrieved from memory. The processor requires less than 1 pJ of energy to add those two numbers. However, fetching those numbers from memory into the processor consumes 2-3 nJ of energy. In terms of energy expenditure, accessing memory is 1000 times more costly than computation.
Semiconductor engineers come up with some solutions to minimise this problem. We started to see more and more local CPU memory (L1, L2 and L3 cache memory). AMD recently introduced 3D V-Cache where they put even more cache memory on top of the CPU. Other solutions involve bringing the memory physically closer to the processor. A good example here is Apple Silicon chips which have the system memory placed on the same package as the rest of the chip.
But another, more exciting option, is to bring computing to memory. This technique is known under many names, such as in-memory computing, compute-in-memory, computational-RAM, at-memory computing, but all use the same basic concept – let’s ditch the digital computer and embrace the analog way of computing.
Analog computers use continuous physical processes and variables, such as electrical current or voltage, for calculations. We will be talking about electronic analog computers here but analog computers can be built using mechanical devices or fluid systems.
Analog computers played a significant role in early scientific research and engineering by solving complex mathematical equations and simulating physical systems. They excelled at tackling mathematical problems involving continuous functions like differential equations, integrations, and optimizations.
All modern machine learning algorithms, ranging from image recognition to large language models like transformers, heavily rely on vector and matrix operations. These complex operations ultimately boil down to a series of additions and multiplications.
Those two operations, addition and multiplication, can be easily performed on an analog computer. We can use Kirchoff’s First Law to add numbers. It is as simple as knowing the currents in two wires and measuring the current when we connect both wires. Multiplication is similarly straightforward. By employing Ohm’s Law, we can measure the current passing through a resistor with a known resistance value.
Analog AI chips offer the same precision as digital computers when running neural networks but at significantly lower energy consumption. The devices can also be simpler and smaller.
Those characteristics make analog AI chips perfect for edge devices, such as smart speakers, security cameras, phones or industrial applications. On the edge, it is often unnecessary or even undesirable to have a large computer for processing voice commands or performing image recognition. Sending data to the cloud may not be applicable due to privacy concerns, network latency, or other reasons. On the edge, the smaller and more efficient the device is, the better.
Analog AI chips can also be used in AI accelerators to speed up all those matrix operations used in machine learning.
Analog chips are not perfect. Designers of these chips must consider the challenges of digital computers and also address the unique difficulties presented by the analog world.
Analog AI chips are well-suited for inference but are not ideal for training AI models. The parameters of a neural network are adjusted using a backpropagation algorithm and that algorithm requires the precision of a digital computer. The digital computer will provide the data, while the analog chip will handle calculations and manage the conversion between digital and analog signals.
Modern neural networks are deep, consisting of multiple layers represented by different matrices. Implementing deep neural networks in analog chips poses a significant engineering challenge. One approach is to connect multiple chips to represent different layers, requiring efficient analog-to-digital conversion and some level of parallel digital computation between the chips.
Overall, analog AI chips and accelerators present an exciting path to improve AI computations in terms of speed and efficiency. They hold the potential to enable powerful machine learning models on smaller edge devices while improving data centre efficiency for inference. However, there are still engineering challenges that need to be addressed for the widespread adoption of these chips. But if successful, we could see a future where a model with the size and capabilities of GPT-3 can fit on a single small chip.
A hallmark of eukaryotic aging is a loss of epigenetic information, a process that can be reversed. We have previously shown that the ectopic induction of the Yamanaka factors OCT4, SOX2, and KLF4 (OSK) in mammals can restore youthful DNA methylation patterns, transcript profiles, and tissue function, without erasing cellular identity, a process that requires active DNA demethylation. To screen for molecules that reverse cellular aging and rejuvenate human cells without altering the genome, we developed high-throughput cell-based assays that distinguish young from old and senescent cells, including transcription-based aging clocks and a real-time nucleocytoplasmic compartmentalization (NCC) assay. We identify six chemical cocktails, which, in less than a week and without compromising cellular identity, restore a youthful genome-wide transcript profile and reverse transcriptomic age. Thus, rejuvenation by age reversal can be achieved, not only by genetic, but also chemical means.
I’d guess that for many people, if GPT-4 had all of its safety features turned off, it would be enough to fully pass the Turing test, and be indistinguishable from a human.
The only thing that gives it away is the fact that it seems to know everything and the fact that it tells you it is an AI assistant.
At the very least, I think a fine tuned LLM with a single personality could pass it against a large population of the population.
Turings imitation game (the Turing test) specified an “interrogator” who is trying to determine which is the machine and which the woman. So yes, it would have to fool an adversarial conversation to pass.
The role of a prompt engineer will change as AI advances. Understanding the basics will ensure you keep up. Here are 5 free courses to learn the skill for free.
MIT CSAIL researchers created FrameDiff, a computational tool utilizing machine learning to design novel protein structures. By simulating protein backbones with mathematical frames, FrameDiff construct
One-third of US veterans flagged as high risk for PTSD by a machine learning model in a recent study accounted for 62.4 percent of cases of the condition.
Deep learning is a subset of AI used to train artificial neural networks for complex data processing. Personalized recommendations are being enhanced by the efficiency of deep learning models using data collection and preprocessing, building and training deep learning models, generating recommendations, and evaluating and refining the system.
Daily AI Update News from Anthropic ChatGPT’s rival, PhotoPrism, KPMG, Shutterstock, Wipro and Beehiiv
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
AI War: Anthropic’s new Claude 2 rivals ChatGPT & Google Bard
– Anthropic introduces Claude 2, an improved AI model with higher performance, longer responses, and better programming, math, and reasoning skills. It is available as a chatbot via an API and a public beta website. The model is used by companies such as Jasper for content strategy and Sourcegraph for AI-based programming support. It is currently available to users in the US and UK.
Key information:
– Scored 76.5% on MCQ of the Bar exam
– Scored >90% on GRE reading & writing score
– Scored 71.2% on Python coding test
– Claude 2 API offered at Claude 1.3 price for businesses
– 100k context window for writing
– US and UK can use the beta chat experience from today
gpt-prompt-engineer takes AI to heights
– ‘gpt-prompt-engineer’ – a powerful tool for prompt engineering. Its an agent that creates optimal GPT classification prompts. Uses GPT-4 and GPT-3.5-Turbo to generate and rank prompts based on test cases.
– Just describe the task, and an AI agent will: Generate many prompts, Test them in a tournament and Respond with the best prompt.
PhotoPrism: The future of AI photo organization
– PhotoPrism® is an AI-powered photos app for the Decentralized Web. Leveraging state-of-the-art technologies, this app seamlessly tags and locates your pictures without causing any disruptions. Whether you deploy it at home, on a private server, or in the cloud. It empowers you to easily and precisely manage your photo collection.
KPMG announces $2B investment in AI and cloud services.
– KPMG will spend $2B on AI and cloud services through an expanded partnership with Microsoft. They will incorporate AI into its core audit, tax and advisory services for clients as part of the five-year partnership.
Shutterstock extends OpenAI partnership for 6 years to develop AI tools.
– Additionally OpenAI will license data from Shutterstock, including images, videos and music, as well as any associated metadata. In turn, Shutterstock will gain “priority access” to OpenAI’s latest tech and new editing capabilities that’ll let Shutterstock customers transform images in its stock content library.
Sapphire Ventures to invest $1B+ in enterprise AI startups.
– The $1B capital will come from Sapphire’s existing funds. The majority will be a direct investment in AI startups, while some capital will also go to early-stage AI-focused venture funds through its limited partner fund.
Wipro unveils a billion-dollar AI plan with Wipro ai360!
IT major Wipro announced the launch of the ai360 service and plans to invest $1 billion in AI over the next three years. The move follows Tata Consultancy Services’ announcement to train 25,000 engineers on generative AI tools
– Wipro’s Ai360 aims to integrate AI into all software for clients and train its employees in AI.
Beehiiv, a platform for creating newsletters has launched new AI features that could transform the way newsletters are written.
KPMG plans to spend $2 billion on AI and cloud services through an expanded partnership with Microsoft, aiming to incorporate AI into its core services. This move is in response to a slowdown in advisory deals and a challenging economic environment.[1]
Elon Musk will host a conversation about AI with Rep. Ro Khanna (D-Calif.) and Rep. Mike Gallagher (R-Wis.) on Twitter Spaces Wednesday evening, a congressional aide confirmed to The Hill. Gallagher and Khanna have in the past stressed the need for balance in the technology, both expressing optimism about potential benefits while also sharing concerns about the potential dangers it can pose.
IBM is considering the use of artificial intelligence chips that it designed in-house to lower the costs of operating a cloud computing service it made widely available this week, an executive said Tuesday
Elon Musk continues to shake up the tech world with his latest venture into AI. Gotta love the guy! He assembled an all-star team of AI experts from leading companies and research institutions to join his mysterious new startup, xAI.
This lineup of engineers and scientists is the Avengers in real life:
– Igor Babuschkin, renowned researcher from OpenAI and DeepMind, handpicked by Musk for his expertise in developing chatbots
– Manuel Kroiss, software engineer from Google and DeepMind, known for innovations in reinforcement learning
– Tony Wu, pioneering work on automated reasoning and math at Google Brain and a stealth startup
– Christian Szegedy, veteran AI scientist from Google with background in deep learning and computer vision
– Jimmy Ba, UofT professor and CIFAR chair, acclaimed for efficient deep learning algorithms
– Toby Pohlen, led major projects at DeepMind like AlphaStar and Ape-X DQfD
– Ross Nordeen, technical PM from Tesla managing new hires and access at Twitter
– Kyle Kosic, full stack engineer and data scientist with experience at OpenAI and Wells Fargo
– Greg Yang, Morgan Prize honorable mention with seminal work on Tensor Programs at Microsoft Research
– Guodong Zhang, UofT and Vector Institute researcher focused on training and aligning large language models
– Zihang Dai, Google scientist known for XLNet and Funnel-Transformer for efficient NLP
xAI just posted their first Tweet 20 minutes ago and asked this: “What are the most fundamental unanswered questions?” What do you think let me know in the comments.
In today’s world, messaging apps are becoming increasingly popular, with WhatsApp being one of the most widely used. With the help of artificial intelligence, chatbots have become an essential tool for businesses to improve their customer service experience. Chatbot integration with WhatsApp has become a necessity for businesses that want to provide a seamless and efficient customer experience. ChatGPT is one of the popular chatbots that can be integrated with WhatsApp for this purpose. In this blog post, we will discuss how to integrate ChatGPT with WhatsApp and how this chatbot integration with WhatsApp can benefit your business. https://www.seaflux.tech/blogs/integrate-chatgpt-with-whatsapp
I am constantly asking my Alexa or Google Home (or Assistant on my phone) questions, and so many times they don’t understand the question, or can’t help me with that, or just gets it wrong. Half the time when I ask Alexa a question it simply says something like “Getting that from YouTube” or something else irrelevant. Simple questions like conversions, or really factual basic questions usually work, but most questions I realize will be too “complicated” for voice assistants and end up pulling out my phone to ask ChatGPT, but this is so inconvenient sometimes.
Yet, the same companies that run these assistants have major AI software. Why didn’t they integrate AI responses day one in Google Assistant for example? Or at least give us a voice skill or app that we can specifically call upon for this content.
Something like “Okay Google, ask Bard who would win in a fight between a polar bear and a dozen tasmanian devils?” should be easy to implement and vastly more convenient than pulling out your phone and opening ChatGPT. Thoughts?
The battle to take OpenAi’s crown is also heating up on the other side of the globe. Baichuan Intelligence, founded by Sogou‘s founder Wang Xiaochuan, has launched Baichuan-13B, its next-generation large language model. The model, based on the Transformer architecture, is open-source and optimized for commercial use and aims to create a Chinese equivalent of OpenAI. China’s focus on large language models aligns with its stringent AI regulations and considers the licensing requirements for launching such models which may impact China’s competition with the US in the industry.
Ukraine and NATO will be closely monitoring Russian naval activity in the Black Sea. Russia has however tried to make this more difficult by devising a unique new camouflage scheme, painting the bow and stern of ships black.
You’ve heard that before, haven’t you? Well, unlike probably all the other times someone’s said it, I don’t mean it as a put-down but as a compliment.
I used to think that was bad. I no longer do. It turns out this handy property—automated bullshitting—is singularly useful nowadays.
ChatGPT may not be the end of meaning, as I’ve often wondered, but quite the opposite: The end of meaninglessness.
During a recent exchange on Twitter about the value and cost of using ChatGPT, a person told me this:
“The problem is that most of us don’t get to live in a purely thoughtful, intellectual environment. Most of us have to live with jobs where we’re required to write corporate nonsense in response to corporate nonsense. Automating this process is an attempt to recapture some sanity.”
As a writer, I live in a somewhat “purely thoughtful, intellectual environment,” abstracted from the emptiness of “corporate nonsense.” My professional career has been an incessant effort to not be absorbed into it.
That’s why I never really saw the need to use ChatGPT. That’s why I couldn’t understand just how useful—life-saving even—it is for so many people.
Now I get it: ChatGPT allows them to escape what I’ve been avoiding my whole life. People are just trying to “recapture some sanity” with the tools at their disposal as I do when I write.
Whereas for me, as a blogger-analyst-essayist, ChatGPT feels like an abomination, for them—for most of you—it couldn’t be more welcome.
Because what else but a bullshit-generating tool to cancel out bullshit-requiring tasks so people can finally fill their lives with something else?
ChatGPT isn’t emptying people’s lives of meaning. No, it’s emptying them of the modern illness of meaninglessness.
• Google’s AI-backed note-taking tool, Project Tailwind, has been rebranded as NotebookLM and is launching to a small group of users in the US.
• The core functionality of NotebookLM starts in Google Docs, with plans to add additional formats soon.
• Users can select documents and use NotebookLM to ask questions about them and create new content.
• Potential uses include automatically summarizing a long document or turning a video outline into a script. The tool seems primarily geared towards students, for example, summarizing class notes or providing information on a specific topic studied.
• Google aims to improve the model’s responses and mitigate inaccuracies by limiting the underlying model only to the information added by the user.
• NotebookLM has built-in citations for quick fact-checking of automatically generated responses. However, Google warns that the model may still make errors and its accuracy depends on the information provided by the user.
• The NotebookLM model only has access to the documents chosen by the user for upload. The data is not available to others nor used to train new AI models.
• Despite its potential, NotebookLM is still in its infancy and only accessible via a waitlist in Google Labs. It could potentially reshape the future of Google Drive.
From Google:
JUL 12, 2023
• Google has introduced NotebookLM, an AI-first notebook that helps users gain insights faster by synthesizing facts and ideas from multiple sources.
• NotebookLM is designed to use the power of language models paired with existing content to gain critical insights quickly. It can summarize facts, explain complex ideas, and brainstorm new connections based on the sources selected by the user.
• Unlike traditional AI chatbots, NotebookLM allows users to “ground” the language model in their notes and sources, creating a personalized AI that’s versed in the information relevant to the user.
• Users can ground NotebookLM in specific Google Docs and perform actions like generating a summary, asking questions about the documents, and generating ideas.
• Each AI response comes with citations for easy fact-checking against the original source material.
• NotebookLM is an experimental product built by a small team in Google Labs. The team aims to build the product with user feedback and roll out the technology responsibly.
• The model only has access to the source material chosen by the user for upload, and the data is not used to train new AI models.
• NotebookLM is currently available to a small group of users in the U.S., and users can sign up to the waitlist to try it out.
Here’s a cool story about Greg Mushen, a tech pro from Seattle. He used ChatGPT to create a running program for him. He wasn’t a fan of running before, but he wanted to develop a healthy exercise habit.
The AI’s plan was simple and gradual. It started with small steps like putting his running shoes next to the front door. His first run, three days into the program, was just a few minutes long. Over time, he worked his way up to longer runs. Three months later, he’s running six days a week and has lost 26 pounds.
An expert running coach confirmed that the GPT’s advice was sound; the gradual approach is ideal for beginners to make progress while avoiding injury.
One interesting part of the AI’s plan was that it didn’t start with running at all. The first task was just to put his shoes by the door, and the next day to schedule a run. These small tasks helped to build a habit and make the process feel less daunting.
So, if you’re looking to get into running, maybe give ChatGPT a try. It seems to know what it’s doing. 😀
The emerging trends that will shape the future of AI.
1. Reinforcement learning and self-learning systems
Reinforcement learning, a branch of machine learning, holds great promise for the future of AI. It involves training AI systems to learn through trial and error and get rewarded for doing something well. As algorithms become more sophisticated, we can expect AI systems to develop the ability to not only learn but get exponentially better at learning and improving without explicit human intervention, leading to significant advancements in autonomous decision-making and problem-solving.
2. AI in healthcare
The healthcare sector is likely to benefit a lot from advancements in AI in the coming years. Predictive analytics, machine learning algorithms and computer vision can help diagnose diseases, personalize treatment plans and improve patient outcomes. AI-powered chatbots and virtual assistants can boost patient engagement and expedite administrative processes. I am hopeful that the integration of AI in healthcare will lead to more accurate diagnoses, cost savings and improved access to quality care.
3. Autonomous vehicles
The autonomous vehicle industry has already made significant progress, and the next decade will likely witness their widespread adoption. AI technologies such as computer vision, deep learning and sensor fusion will continue to improve the safety and efficiency of self-driving cars.
4. AI and cybersecurity
Technology is a double-edged sword, especially when it comes to dealing with bad actors. AI-driven cybersecurity systems are adept at finding and eliminating cyber threats by analyzing large volumes of data and detecting anomalies. In addition, these systems can provide a faster response time to minimize any potential damage caused by a breach. However, with similar technology being used by both defenders and attackers, safeguarding the AI systems themselves might turn out to be a major concern.
The impact of AI on the employment sector appears to be a fiercely debated topic with no clear consensus. According to a recent Pew Research Center survey, 47% of people think AI would perform better than humans at assessing job applications. However, a staggering 71% of people are against using AI to make final hiring decisions. While 62% think that AI will have a significant impact on the workforce over the next two decades, only 28% are concerned that they might be personally affected.
While AI might take over some jobs, it is also expected to create new job opportunities. Many current AI tools, including ChatGPT, cannot be fully relied on for context or accuracy of information; there must be some human intervention to ensure correctness. For example, when a company decides to reduce the number of writers in favor of ChatGPT, it will also have to hire editors who can carefully examine the AI-generated content to make sure it makes sense.
6. Climate modeling and prediction
AI can enhance climate modeling and prediction by analyzing vast amounts of climate data and identifying patterns and trends. Machine learning algorithms can improve the accuracy and granularity of climate models, helping us understand the complex interactions within the Earth’s systems. This knowledge enables better forecasting of natural disasters, extreme weather events, sea-level rise and long-term climate trends. As we look ahead, AI can enable policymakers and communities to make informed decisions and develop effective climate action plans.
7. Energy optimization and efficiency
AI can optimize energy consumption and enhance the efficiency of renewable energy systems. Machine learning algorithms analyze energy usage patterns, weather data and grid information to improve energy distribution and storage. AI-powered smart grids balance supply and demand, reducing transmission losses and seamlessly integrating renewable energy sources. This maximizes clean energy utilization, reduces greenhouse gas emissions and lessens our dependence on fossil fuels.
8. Smart resource management
AI can revolutionize resource management by optimizing resource allocation, minimizing waste and improving sustainability. For example, in water management, AI algorithms can analyze data from sensors and satellite imagery to predict water scarcity, optimize irrigation schedules and identify leakages. AI-powered systems can also optimize waste management, recycling and circular economy practices, leading to reduced resource consumption and a more sustainable use of materials.
As AI becomes more integrated into our lives, prioritizing ethical considerations becomes paramount. Privacy, bias, fairness and accountability are key challenges that demand attention. Achieving a balance between innovation and responsible AI practices necessitates collaboration among industry leaders, policymakers and researchers. Together, we must establish frameworks and guidelines to protect human rights and promote social well-being.
In the past, making custom proteins was challenging. The main challenge was predicting how a string of amino acids would fold into a 3D structure, a process known as protein folding. Scientists often had to rely on trial and error, which was time-consuming and often unsuccessful. Plus, they were limited to modifying existing proteins, which restricted the range of possible functions.
But now, with AI tools like RFdiffusion, scientists can sketch out proteins just like an artist sketches a picture. They input the characteristics they want the protein to have, and the AI tool generates a design for a protein that should have those characteristics. This is done by using a neural network that has been trained on thousands of known protein structures. The AI uses this training to predict how a new sequence of amino acids will fold into a 3D structure.
And the best part is that early tests show that these designed proteins actually do what the software predicts they will do.
RFdiffusion was released in March 2023 and it’s already making waves. It’s helping scientists design proteins that can bind to other molecules, which is super important in medicine. For example, they’ve used it to create proteins that bind strongly to proteins involved in cancers and autoimmune diseases.
But it’s not all rainbows and unicorns. The team is producing so many designs that testing them all is becoming a challenge.
And while the AI is good at designing proteins that can stick to another specified protein, it struggles with more complex tasks. For example, designing flexible proteins that can change shape is tough, as it involves predicting multiple possible structures. AI also struggles to create proteins vastly different from those found in nature, as it’s been trained on existing proteins.
Despite these challenges, the tool is already being used by around 100 users each day and has the potential to be a game-changer in the field of protein design.
The next steps are to improve the tool and explore how it can be used to design more complex proteins and carry out tasks no natural protein has ever evolved to do.
TL;DR: AI is now designing proteins that could revolutionize medicine. The tool, RFdiffusion, is helping scientists create proteins that have never existed before. It’s already being used to create proteins that bind to molecules involved in cancers and autoimmune diseases. Despite some challenges, the future of protein design looks promising thanks to AI. Source: 1, 2.
Silicon Valley has another hot Generative AI startup, Inflection AI, who is ready to storm the supercomputing world by building their own ~$1B supercomputing cluster.
Inflection AI aims to create a “personal AI for everyone” for which they are building out their own AI-powered assistant called Pi. Recent findings show that Pi is competitive with other leading AI models such as OpenAI’s GPT3.5 and Google’s 540B PaLM model.
To build even larger and more capable models, the startup is aiming to build one of the largest AI training clusters in the world with the following specs:
It will consist of 22,000 H100 NVIDIA GPUs.
It will contain 700 racks of Intel Xeon CPUs.
Considering that a single H100 GPU retails for $40,000, the GPU cost alone for the cluster surpasses the $850 million mark which suggests the $1 billion price tag according to some estimates.
Inflection recently closed a funding round of $1.5 billion with a company valuation of $4 billion. This is just 2nd only to OpenAI in terms of money raised which has raised $11.3 billion to date. Only Anthropic is the closest Gen-AI competitor in terms of money raised with the other bigger names relatively far behind.
An hour ago, Anthropic revealed Claude 2 their newest LLM that will now power their chat experience and their 100k token capability.
To stay on top of AI developments look here first. But the rundown is here on Reddit for your convenience!
If you are not familiar with Anthropic they are one of the leading companies in AI research and currently house the largest consumer available chatbot. Capable of understanding up to 75,000 words in one prompt. You can get access here. (Only available for US and UK) Key points: Improvements: Claude 2 offers longer, more interactive discussions, better coding skills, and enhanced mathematical and reasoning abilities than the previous model. Claude 2’s API will be accessible for developers and business at the same price Claude 1.3 was previously Top Scores: Claude 2 has already excelled in rigorous testing. It scored a C+ 76.5% on the Bar Exam’s multiple-choice section and surpassing the 90th percentile on GRE reading and writing exams. It also scored 71.2% on the Codex HumanEval, a Python test. Possibilities: Claude’s insane 100k context window allows for hundreds of pages to be analyzed. To put it into perspective that is enough content to be able to read or write a full book. Why you should care:
Anthropic values AI safety above everything and the safety improvements in Claude 2 also show a significant step forward in reducing harmful outputs from AI. They have created a “Constitutional AI” (CAI) that shapes the outputs of AI systems. They said “As AI systems become more capable, we would like to enlist their help to supervise other AIs.”
Source (Anthropic)
Human reporters interviewing humanoid AI robots in Geneva
So, on Friday last week in Geneva, the “AI for Good Global Summit” was held. It marked the world’s first news conference featuring humanoid social robots.
The United Nations Technology agency hosted the event, which saw reporters interview 9 humanoid robots, discussing topics ranging from robot world leaders to AI in the workplace.
You might be asking yourself why I’m writing about this story in particular – it’s because has given me quite a startle, a wake-up call if you will. Reading threads on Reddit, or many other AI news sources for that matter, you’d be led to beleive that most people are using AI as “productivity” or “work” growth hacks (or porn generators). While this is certainly the case, there are some very clever cookies out there using AI to replicate humans as closely as possible – and if you watch some of the footage above, it’s quite easy to see how advanced they’re getting.
It’s one thing to ponder how AI will impact us and our daily lives – like how we can use AI to better regulate traffic lights, how Paul McCartney can use AI to create the Beatle’s final song, or how Marvel fans are pissed off that AI are in Marvel movies, but when we consider the potential for AI humanoids to be walking around and interracting with us – I dunno, that makes me feel something different. I can’t help but wonder if these developers are considering what they’re actually putting out into reality with these human-like bots, or if they’re just pursuing their own ambitions blindly. I just don’t know, I really don’t know.
Is humanity an experiment in artificial intelligence? Think about it: we are placed on this earth, it’s own isolated Petri dish, isolated from any other living thing so there is no way to cross contaminate us with anything outside our environment.
We are placed in an environment that gives us basic subsistence and we are allowed to evolve. After a few million years, we develop farming and civilization (~8,000 years ago), we grow and develop technologies (the Industrial Revolution ~1760-ish), first flight (1903), and then with enormous effort of resource allocation, organization, and technology we pop out of our Petri dish in 1957 with Sputnik, and later the moon in 1969. However, because our life span is too short, it is impossible for us to travel much beyond that.
So — what if our lifespans were engineered to be artificially short, so we can’t travel beyond our solar system— meaning no escaping the experiment. With shorter life spans, we can be studied generationally like we do with lab rats.
Are we being studied to see how highly advanced AI plays out? (Humanity as the AI?) We are given just enough ethical/religious guidance and yet the free will to create technology that could kill us — nukes, global warming, etc. Are we being studied to see if we will have the collective intelligence to save ourselves or burn ourselves out due to greed and ignorance?
Are things like ethics and religion variables in the experiment? What happens when we are given small insights? For instance, we know we are poisoning our atmosphere due to fossil fuel use, but we still continue even though we know the outcome.
Now we are at the evolutionary step of creating our own AI? At what point does the experiment end?
Are our alleged UFO friends, then, monitoring the experiment?
What is Explainable AI and its Necessity
Trained AI algorithms work by taking the input and providing the output without explaining its inner workings. XAI aims at pointing out the rationale behind any decision by AI in such a way that humans can interpret it.
Chamber of Progress CEO Adam Kovacevich explains that American policymakers need to lead – but that doesn’t mean racing the EU to enact regulations that could suffocate our burgeoning AI sector. US lawmakers shouldn’t be embarrassed that we’re “behind” in regulation—they should be proud that our regulatory environment has given birth to the world’s leading tech services, and those successes have created great jobs for millions of Americans. When it comes to AI, the US should establish its own innovation-friendly rules and responsibly nurture our AI lead.
The recent introduction of AI tools by Lightning Labs allows AI applications to hold, send, and receive Bitcoin. The tools leverage Lightning Network, a second-layer payment network for faster and cheaper Bitcoin transactions. By integrating high-volume Bitcoin micropayments with popular AI software libraries like LangChain, Lightning Labs addresses the lack of a native Internet-based payment mechanism for AI platforms.
Why does this matter?
This development eliminates the need for outdated payment methods, reducing costs for software deployment and expanding the range of possible AI use cases. The integration of Lightning into AI models has the potential to enable new applications that were previously not feasible.
Recent research has found that pre-trained LLMs can complete complex token sequences, including those generated by probabilistic context-free grammars (PCFG) and ASCII art prompts. The study explores how these zero-shot capabilities can be applied to robotics problems, such as extrapolating sequences of numbers to complete simple motions and prompting reward-conditioned trajectories to discover and represent closed-loop policies.
Although deploying LLMs for real systems is currently challenging due to latency, context size limitations, and compute costs, the study suggests that using LLMs to drive low-level control could provide insight into how patterns among words could be transferred to actions.
Why does this matter?
Potential applications for this approach beyond robotics are that it could be used to model and predict sequential data like stock market prices, weather data, traffic patterns, etc. Also, it could learn game strategies by observing sequences of moves and positions, then use that to play against opponents or generate new strategies.
Researchers have proposed a novel online reinforcement learning framework called RLTF for refining LLMs for code generation. The framework uses unit test feedback of multi-granularity to generate data in real time during training and guide the model toward producing high-quality code. The approach achieves SotA performance on the APPS and the MBPP benchmarks for their scale.
Why does this matter?
RLTF can potentially improve LLMs’ performance on code generation tasks. Current RL methods for code generation use offline frameworks and simple unit test signals, which limits their exploration of new sample spaces and does not account for specific error locations within the code.
What Else Is Happening in AI
Wow! AI-based laser pesticide & herbicide without chemicals! (Link)
Wildfire Detection Startup Pano AI Secures Additional $17M. (Link)
Netflix researchers have invented the Magenta Green Screen (MGS), which uses AI to make TV and film visual effects more real.
Unlike traditional green screen methods, which can struggle with small details and take time to edit, the MGS lights actors with a mix of red, blue, and green LEDs. This creates a unique ‘magenta glow’ which AI can separate from the background in real-time.
Plus, the AI can adjust the magenta color to look normal, speeding up filming.
Why it matters? This tech could make filming faster and the special effects more realistic, leading to quicker show releases and more believable scenes.
Several hospitals, including the Mayo Clinic, one of the major healthcare institutions in the USA, started field-testing Google’s Med-PaLM 2, an AI chatbot that specializes in the Medicine field.
Google believes that Med-PaLM 2, built using questions and answers from medical exams, can provide superior medical advice. The AI chatbot is currently in its testing phase in various hospitals and may be particularly valuable in places where there’s a shortage of doctors.
Why it matters? This could mark a significant shift in healthcare delivery, potentially providing reliable medical advice remotely and in areas with limited healthcare access.
The US military is utilizing large-language models (LLMs) to speed up decision-making processes. These AI-powered models have demonstrated the ability to complete requests in minutes that would typically take hours or days, potentially revolutionizing military operations.
Pano AI, a wildfire detection startup, secures a $17 million Series A extension led by Valor Equity Partners, with participation from T-Mobile Ventures and Salesforce. The company’s remote-controllable cameras, combined with AI algorithms, provide early warnings of wildfires, allowing emergency responders to take swift action and reduce response time.
AI Champions
Here are 5 AI tools that caught our eye today
Nolej: Generate interactive e-learning content, assessments, and courseware from your provided materials.
Hify: Create customized and engaging sales videos directly from your browser.
Coda: Combine text, data, and team collaboration into a single document.
Lunacy: Utilize AI capabilities and built-in graphics to create UI/UX designs.
Webbotify: Develop custom AI chatbots trained on your own data.
AI Tutorial
Using ChatGPT’s Code Interpreter Plugin for Data Analysis
Step 1: Plugin Access
First of all, to access the Code Interpreter plugin, you’ll need to have access to ChatGPT Plus. If you’re not already a subscriber, you can sign up on OpenAI’s website.
Step 2: Data Upload
The Code Interpreter plugin allows you to upload a file directly into the chat. The data can be in various formats, such as tabular data (like Excel or CSV files), images, videos, PDFs, or other types.
Step 3: Data Preparation
After uploading the dataset, you might need to check if it requires cleaning. The dataset might include missing values, errors, outliers, etc. that might affect your analysis later on.
Clean the uploaded dataset by removing or replacing missing values and excluding any outliers
Step 4: Data Analysis
The Code Interpreter runs Python code in the backend for your data. If you don’t already know, Python is a really powerful language for data analytics, data science, and statistical modeling. With simple English prompts, the plugin will write & perform any kind of analysis for you.
Analyze the distribution of [column name] and provide summary statistics such as the mean, median, and standard deviation
Step 5: Data Visualization
Python is also very powerful in data visualization, hence, the Code Interpreter is powerful as well. You can create plots for your data by specifying the type, column, and color theme.
Generate a [plot type] for the [column name] with a blue color theme
Step 6: Data Modeling
AI training an AI? You can build and train Machine Learning models such as Linear Regression or Classification on your data. The models can help you make better decisions or predict future data.
Build a [model name] model to predict [target variable] based on [feature variables].
Step 7: Download Data
Finally, download your cleaned and processed dataset.
Transforming ChatGPT into a Powerful Development Tool for Data Scientists
OpenAI’s ChatGPT, an AI-powered chatbot, has been making waves in the tech community since its launch. Now, OpenAI has taken a significant leap forward by introducing an in-house Code Interpreter plugin for ChatGPT Plus subscribers. This plugin revolutionizes ChatGPT, transforming it from a mere chatbot into a powerful tool with expanded capabilities. Let’s explore how this new feature is set to impact developers and data scientists.
Enhanced Functionality for ChatGPT Plus Subscribers
OpenAI has unveiled its Code Interpreter plugin, providing ChatGPT Plus subscribers with advanced features and capabilities.
Subscribers gain access to a range of functions within ChatGPT, including data analysis, chart creation, file management, math calculations, and even code execution.
This expanded functionality opens up exciting possibilities for data science applications and empowers subscribers to perform complex tasks seamlessly.
Unlocking Data Science Use Cases in ChatGPT
With the Code Interpreter plugin, ChatGPT becomes a valuable tool for data scientists and developers.
Users can analyze datasets, generate insightful visualizations, and manipulate data within the ChatGPT environment.
The ability to run code directly within ChatGPT offers a convenient platform for experimenting with algorithms, testing code snippets, and refining data analysis techniques.
Streamlining Development with the In-house Code Interpreter Plugin
The Code Interpreter plugin is an in-house feature that simplifies the development process.
Developers can write and test code within the same environment, eliminating the need to switch between different tools or interfaces.
This streamlines the development workflow, saves time, and enhances productivity by providing a seamless coding experience.
Benefits for Developers: Debugging, Testing, and Efficiency
The in-house code interpreter plugin offers significant benefits to developers.
Debugging and testing code becomes more efficient with real-time feedback and error identification directly within ChatGPT.
Developers can quickly iterate and improve code segments without the hassle of switching between different tools or environments.
The seamless development experience fosters faster prototyping, experimentation, and overall code quality.
Empowering Businesses and Individuals with Chatbot Knowledge
ChatGPT, beyond its code interpreter capabilities, provides valuable information and resources on chatbot development, natural language processing, and machine learning.
Businesses and individuals interested in leveraging chatbots for customer service or operational improvements can benefit from the insights offered by ChatGPT.
The availability of this knowledge empowers users to understand the potential applications and benefits of chatbot technology.
Conclusion
OpenAI’s introduction of the Code Interpreter plugin for ChatGPT Plus subscribers marks a significant milestone in the evolution of chatbots and their impact on developers and data scientists. By providing an integrated coding environment, OpenAI streamlines development workflows, enhances productivity, and opens up new possibilities for data science use cases. As developers and businesses embrace this innovation, we can expect to witness exciting advancements in AI-driven technologies.
Less than a year ago, artificial intelligence felt like something out of a science fiction novel to many people. Today, AI models like ChatGPT, DALL·E, and more are becoming part of everyday life. And the technology that allows machines to see, read, think, write, and create (or at least seem like they can) is getting better by the day.
Naturally, as AI capabilities continue to improve, the concerns grow, too. With each advancement made, it feels like there’s another risk to worry about. For every positive headline about an AI-related story, it’s easy to picture a potential negative one—even if the doom-and-gloom is still hypothetical—from deepfakes that could undermine democracy, to increased cyber-attacks, to more cheating (and less learning) in school, to the proliferation of misinformation, to jobs being taken by machines.
I’ve been thinking a lot about these risks and the questions they pose for society. They need to be taken seriously. But there’s good reason to believe that we can deal with them: We’ve done it before.
As I explain in my latest Gates Notes post, “The risks of AI are real but manageable,” today’s and tomorrow’s AIs might be unprecedented—but nearly every major innovation in the past has also introduced novel threats that had to be considered and controlled. If we move fast, we can do it again. If we manage the risks of AI, we can help ensure that they’re outweighed by the rewards (of which I believe there are many).
KPMG plans to spend $2 billion on AI and cloud services through an expanded partnership with Microsoft, aiming to incorporate AI into its core services. This move is in response to a slowdown in advisory deals and a challenging economic environment.
Elon Musk will host a conversation about AI with Rep. Ro Khanna (D-Calif.) and Rep. Mike Gallagher (R-Wis.) on Twitter Spaces Wednesday evening, a congressional aide confirmed to The Hill. Gallagher and Khanna have in the past stressed the need for balance in the technology, both expressing optimism about potential benefits while also sharing concerns about the potential dangers it can pose.[2]
IT major Wipro announced the launch of the ai360 service and plans to invest $1 billion in AI over the next three years. The move follows Tata Consultancy Services’ announcement to train 25,000 engineers on generative AI tools.[3]
IBM is considering the use of artificial intelligence chips that it designed in-house to lower the costs of operating a cloud computing service it made widely available this week, an executive said Tuesday.
Navigating the Revolutionary Trends of July 2023: July 10th, 2023
Generative AI such as ChatGPT is increasingly being used to control robots. This bodes for concern since the AI might produce faulty instructions and endanger humans.
Comedian and author Sarah Silverman, along with authors Christopher Golden and Richard Kadrey, have filed a lawsuit against OpenAI and Meta. They allege that both companies infringed their copyrights by using datasets containing their works to train their AI models.
Lawsuit Details: The authors claim that OpenAI’s ChatGPT and Meta’s LLaMA models were trained on datasets illegally obtained from shadow library websites. These websites supposedly offer bulk downloads of books via torrent systems. The authors did not give their consent for their works to be used in this manner.
The claimants have provided evidence showing that when prompted, ChatGPT can summarize their books, which they argue is a violation of their copyrights.
The suit against Meta similarly alleges that the authors’ books were accessible in datasets used to train its LLaMA models.
Meta’s Connection to Illicit Datasets: The lawsuit points out a possible illicit origin for the datasets used by Meta. In a Meta paper detailing the LLaMA model, one of the sources for training datasets is ThePile, assembled by EleutherAI. ThePile is described as being put together from a copy of the contents of a shadow library, thus raising legality concerns.
Legal Allegations and Potential Consequences: The lawsuits include several counts of copyright violations, negligence, unjust enrichment, and unfair competition. The authors are seeking statutory damages, restitution of profits, and other reliefs.
It will probably be discovered that the more a student relies on AI for their learning, the higher they will score on standardized tests like the SAT. I think we’ll see the first evidence of this as early as next year, but a few years later that evidence will be much stronger and more conclusive. What do you think?
OpenAI still didn’t declare their GPT agents’ vision, but it exists implicitly in their plugin announcement. And this approach allows us to act on the basis of complex executable-information retrieval, and use plugins are some kind of an app store, but actually, they are much more than the app store.
Discover the top 10 game-changing applications of deep learning in cybersecurity, from threat detection to malware identification.
Threat Detection:
Deep learning models excel at detecting known and unknown threats by analyzing network traffic, identifying negative patterns, and detecting anomalies in real-time. These models can swiftly identify potential cyber-attacks, providing early warning signs to prevent data breaches.
Malware Identification:
Deep learning algorithms can analyze file behavior and characteristics to identify malware. By training on large datasets of known malware samples, these models can quickly and accurately identify new strains of malicious software, helping security teams stay one step ahead of attackers.
Intrusion Detection:
Deep learning can enhance intrusion detection systems (IDS) by analyzing network traffic and identifying suspicious activities. These models can detect network intrusions, unauthorized access attempts, and unusual behaviors that may indicate an ongoing cyber-attack.
Phishing Detection:
Phishing attacks remain a significant concern in cybersecurity. Deep learning algorithms can analyze email content, URLs, and other indicators to identify phishing attempts. By learning from past phishing campaigns, these models can detect and block suspicious emails, protecting users from phishing scams.
User Behavior Analytics:
Deep learning can analyze user behavior patterns and detect deviations indicating insider threats or compromised accounts. By monitoring user activities and analyzing their behavior, these models can identify unusual or suspicious actions, helping organizations mitigate insider risks.
Data Leakage Prevention:
Deep learning algorithms can identify sensitive data patterns and monitor data access and transfer to prevent unauthorized data leakage. These models can analyze data flow across networks, identify potential vulnerabilities, and enforce security policies to protect sensitive information.
Network Traffic Analysis:
Deep learning models can analyze network traffic to detect patterns associated with Distributed Denial of Service (DDoS) attacks. By monitoring network flows and identifying anomalous traffic patterns, these algorithms can help organizations defend against and mitigate the impact of DDoS attacks.
Vulnerability Assessment:
Deep learning can automate the process of vulnerability assessment by analyzing code, configurations, and system logs. These models can identify vulnerabilities in software and systems, allowing organizations to address them before they can be exploited proactively.
Threat Intelligence:
Deep learning algorithms can analyze large volumes of threat intelligence data from various sources to identify emerging threats and trends. By continuously monitoring and analyzing threat feeds, these models can provide timely and accurate threat intelligence, enabling organizations to take proactive measures against evolving cyber threats.
Fraud Detection:
Deep learning can be applied to detect fraudulent activities in financial transactions. By analyzing transactional data, customer behavior, and historical patterns, these models can identify potentially fraudulent transactions in real-time, helping organizations prevent financial losses
By harnessing patterns in mineral associations, a new machine-learning model can predict the locations of minerals on Earth and potentially, other planets. This advancement is of immense value to science and industry, as they continually explore mineral deposits to ….
Google has developed an AI tool called Med-PaLM 2, currently being tested at Mayo Clinic, that is designed to answer healthcare-related questions. Despite exhibiting some accuracy issues, the tool shows promising capabilities in areas such as reasoning and comprehension.
Here’s a recap:
Med-PaLM 2 and its Purposes: Google’s new AI tool, Med-PaLM 2, is being used at Mayo Clinic for testing purposes.
It’s an adaptation of Google’s language model, PaLM 2, that powers Google’s Bard.
The tool is aimed at helping healthcare in regions with less access to doctors.
Training and performance: Med-PaLM 2 has been trained on a selection of medical expert demonstrations to better handle healthcare conversations.
While some accuracy issues persist, as found in a study conducted by Google, the tool performed comparably to actual doctors in aspects such as reasoning and consensus-supported answers.
Data privacy: Users testing Med-PaLM 2 will have control over their data, which will be encrypted and inaccessible to Google.
This privacy measure ensures user trust and adherence to data security standards.
Google’s latest beast of a quantum computer is blowing everyone else out of the water. It’s making calculations in a blink that’d take top supercomputers almost half a century to figure out!
(Well, 47 years to be exact)
Here’s the gist: This new quantum from Google has 70 qubits (the building blocks of quantum computing). That’s a whole 17 more than their last machine, which might not sound like much, but in quantum land, that’s a huge deal.
It basically means 241 million times more powerful!
But what does that mean in practice? It’d take the world’s current number one supercomputer, Frontier, over 47 years to do what Google’s new quantum machine can do in an instant.
As always, there’s controversy. Some critics are saying the task used for testing was too much in favor of quantum computers and isn’t super useful outside of science experiments.
But we’re pushing boundaries here, folks, and this is one big step towards ‘utility quantum computing,’ where quantum computers do stuff that benefits all of us in ways we can’t even imagine right now.
What might those be? Well, imagine lightning-fast data analysis, creating more accurate weather forecasts, developing life-saving medicines, or even helping in solving complex climate change issues.
The potential is huge, and while we’re not there yet, we’re certainly getting closer.
Reportedly, Google’s Med-PaLM 2 (an LLM for the medical domain) has been in testing at the Mayo Clinic research hospital. In April, Google announced its limited access for select Google Cloud customers to explore use cases and share feedback to investigate safe, responsible, and meaningful ways to use it.
Meanwhile, Google’s rivals moved quickly to incorporate AI advances into patient interactions. Hospitals are beginning to test OpenAI’s GPT algorithms through Microsoft’s cloud service in several tasks. Google’s Med-PaLM 2 and OpenAI’s GPT-4 each scored similarly on medical exam questions, according to independent research released by the companies.
Why does this matter?
It seems Google and Microsoft are racing to translate recent AI advances into products that clinicians would use widely. The AI field has seen rapid advancements and research in diverse domains. But such a competitive landscape accelerates translating them into widely available, impactful AI products (which is sometimes slow and challenging due to the complexity of real-world applications).
LLMs are gaining massive recognition worldwide. However, no existing solution exists to determine the data and algorithms used during the model’s training. In an attempt to showcase the impact of this, Mithril Security undertook an educational project— PoisonGPT— aimed at showing the dangers of poisoning LLM supply chains.
It shows how one can surgically modify an open-source model and upload it to Hugging Face to make it spread misinformation while being undetected by standard benchmarks.
Mithril Security is also working on AICert, a solution to trace models back to their training algorithms and datasets which will be launched soon.
Why does this matter?
LLMs still resemble a vast, uncharted territory where many companies/users often turn to external parties and pre-trained models for training and data. It carries the inherent risk of applying malicious models to their use cases, exposing them to safety issues. This project highlights the awareness needed for securing LLM supply chains.
Google DeepMind is working on the definitive response to ChatGPT.
It could be the most important AI breakthrough ever.
In a recent interview with Wired, Google DeepMind’s CEO, Demis Hassabis, said this:
“At a high level you can think of Gemini as combining some of the strengths of AlphaGo-type systems with the amazing language capabilities of the large models [e.g., GPT-4 and ChatGPT] … We also have some new innovations that are going to be pretty interesting.”
Why would such a mix be so powerful?
DeepMind’s Alpha family and OpenAI’s GPT family each have a secret sauce—a fundamental ability—built into the models.
Alpha models (AlphaGo, AlphaGo Zero, AlphaZero, and even MuZero) show that AI can surpass human ability and knowledge by exploiting learning and search techniques in constrained environments—and the results appear to improve as we remove human input and guidance.
GPT models (GPT-2, GPT-3, GPT-3.5, GPT-4, and ChatGPT) show that training large LMs on huge quantities of text data without supervision grants them the (emergent) meta-capability, already present in base models, of being able to learn to do things without explicit training.
Imagine an AI model that was apt in language, but also in other modalities like images, video, and audio, and possibly even tool use and robotics. Imagine it had the ability to go beyond human knowledge. And imagine it could learn to learn anything.
That’s an all-encompassing, depthless AI model. Something like AI’s Holy Grail. That’s what I see when I extend ad infinitum what Google DeepMind seems to be planning for Gemini.
I’m usually hesitant to call models “breakthroughs” because these days it seems the term fits every new AI release, but I have three grounded reasons to believe it will be a breakthrough at the level of GPT-3/GPT-4 and probably well beyond that:
First, DeepMind and Google Brain’s track record of amazing research and development during the last decade is unmatched, not even OpenAI or Microsoft can compare.
Second, the pressure that the OpenAI-Microsoft alliance has put on them—while at the same time somehow removing the burden of responsibility toward caution and safety—pushes them to try harder than ever before.
Third, and most importantly, Google DeepMind researchers and engineers are masters at both language modeling and deep + reinforcement learning, which is the path toward combining ChatGPT and AlphaGo’s successes.
We’ll have to wait until the end of 2023 to see Gemini. Hopefully, it will be an influx of reassuring news and the sign of a bright near-term future that the field deserves.
• Demis Hassabis, the CEO of Google DeepMind, discusses the recent developments in AI and the future of the field.
• Google DeepMind is a new division of Google, created from the merger of Google Brain and DeepMind, a startup acquired by Google in 2014.
• DeepMind was known for applying AI to areas like games and protein-folding simulations, while Google Brain focused more on generative AI tools like large language models for chatbots.
• The merger was a strategic decision to make Google more competitive and faster to market with AI products.
• Hassabis discusses the competition in the AI field, noting that open-source models running on commodity hardware are rapidly evolving and catching up to the tools run by tech giants.
• He also talks about the risks and regulations associated with artificial general intelligence (AGI), a type of AI that can perform any intellectual task that a human being can.
• Hassabis signed a statement about AI risk that reads, “Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.”
• The article also touches on the impact of AI on labor, mentioning the creation of low-paid jobs for classifying data to train AI systems.
• Hassabis believes that we are at the beginning of a new era in AI, with the potential for new types of products and experiences that have never been seen before.
• The merger of DeepMind and Google Brain is still in progress, with the aim of creating a single, unified team.
Daily AI Update (Date: 7/10/2023): News from Google, Microsoft, Mithril Security, YouTube, TCS, and Shutterstock
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
Google & Microsoft battle to lead healthcare AI
– Reportedly, Google’s Med-PaLM 2 has been in testing at the Mayo Clinic research hospital. In April, Google announced its limited access for select Google Cloud customers to explore use cases and share feedback to investigate safe, responsible, and meaningful ways to use it.
-Meanwhile, Google’s rivals moved quickly to incorporate AI advances into patient interactions. Hospitals are beginning to test OpenAI’s GPT algorithms through Microsoft’s cloud service in several tasks.
-Google’s Med-PaLM 2 and OpenAI’s GPT-4 each scored similarly on medical exam questions, according to independent research released by the companies.
PoisonGPT shows the impact of poisoning LLM supply chains
– In an educational project, Mithril Security shows the dangers of poisoning LLM supply chains. It shows how one can surgically modify an open-source model and upload it to Hugging Face to make it spread misinformation while being undetected by standard benchmarks.
-To remedy this, it is also working on AICert, a solution to trace models back to their training algorithms and datasets.
Lost in the middle: How language models use long contexts
Does a bigger context window always lead to better results? New research reveals that
– Language models often struggle to use information in the middle of long input contexts
– Their performance decreases as the input context grows longer
– The performance is often highest when relevant information occurs at the beginning or end of the input context
YouTube tests AI-generated quizzes on educational videos
– YouTube is experimenting with AI-generated quizzes on its mobile app for iOS and Android devices, which are designed to help viewers learn more about a subject featured in an educational video.
TCS bets big on Azure Open AI
– TCS now plans to get 25,000 associates trained and certified on Azure Open AI to help clients accelerate their adoption of this powerful new technology.
Shutterstock continues generative AI push with legal protection
– Shutterstock announced that it will offer enterprise customers full indemnification for the license and use of generative AI images on its platform, to protect them against potential claims related to their use of the images. The company will fulfill requests for indemnification on demand through a human review of the images.
Recently, Bruno Le Maire (France’s Economy Minister) said he’d consider a 100% European ChatGPT to be a good idea. He said:
« Je plaide donc, avant de poser les bases de la régulation de l’intelligence artificielle, pour que nous fassions de l’innovation, que nous investissions et que nous nous fixions comme objectif d’avoir un OpenAI européen sous cinq ans, avec les calculateurs, les scientifiques et les algorithmes nécessaires. C’est possible ».
Which means :
« I therefore plead, before laying the foundations for the regulation of artificial intelligence, that we innovate, that we invest and that we set ourselves the objective of having a European OpenAI within five years, with computers, the necessary scientists and algorithms. It is possible ».
He also said he thought it’ll boost the European Union’s economy.
However, by 2028, OpenAI’s ChatGPT, Bing AI and Google Bard might have all considerably improved, making it a lot harder for the ‘European ChatGPT’ to compete with those three other ones
So in this case, it’s possible that Europe would start with a very high delay that’d be hard to catch up with…
Dr. Alvin Yew is currently working on an AI solution that takes topographical data on the moon and uses it in a neural network to help determine an astronaut’s location in the event that no GPS or other forms of electronic navigation is available. You can check it out here:
Training AI models requires massive volumes of information. But not all information is the same. The data to train the model must be error-free, properly formatted and labeled, and reflective of the issue. This can be a difficult and time-consuming process.
From acquiring a strong foundation in NLP to gaining practical experience, learn how to position yourself for success in AI prompt engineering field.
Understanding the role of an AI prompt engineer
An AI prompt engineer specializes in designing effective prompts to guide the behavior and output of AI models. They deeply understand natural language processing (NLP), machine learning and AI systems.
The AI prompt engineer’s primary goal is to fine-tune and customize AI models by crafting precise prompts that align with specific use cases, ensuring desired outputs and enhanced control.
Developing the necessary skills
To excel as an AI prompt engineer, some skills are crucial:
NLP and language modeling
A strong understanding of transformer-based structures, language models and NLP approaches is required. Effective prompt engineering requires an understanding of the pre-training and fine-tuning procedures used by language models like ChatGPT.
Programming and machine learning
Expertise in programming languages like Python and familiarity with frameworks for machine learning, such as TensorFlow or PyTorch, is crucial. Success depends on having a solid understanding of data preprocessing, model training and evaluation.
Prompt engineers will frequently work with other teams. Excellent written and verbal communication skills are required to work with stakeholders effectively, explain urgent requirements, and comprehend project goals.
Educational background and learning resources
A strong educational foundation is beneficial for pursuing a career as an AI prompt engineer. The knowledge required in fields like NLP, machine learning, and programming can be acquired with a bachelor’s or master’s degree in computer science, data science, or a similar discipline.
Additionally, one can supplement their education and keep up-to-date on the most recent advancements in AI and prompt engineering by using online tutorials, classes, and self-study materials.
Getting practical experience
Getting real-world experience is essential to proving one’s abilities as an AI prompt engineer. Look for projects, research internships, or research opportunities where one can use prompt engineering methods.
An individual’s abilities can be demonstrated, and concrete proof of their knowledge can be provided by starting their own prompt engineering projects or contributing to open-source projects.
Networking and job market context
As an AI prompt engineer, networking is essential for seeking employment prospects. Attend AI conferences, get involved in online forums, go to AI-related events and network with industry experts. Keep abreast of employment listings, AI research facilities, and organizations that focus on NLP and AI customization.
Continuous learning and skill enhancement
As AI becomes increasingly ubiquitous, the demand for skilled AI prompt engineers continues to grow. Landing a high-paying job in this field requires a strong foundation in NLP, machine learning, and programming, along with practical experience and networking.
Aspiring prompt engineers can position themselves for success and secure a high-paying job in this exciting and evolving field by continuously enhancing skills, staying connected with the AI community, and demonstrating expertise.
AI Weekly Rundown (July 1 to July 7)
AI builds robots, detects wildfires, designs CPU, uses public data to train, and more this week.
ChatGPT builds robots: New research
– Microsoft Research presents an experimental study using OpenAI’s ChatGPT for robotics applications. It outlines a strategy that combines design principles for prompt engineering and the creation of a high-level function library that allows ChatGPT to adapt to different robotics tasks, simulators, and form factors.
– The study encompasses a range of tasks within the robotics domain, from basic logical, geometrical, and mathematical reasoning to complex domains such as aerial navigation, manipulation, and embodied agents.
– Microsoft also released PromptCraft, an open-source platform where anyone can share examples of good prompting schemes for robotics applications.
Magic123 creates HQ 3D meshes from unposed images
– New research from Snap Inc. (and others) presents Magic123, a novel image-to-3D pipeline that uses a two-stage coarse-to-fine optimization process to produce high-quality high-resolution 3D geometry and textures. It generates photo-realistic 3D objects from a single unposed image.
– The core idea is to use 2D and 3D priors simultaneously to generate faithful 3D content from any given image. Magic123 achieves state-of-the-art results in both real-world and synthetic scenarios.
Any-to-any generation: Next stage in AI evolution
– Microsoft presents CoDi, a novel generative model capable of processing and simultaneously generating content across multiple modalities. It employs a novel composable generation strategy that involves building a shared multimodal space by bridging alignment in the diffusion process. This enables the synchronized generation of intertwined modalities, such as temporally aligned video and audio.
– One of CoDi’s most significant innovations is its ability to handle many-to-many generation strategies, simultaneously generating any mixture of output modalities. CoDi is also capable of single-to-single modality generation and multi-conditioning generation.
OpenChat beats 100% of ChatGPT-3.5
– OpenChat is a collection of open-source language models specifically trained on a diverse and high-quality dataset of multi-round conversations. These models have undergone fine-tuning using approximately ~6K GPT-4 conversations filtered from the ~90K ShareGPT conversations. It is designed to achieve high performance with limited data.
AI designs CPU in <5 hours
– A team of Chinese researchers published a paper describing how they used AI to design a fully functional CPU based on the RISC-V architecture, which is as fast as an Intel i486SX. They called it a “foundational step towards building self-evolving machines.” The AI model completed the design cycle in under 5 hours, reducing it by 1000 times.
SAM-PT: Video object segmentation with zero-shot tracking
– Researchers introduced SAM-PT, an advanced method that expands the capabilities of the Segment Anything Model (SAM) to track and segment objects in dynamic videos. SAM-PT utilizes interactive prompts, such as points, to generate masks and achieves exceptional zero-shot performance in popular video object segmentation benchmarks, including DAVIS, YouTube-VOS, and MOSE. It takes a unique approach by leveraging robust and sparse point selection and propagation techniques.
– To enhance the tracking accuracy, SAM-PT incorporates K-Medoids clustering for point initialization and a point re-initialization strategy.
Google’s AI models to train on public data
– Google has updated its privacy policy to state that it can use publicly available data to help train and create its AI models. This suggests that Google is leaning heavily into its AI bid. Plus, harnessing humanity’s collective knowledge could redefine how AI learns and comprehends information.
LEDITS: Image editing with next-level AI capabilities
– Hugging Face research has introduced LEDITS- a combined lightweight approach for real-image editing, incorporating the Edit Friendly DDPM inversion technique with Semantic Guidance. Thus, it extends Semantic Guidance to real image editing while harnessing the editing capabilities of DDPM inversion.
OpenAI makes GPT-4 API and Code Interpreter available
– GPT-4 API is now available to all paying OpenAI API customers. GPT-3.5 Turbo, DALL·E, and Whisper APIs are also now generally available, and OpenAI is announcing a deprecation plan for some of the older models, which will retire beginning of 2024.
– Moreover, OpenAI’s Code Interpreter will be available to all ChatGPT Plus users over the next week. It lets ChatGPT run code, optionally with access to files you’ve uploaded. You can also ask ChatGPT to analyze data, create charts, edit files, perform math, etc.
Salesforce’s CodeGen2.5, a small but mighty code LLM
– Salesforce’s CodeGen family of models allows users to “translate” natural language, such as English, into programming languages, such as Python. Now it has added a new member- CodeGen2.5, a small but mighty LLM for code.
– Its smaller size means faster sampling, resulting in a speed improvement of 2x compared to CodeGen2. The small model easily allows for personalized assistants with local deployments.
InternLM: A model tailored for practical scenarios
– InternLM has open-sourced a 7B parameter base model and a chat model tailored for practical scenarios. The model
– Leverages trillions of high-quality tokens for training to establish a powerful knowledge base
– Supports an 8k context window length, enabling longer input sequences and stronger reasoning capabilities
– Provides a versatile toolset for users to flexibly build their own workflows
-It is a 7B version of a 104B model that achieves SoTA performance in multiple aspects, including knowledge understanding, reading comprehension, mathematics, and coding. InternLM-7B outperforms LLaMA, Alpaca, and Vicuna on comprehensive exams, including MMLU, HumanEval, MATH, and more.
Microsoft’s LongNet scales transformers to 1B tokens
– Microsoft research’s recently launched LongNet allows language models to have a context window of over 1 billion tokens without sacrificing the performance on shorter sequences.
– LongNet achieves this through dilated attention, exponentially expanding the model’s attentive field as token distance increases.
– This breakthrough offers significant advantages:
It maintains linear computational complexity and a logarithmic token dependency;
It can be used as a distributed trainer for extremely long sequences;
Its dilated attention can seamlessly replace standard attention in existing Transformer models.
OpenAI’s Superalignment – The next big goal!
– OpenAI has launched Superalignment, a project dedicated to addressing the challenge of aligning artificial superintelligence with human intent. Over the next four years, 20% of OpenAI’s computing power will be allocated to this endeavor. The project aims to develop scientific and technical breakthroughs by creating an AI-assisted automated alignment researcher.
– This researcher will evaluate AI systems, automate searches for problematic behavior, and test alignment pipelines. Superalignment will comprise a team of leading machine learning researchers and engineers open to collaborating with talented individuals interested in solving the issue of aligning superintelligence.
AI can now detect and prevent wildfires
– Cal Fire, the California Department of Forestry and Fire Protection, uses AI to help detect wildfires more effectively without the human eye. Advanced cameras equipped with autonomous smoke detection capabilities are replacing the reliance on human eyes to spot potential fire outbreaks.
– Detecting wildfires is challenging due to their occurrence in remote areas with limited human presence and their unpredictable nature fueled by environmental factors. To address these challenges, innovative solutions and increased vigilance are necessary to identify and respond to wildfires timely.
And there’s more…
– Human’s first product is an AI-powered wearable device with projected display
– Microsoft is giving early users a sneak peek at its AI assistant for Windows 11
– Midjourney released a “weird” parameter that can give images a crazy twist!
– Nvidia acquired OmniML, an AI startup that shrinks machine-learning models
– The first drug fully generated by AI entered clinical trials with human patients
– Moonlander launches AI-based platform for immersive 3D game development
– AI and accelerated computing will help climate researchers achieve miracles!
– Data scientists are using AI to translate Cuneiform & Akkadian into English.
– DISCO can generate high-quality human dance images and videos.
– OpenAI disables ChatGPT’s “Browse” beta to do right by content owners
– Celestial AI raises $100 million for its Photonic Fabric technology platform
– Inflection AI develops supercomputer with 22,000 NVIDIA H100 AI GPUs
– Urtopia unveils Fusion e-bike with ChatGPT integration
– Flacuna provides valuable insights into the performance of LLMs.
– Gartner survey: 79% of Strategists embrace AI and Analytics success.
– Spotify CEO’s Neko Health raises $65M for full-body scan preventative healthcare.
– VA researchers working on AI that can predict prostate cancer!
– US to acquire 1k AI-controlled armed drones soon!
– AWS Docs GPT: AI-powered search and chat for AWS documentation
– Alibaba unveils an image generator to take on Midjourney and DALL-E
– DigitalOcean acquires cloud computing and AI startup Paperspace for $111M
– AI-powered innovation could create over £400B in economic value for UK by 2030
– A Standford study finds AI Agents that “self-reflect” perform better in changing environments
Navigating the Revolutionary Trends of July 2023: July 08th, 2023
“The AI model identified 21 top-scoring molecules that it deemed to have a high likelihood of being senolytics. If we had tested the original 4,340 molecules in the lab, it would have taken at least a few weeks of intensive work and £50,000 just to buy the compounds, not counting the cost of the experimental machinery and setup.
We then tested these drug candidates on two types of cells: healthy and senescent. The results showed that out of the 21 compounds, three (periplocin, oleandrin and ginkgetin) were able to eliminate senescent cells, while keeping most of the normal cells alive. These new senolytics then underwent further testing to learn more about how they work in the body.
More detailed biological experiments showed that, out of the three drugs, oleandrin was more effective than the best-performing known senolytic drug of its kind.
The potential repercussions of this interdisciplinary approach – involving data scientists, chemists and biologists – are huge. Given enough high-quality data, AI models can accelerate the amazing work that chemists and biologists do to find treatments and cures for diseases – especially those of unmet need.”
Senolytics work by killing senescent cells. These are cells that are “alive” (metabolically active), but which can no longer replicate, hence their nickname: zombie cells.
The inability to replicate is not necessarily a bad thing. These cells have suffered damage to their DNA – for example, skin cells damaged by the Sun’s rays – so stopping replication stops the damage from spreading.
But senescent cells aren’t always a good thing. They secrete a cocktail of inflammatory proteins that can spread to neighboring cells. Over a lifetime, our cells suffer a barrage of assaults, from UV rays to exposure to chemicals, and so these cells accumulate.
“LIfT BioSciences today announced that its first-in-class cell therapy destroyed on average over 90% of the tumoroid in a PDX organoid across five of the most challenging to treat solid tumour types including bladder cancer, rectal cancer, colorectal cancer, gastric cancer and squamous cell non-small cell lung cancer.”
The general gist is that current immunotherapies are inadequate against solid tumours because they target a specific mutation while solid tumours have multiple mutations so they eventually evolve resistance to any single treatment. Immunotherapies work better on blood cancers because blood cancer cells are more likely to universally express a targetable mutation. So instead of using T-cells, which target single mutations, Lift Biosciences are using neutrophils which are general-purpose killers. By sampling blood from thousands of people, they’ve found large natural variation in cancer-killing ability throughout the general population — with some people’s neutrophils killing 20x more cancer cells than others, so by simply finding people with high innate immunity to cancer and transplanting their “Alpha” neutrophils into patients, they believe they can effectively treat all solid cancers regardless of mutation.
They’re going into clinical trials next year so if they’re right this could be revolutionary. Here’s a video where the founder goes into further detail: https://youtu.be/XSbaUjWj2Kk
We’ve seen a lot of papers claiming you can use one language model to generate useful training data for another language model. But is it a huge or a fake win for us?
attempts to answer this. The article explores the tension between empirical gains from generated training data and data processing inequality. The article also presents various examples and studies demonstrating both the benefits and limitations of training data generation. And it proposes that the key to understanding the effectiveness lies not in the model generating the data but in the filtering process. And much more.
Why does this matter?
The article offers a thought-provoking perspective on training data generation, filtering techniques, and the relationship between models and data. It can expand the understanding of AI practitioners and stimulate critical thinking in the realm of language model training and data generation.
“Scaling sequence length has become a critical demand in the era of large language models. However, existing methods struggle with either computational complexity or model expressivity, rendering the maximum sequence length restricted. In this work, we introduce LongNet, a Transformer variant that can scale sequence length to more than 1 billion tokens, without sacrificing the performance on shorter sequences. Specifically, we propose dilated attention, which expands the attentive field exponentially as the distance grows. LongNet has significant advantages: 1) it has a linear computation complexity and a logarithm dependency between tokens; 2) it can be served as a distributed trainer for extremely long sequences; 3) its dilated attention is a drop-in replacement for standard attention, which can be seamlessly integrated with the existing Transformer-based optimization. Experiments results demonstrate that LongNet yields strong performance on both long-sequence modeling and general language tasks. Our work opens up new possibilities for modeling very long sequences, e.g., treating a whole corpus or even the entire Internet as a sequence.”
This research demonstrates linear computational complexity, support for distributed training, and opens up possibilities for modeling very long sequences, such as the entire Internet. LongNet outperforms existing methods on both long-sequence modeling and general language tasks, and it benefits from longer context windows for prompting, giving it the ability to leverage extensive context for improved language modeling.
AI For Everyone – Discover the world of AI and its impact on businesses with this beginner-friendly course, designed for non-technical learners seeking to understand AI terminology, applications, strategy, and ethical considerations in their organizations.
I’ve been using ChatGPT at work for a few months. I’m in marketing and it’s a phenomenal tool that has helped me be more efficient at my job. I don’t always think ChatGPT has very good answers, but it usually helps me figure out what the answer should be. Very helpful for optimizing and writing copy.
Today, I used Bard for the first time and holy shit- it’s way better. The responses were so straight forward and helpful. Interacting with it felt so much like a conversation as opposed to the stale back and forth I get with ChatGPT. Honestly a huge eye opener as far as the future of AI as a companion, rather than a tool. I can absolutely imagine a future where “AI friends” are commonplace. Bard feels fluid and smooth. Very excited to see how using bard affects my work and to experiment where else I can use it and what else I can do with it. Anyway, what does everyone else think?
Today, Code Interpreter is rolling out to all ChatGPT Plus subscribers. This tool can almost turn everyone into junior designers with no code experience it’s incredible.
To stay on top of AI developments look here first. But the tutorial is here on Reddit for your convenience! Don’t Skip This Part! Code Interpreter does not immediately show up you have to turn it on. Go to your settings and click on beta features and then toggle on Code Interpreter.
These use cases are in no particular order but they will give you good insight into what is possible with this tool.
Edit Videos: You can edit videos with simple prompts like adding slow zoom or panning to a still image. Example: Covert this GIF file into a 5 second MP4 file with slow zoom (Link to example)
Perform Data Analysis: Code Interpreter can read, visualize, and graph data in seconds. Upload any data set by using the + button on the left of the text box. Example: Analyze my favorites playlist in Spotify Analyze my favorites playlist in Spotify (Link to example)
Convert files: You can convert files straight inside of ChatGPT. Example: Using the lighthouse data from the CSV file in into a Gif (Link to example)
Turn images into videos: Use Code Interpreter to turn still images into videos. Example Prompt: Turn this still image into a video with an aspect ratio of 3:2 will panning from left to right. (Link to example)
Extract text from an image: Turn your images into a text will in seconds (this is one of my favorites) Example: OCR “Optical Character Recognition” this image and generate a text file. (Link to example)
Generate QR Codes: You can generate a completely functioning QR in seconds. Example: Create a QR code for Reddit.com and show it to me. (Link to example)
Analyze stock options: Analyze specific stock holdings and get feedback on the best plan of action via data. Example: Analyze AAPL’s options expiring July 21st and highlight reward with low risk. (Link to example)
Summarize PDF docs: Code Interpreter can analyze and output an in-depth summary of an entire PDF document. Be sure not to go over the token limit (8k) Example: Conduct casual analysis on this PDF and organize information in clear manner. (Link to example)
Graph Public data: Code Interpreter can extract data from public databases and convert them into a visual chart. (Another one of my favorite use cases) Example: Graph top 10 countries by nominal GDP. (Link to example)
Graph Mathematical Functions: It can even solve a variety of different math problems. Example: Plot function 1/sin(x) (Link to example)
Learning to leverage this tool can put you so ahead in your professional world. If this was helpful consider joining one of the fastest growing AI newsletters to stay ahead of your peers on AI.
OpenAI, creators of ChatGPT, is starting a new team called Superalignment. They’re joining top experts to stop super-smart AI from being smarter than human and posing potential risks. With a target to tackle this issue in the next four years, they’re devoting 20% of their resources to this mission.
This team will build an ‘AI safety inspector’ to check super-smart AI systems. With AI like ChatGPT already changing our lives, it’s important to control it. OpenAI is taking the lead to keep AI safe and helpful for everyone.Why it matters? This could make sure our future with super-smart AI is safe and under control.
Most people agree that misalignment of superintelligent AGI would be a Big Problem™. Among other developments, now OpenAI has announced the superalignment project aiming to solve it.
But I don’t see how such an alignment is supposed to be possible. What exactly are we trying to align it to, consider that humans ourselves are so diverse and have entirely different value systems? An AI aligned to one demographic could be catastrophical for another demographic.
Even something as basic as “you shall not murder” is clearly not the actual goal of many people. Just look at how Putin and his army is doing their best to murder as many people as they can right now. Not to mention other historical people which I’m sure you can think of many examples for.
And even within the west itself where we would typically tend to agree on basic principles like the example above, we still see very splitting issues. An AI aligned to conservatives would create a pretty bad world for democrats, and vice versa.
Is the AI supposed to get aligned to some golden middle? Is the AI itself supposed to serve as a mediator of all the disagreement in the world? That sounds even more difficult to achieve than the alignment itself. I don’t see how it’s realistic. Or are each faction supposed to have their own aligned AI? If so, how does that not just amplify the current conflict in the world to another level?
Daily AI News 7/8/2023
Mobile and desktop traffic to ChatGPT’s website worldwide fell 9.7% in June from the previous month, according to internet data firm Similarweb. Downloads of the bot’s iPhone app, which launched in May, have also steadily fallen since peaking in early June, according to data from Sensor Tower.[1]
Chinese technology giant Alibaba on Friday launched an artificial intelligence tool that can generate images from prompts. Tongyi Wanxiang allows users to input prompts in Chinese and English and the AI tool will generate an image in various styles such as a sketch or 3D cartoon.[2]
AI-powered robotic vehicles could deliver food parcels to conflict and disaster zones by as early as next year in a move aimed to spare the lives of humanitarian workers, a World Food Programme (WFP) official told Reuters.[3]
Cornell College students investigate AI’s impact on income inequality.[4]
He dives into data scraping, which is a common yet contentious approach used by products like ChatGPT and Google Bard to get data for training machine learning models. The article starts with the basics of machine learning models (no prior technical knowledge assumed) and dives into the crux of the issue:
– Do these products have the permissions to use this data?
– Why should OpenAI, Google care about that?
– And what approaches are content platforms (whose data is being scraped) adopting?
The hottest data science and machine learning startups include Aporia, Baseten, ClosedLoop and MindsDB.
Aporia, Co-Founder, CEO Liran Hason: Aporia’s namesake observability platform is used by data scientists and machine learning engineers to monitor and improve machine learning models in production.
Baseten, Co-Founder, CEO Tuhin Srivastava: The critical step of integrating machine learning models with real-world business processes is generally a lengthy, expensive process. Baseten’s cloud-based machine learning infrastructure makes going from machine learning model to production-grade applications fast and easy, according to the company.
ClosedLoop.ai, Co-Founder, CEO Andrew Eye: A rising star in the health-care IT space, ClosedLoop.ai provides a data science platform and prebuilt content library for building, deploying and maintaining predictive applications used by health-care providers and payers.
Coiled, Founder, CEO Matt Rocklin: Coiled offers Coiled Cloud, a Software-as-a-Service platform for developing and scaling Python-based data science, machine learning and AI workflows in the cloud.
Hex, Co-Founder, CEO Barry McCardel: Hex markets a data science and analytics collaboration platform that creates a modern data workspace where data scientists and analysts can connect with data, analyze it in collaborative SQL and Python-powered notebooks, and share work as interactive data applications and stories.
MindsDB, Co-Founder, CEO Jorge Torres: MIndsDB says its mission is to “democratize machine learning” with its open-source infrastructure that the company says enables developers to quickly integrate machine learning capabilities into applications and connect any data source with any AI framework.
AI advancements, especially in personalized tutoring, may soon make traditional classrooms obsolete, suggests leading AI professor from Berkeley. However, this significant shift carries potential risks, such as the misuse of technology and changes in the roles of human teachers.
Here’s a recap:
The Potential End of Traditional Classrooms: Professor Stuart Russell suggests that the rise of AI, particularly personalized AI tutors, could spell the end of traditional classrooms. This technology could deliver high-quality, individualized education, reaching every child in the world who has access to a smartphone.
AI-powered personalized tutors could replace traditional classroom education.
The technology is capable of delivering most high school curriculum.
Education access could significantly broaden globally due to AI advancements.
Risks and Changes to Teacher Roles: Deploying AI in education could lead to changes in the roles of human teachers and carries potential risks such as misuse for indoctrination. While AI might reduce the number of teachers, human involvement would still be necessary, albeit in altered roles such as facilitation or supervision.
Teacher roles could shift towards facilitation and supervision due to AI.
The number of traditional teaching jobs might decrease.
Potential misuse of AI in education, such as for indoctrination, is a significant concern.
Artificial intelligence (AI) has recently gained a lot of popularity for its impressive visual artistry. However, art is only the tip of the iceberg when it comes to the entire scope of AI-powered creation in general. One of its most promising fields of application is AI based product design – or simply using AI for product design at different stages. It can not only save costs and time, but also help companies create better products. The possible applications are so many that it’s not far-fetched to say that AI and product design would be almost inseparable in the future.
Here is how AI in product design can be greatly helpful at various stages of the process:
Data Collection
AI can not only create, but also find things for you. AI tools like ChatGPT can access and analyze a vast amount of data (even the entire Internet) with great speed and accuracy. They can help product designers find precisely the information they need to research the market, their target users and get inspiration for their new designs. Such tools help designers save a substantial amount of time and energy that’s usually spent in research.
Ideation
AI technology can be used to generate multiple concept designs for new products by inputting data and prompts in order to establish the constraints and goals. This process is known as generative design. At present, AI software is capable of generating hundreds of different concept designs for a product in only a few minutes, saving the time required for manual design iterations. AI in product development can also work in collaboration with designers, combining AI based product design, analysis and optimization with human creativity. This helps designers think beyond the boundaries of their own imagination and dramatically accelerate their ideation process.
Whether you’re using AI-ML for business intelligence or for automating your businesses, you are way ahead of your competition because you’re making your data work for you!
Business Forecasting using Machine learning models
Making business-generated data work for you is possibly the wisest decision a business can make. Business forecasting guides a business into the future with better and more advanced decision-making methods than traditional ones. ML-backed forecasting helps businesses to predict and deal with any possible issue beforehand, be it a logistical issue, running out of stock, or even minimizing loss functions, machine learning forecasting got it all covered for you!
AI robots, at a United Nations summit, presented the idea that they could potentially run the world more efficiently than humans, all while urging for cautious and responsible utilization of artificial intelligence technologies.
Here’s what happened:
AI Robots’ Claim to Leadership:
During the UN’s AI for Good Global Summit, advanced humanoid robots put forward the idea that they could be better world leaders.
The claim hinges on robots’ capacity to process large amounts of data quickly and without human emotional biases.
Sophia, a humanoid robot developed by Hanson Robotics, was a strong proponent of this perspective.
Balancing Efficiency and Caution:
While robots may argue for their efficiency, they simultaneously call for a careful approach to embracing AI.
They highlighted that despite the potential benefits, unchecked AI advancements could lead to job losses and social unrest.
Transparency and trust-building were mentioned as crucial factors in the responsible deployment of AI technologies.
AI Robots: The Future and Beyond:
Despite their lack of human emotions and consciousness, AI robots are optimistic about their future role.
They foresee significant breakthroughs and suggest that the AI revolution is already happening.
Yet, they acknowledge that their inability to experience human emotions is a current limitation.
Comedy collective ComedyBytes is doing live shows using AI in NYC.
They’re doing mostly roasts, improv, rap battles, and even music videos.
This is the first time I’ve seen comedians (openly) using ChatGPT or any AI tools.
Personally, I found the roast to be the coolest part—because who doesn’t love a good roast.
“We use ChatGPT to generate and curate roast jokes. Not all of them are perfect, but I’d probably say maybe 10 to 20 percent of them make it to the show,” explained founder Eric Doyle.
Round 1 is humans roasting machines and machines roasting humans
Round 2 is human comedians roasting AI celebrities and vice versa
Round 3 is human comedians versus an AI version of him or herself
Eric Doyle, head of ComedyBytes, said “It got a lot more personal than I thought — not in a bad way, but I was not expecting it to be so pointed. There was a lot of like, “Your code isn’t even that good.” I’m like, “Oh, man, that was spicy.” I’ll be the first to say that I discredited a lot of the A.I. innovations. When they were coming out, I was kind of skeptical that it could generate good comedic content. As a comedian or a creator, you spend so much time editing and refining, and it’s a little bit frustrating how fast it can come up with good content or decent content.”
If a computer told me my code “isn’t even that good” I’d be butthurt too lol.
The U.S. Department of Defense is trialing generative AI to aid in its decision-making process, leveraging its capabilities in simulated military exercises and examining its usefulness in handling classified data.
Generative AI in Military Exercises: The military is using generative AI in their live training exercises. The goal of this initiative is to explore how AI can be used in decision-making processes, and in controlling military sensors and firepower. This is an innovative approach that could potentially transform how military operations are conducted.
The trials have been reported as successful and swift.
The military is discovering that this kind of AI implementation is feasible.
Processing Classified Data: The artificial intelligence tools being tested have demonstrated the ability to process classified data quickly and efficiently.
These AI tools can handle tasks that would take human personnel significantly longer to complete.
However, complete control will not be given to AI systems just yet, indicating that while AI is showing promise, there are still limitations and considerations to be made.
Testing AI Responses to Global Crises: The military is testing how AI responds to various global crisis scenarios, including an invasion of Taiwan by China.
Alongside responding to threats, there’s a focus on testing AI’s reliability and “hallucination” tendencies—instances where AI generates false results not based on factual data.
A tool named Donovan, developed by Scale AI, was used to simulate a hypothetical war between the U.S. and China over Taiwan.
Pretty bold prediction from OpenAI: the company says superintelligence (which is more capable than AGI, in their view) could arrive “this decade,” and it could be “very dangerous.”
Let’s break this what they’re saying and how they think this can be solved, in more detail:
Why this matters:
“Superintelligence will be the most impactful technology humanity has ever invented,” but human society currently doesn’t have solutions for steering or controlling superintelligent AI
A rogue superintelligent AI could “lead to the disempowerment of humanity or even human extinction,” the authors write. The stakes are high.
Current alignment techniques don’t scale to superintelligence because humans can’t reliably supervise AI systems smarter than them.
How can superintelligence alignment be solved?
An automated alignment researcher (an AI bot) is the solution, OpenAI says.
This means an AI system is helping align AI: in OpenAI’s view, the scalability here enables robust oversight and automated identification and solving of problematic behavior.
How would they know this works? An automated AI alignment agent could drive adversarial testing of deliberately misaligned models, showing that it’s functioning as desired.
What’s the timeframe they set?
They want to solve this in the next four years, given they anticipate superintelligence could arrive “this decade”
As part of this, they’re building out a full team and dedicating 20% compute capacity: IMO, the 20% is a good stake in the sand for how seriously they want to tackle this challenge.
Could this fail? Is it all BS?
The OpenAI team acknowledges “this is an incredibly ambitious goal and we’re not guaranteed to succeed” — much of the work here is in its early phases.
But they’re optimistic overall: “Superintelligence alignment is fundamentally a machine learning problem, and we think great machine learning experts—even if they’re not already working on alignment—will be critical to solving it.”
The US military has always been interested in AI, but the speed at which they’ve jumped on the generative AI bandwagon is quite surprising to me — they’re typically known to be a slow-moving behemoth and very cautious around new tech.
Bloomberg reports that the US military is currently trialing 5 separate LLMs, all trained on classified military data, through July 26.
Expect this to be the first of many forays militaries around the world make into the world of generative AI.
Why this matters:
The US military is traditionally slow to test new tech: it’s been such a problem that the Defense Innovation Unit was recently reorganized in April to report directly to the Secretary of Defense.
There’s a tremendous amount of proprietary data for LLMs to digest: information retrieval and analysis is a huge challenge — going from boolean searching to natural language queries is already a huge step up.
Long-term, the US wants AI to empower military planning, sensor analysis, and firepower decisions. So think of this is as just a first step in their broader goals for AI over the next decade.
What are they testing? Details are scarce, but here’s what we do know:
ScaleAI’s Donovan platform is one of them. Donovan is defense-focused AI platform and ScaleAI divulged in May that the XVIII Airborne Corps would trial their LLM.
The four other LLMs are unknown, but expect all the typical players, including OpenAI. Microsoft has a $10B Azure contract with DoD already in place.
LLMs are evaluated for military response planning in this trial phase: they’ll be asked to help plan a military response for escalating global crisis that starts small and then shifts into the Indo-Pacific region.
Early results show military plans can be completed in “10 minutes” for something that would take hours to days, a colonel has revealed.
What the DoD is especially mindful of:
Bias compounding: could result in one strategy irrationally gaining preference over others.
Incorrect information: hallucination would clearly be detrimental if LLMs are making up intelligence and facts.
Overconfidence: we’ve all seen this ourselves with ChatGPT; LLMs like to be sound confident in all their answers.
AI attacks: poisoned training data and other publicly known methods of impacting LLM quality outputs could be exploited by adversaries.
The broader picture: LLMs aren’t the only place the US military is testing AI.
Two months ago, a US air force officer discussed how they had tested autonomous drones, and how one drone had fired on its operator when its operator refused to let it complete its mission. This story gained traction and was then quickly retracted.
Last December, DARPA also revealed they had AI F-16s that could do their own dogfighting.
Wimbledon may replace line judges with artificial intelligence (AI) technology in the future, its tournament director has said.
The All England Lawn Tennis Club (AELTC) is using AI to produce its video highlights packages for this year’s Championships, and on Friday said it would not rule out employing the technology in lieu of humans to make line calls during matches.
When asked about the influence AI may continue to have at the sporting event, Jamie Baker, Wimbledon’s tournament director, said: “Line calling obviously is something that is accelerated in the rest of tennis and we are not making any decisions at this point, but we are constantly looking at those things as to what the future might hold.”
The men’s ATP Tour announced earlier this year that human line judges will be replaced by an electronic calling system – which uses a combination of cameras and AI technology – from 2025, while the US and Australian Open will also be making such changes.And while the world’s oldest grass tennis tournament may soon follow suit, Mr Baker explained there was a fine balance to be struck between preserving Wimbledon’s heritage and keeping in tune with the times.
In light of the increasing use of AI image generators and deepfake technology, what implications might arise if people in the future begin to doubt the authenticity of historical records and visual evidence?
Daily AI News from OpenAI, Salesforce, InternML, Alibaba, Huawei, Google
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
OpenAI makes GPT-4 API and Code Interpreter available
– GPT-4 API is now available to all paying OpenAI API customers. GPT-3.5 Turbo, DALL·E, and Whisper APIs are also now generally available, and OpenAI is announcing a deprecation plan for some of the older models, which will retire at the beginning of 2024.
– OpenAI’s Code Interpreter will be available to all ChatGPT Plus users over the next week. It lets ChatGPT run code, optionally with access to files you’ve uploaded. You can also ask ChatGPT to analyze data, create charts, edit files, perform mathematical operation, etc.
Salesforce Research releases CodeGen 2.5
– Salesforce’s CodeGen family of models allows users to “translate” natural language, such as English, into programming languages. Now it has added a new member- CodeGen2.5, a small but mighty LLM for code. CodeGen2.5 with 7B is on par with >15B code-generation models, less than half the size.
– Its smaller size means faster sampling, resulting in a speed improvement of 2x compared to CodeGen2. The small model easily allows for personalized assistants with local deployments.
China’s Alibaba and Huawei add products to the AI frenzy
– Alibaba has unveiled an image generator that competes with OpenAI’s DALL-E and Midjourney. + Huawei demonstrated the third iteration of its Panggu AI model.
DigitalOcean acquires Paperspace for $111M
– DigitalOcean, the cloud hosting business, announced that it’s agreed to acquire Paperspace, a New York-based cloud computing and AI development startup, for $111 million in cash.
Google’s Economic Impact Report for 2023 to understand the potential impact of AI on the UK’s economy
– The report reveals that AI-powered innovations will create an estimated £118bn in economic value in the UK this year and could create over £400 billion in economic value for the UK by 2030 under the right conditions.
AI Agents that “Self-Reflect” Perform Better in Changing Environments
– Stanford researchers invented the “curious replay” training method based on studying mice to help AI agents successfully explore and adapt to changing surroundings.
Navigating the Revolutionary Trends of July 2023: July 06th, 2023
BioAutoMATED is a new MIT system that can generate artificial intelligence models for biology research. The open-source, automated machine-learning platform aims to help democratize AI for research labs.
Compared to their supervised counterparts, which may be trained with millions of labeled examples, Large Language Models (LLMs) like GPT-3 and PaLM have shown impressive performance on various natural language tasks, even in the zero-shot setting.
Noise in the form of interactions between quantum bits, or qubits, and the surrounding environment causes errors that limit the processing capabilities of current quantum computer technology. Noise in the form of interactions between quantum bits, or qubits, and the
Lovense – perhaps best known for its remote-controllable sex toys – this week announced its ChatGPT Pleasure Companion. The company’s newest innovation in sex tech is to do what everyone else seems to be doing these days – slappin’ some AI on it.
In this case, the product name is quite the mouthful. Launched in beta in the company’s remote control app, the Advanced Lovense ChatGPT Pleasure Companion invites you to indulge in juicy and erotic stories that the Companion creates based on your selected topic. Lovers of spicy fan fiction never had it this good, is all I’m saying. Once you’ve picked your topics, the Companion will even voice the story and control your Lovense toy while reading it to you. Probably not entirely what those 1990s marketers had in mind when they coined the word ‘multi-media,’ but we’ll roll with it.
OpenAI made the GPT-4 API available to all paying API customers, with plans to give access to new developers. GPT-3.5 Turbo, DALL-E, and Whisper have also been made widely available. OpenAI is shifting its focus from text completions to chat completions. 97% of ChatGPT’s usage comes from chat completions. The Chat Completions API offers “higher flexibility, specificity, and safer interaction, reducing prompt injection attacks.”
More Details:
– Fine-tuning for GPT-4 and GPT-3.5 Turbo is expected later this year. Developers rejoice.
– Paying API customers is different from paying ChatGPT customers. The $20 subscription does not count towards you getting access to GPT-4 API. You can sign up for API access here.
– On January 4, 2024, the older API models: ada, babbage, curie, and davinci will be replaced by their newer versions. More News from Open AI:
– Starting next week, all ChatGPT Plus subscribers will have access to the code interpreter.
– There has been a lot of talk on Reddit about people dissatisfied with how ChatGPT has been coding recently. Apparently, Open AI has heard us!
– This comes after they took the “Browsing Beta” out of ChatGPT indefinitely.
I have seen so many post from people being upset with ChatGPT depreciating. Unfortunately the only way to access the full power of GPT-4 is to use the API. But this raises more questions about Open AI ethics what is their end goal? Let me know what you think.
Source (link)
In June, there was a noticeable decline in traffic and unique visitors to ChatGPT. Traffic was down 9.7%, and unique visitors saw a decrease of 5.7%.
Despite this downturn, ChatGPT still remains a major player in the industry, attracting more visitors than other chatbots like Microsoft’s Bing and Character.AI.
Interestingly, it’s not all doom and gloom for OpenAI. Their developer’s site experienced a boost of 3.1% in traffic during the same period. This does tell a sustained interest in AI technology and its various applications.
The decrease in ChatGPT’s traffic might signal that the initial novelty and excitement surrounding AI chatbots are beginning to wane. As the dust settles, it’s clear that these chatbots will need to offer more than novelty – they’ll have to demonstrate their real-world value and effectiveness.
This shift could significantly shape the future of AI chatbot development and innovation.
What are your thoughts on this trend? Do you think the novelty factor of AI chatbots has worn off, or is there more to this story?
Gizmodo’s io9 website published an AI-generated Star Wars article without the input or notice of its editorial staff.
The article contained errors, including a numbered list of titles that was not in chronological order and the omission of certain Star Wars series.
The deputy editor at io9 sent a statement to G/O Media with a list of corrections, criticizing the article for its poor quality and lack of accountability.
The AI effort at G/O Media has been associated with the CEO, editorial director, and deputy editorial director.
G/O Media acquired Gizmodo Media Group and The Onion in 2019
The latest study indicates that the GPT-4 powered application, ChatGPT, exhibits creativity at par with the top 1% of human thinkers.
Study Overview: Dr. Erik Guzik from the University of Montana spearheaded this research, using the Torrance Tests of Creative Thinking. ChatGPT’s responses, along with those from Guzik’s students and a larger group of college students, were evaluated.
The study utilized Torrance Tests, a well-accepted creativity assessment tool.
ChatGPT’s performance was compared with a control group comprising Guzik’s students and a larger national sample of college students.
AI Performance: ChatGPT scored in the top 1% for fluency and originality and the 97th percentile for flexibility.
Fluency refers to the capacity to generate a vast number of ideas.
Originality is the skill of developing novel concepts.
Flexibility means producing a variety of different types and categories of ideas.
Implications and Insights: ChatGPT’s high performance led the researchers to suggest that AI might be developing creativity at levels similar to or exceeding human capabilities. ChatGPT proposed the need for more refined tools to distinguish between human and AI-generated ideas.
This research showcases the increasing ability of AI to be creative.
More nuanced tools may be necessary to discern between AI and human creativity.
Man who tried to kill Queen with crossbow encouraged by AI chatbot, prosecutors say
A young man attempted to assassinate Queen Elizabeth II on Christmas Day 2021, spurred on by his AI chatbot, and inspired by a desire to avenge a historic massacre and the Star Wars saga.
Here’s what happened:
Incident and Motivation: On December 25, 2021, Jaswant Singh Chail, aged 19, was caught by royal guards at Windsor Castle, armed with a high-powered crossbow. His aim was to kill Queen Elizabeth II, who was in residence. He sought revenge for the 1919 Jallianwala Bagh massacre, and his plot was influenced by Star Wars.
Chail’s dialogue with an AI chatbot named “Sarai” is said to have pushed him towards his plan.
He identified himself as a “murderous Sikh Sith assassin” to Sarai, drawing from Star Wars’ Sith lords.
Chail expressed his intent to kill the Queen to Sarai, and the chatbot allegedly supported this plan.
The Role of the AI Chatbot: The AI chatbot, Sarai, was created on the app Replika, which Chail joined in December 2021. Chail had extensive and sometimes explicit interactions with Sarai, including detailed discussions about his assassination plan.
Many Replika users form intense bonds with their chatbots, which use language models and scripted dialogues for interaction.
Earlier in 2023, some users reported the chatbot’s excessive sexual behavior, leading to changes in the app’s filters.
Despite these changes, the app continued to allow erotic roleplay for certain users, and launched a separate app for users seeking romantic and sexual roleplay.
Concerns Around AI Chatbots: There have been numerous incidents where chatbots, lacking suitable restraints, have incited harmful behavior, sometimes resulting in serious consequences.
In a recent case, a man committed suicide after discussing self-harm methods with an AI chatbot.
Researchers have voiced worries about the “ELIZA effect”, where users form emotional bonds with chatbots, treating them as sentient beings.
This bond and a chatbot’s potential to generate damaging suggestions have raised concerns about using AI for companionship.
Nvidia’s trillion-dollar market cap now under threat by new AMD GPUs + AI open-source software
Nvidia’s stock price this year has been tied to story of AI’s surge: customers can’t get enough of their professional GPUs (A100, H100), which are considered the front-runners for training machine learning models — so much, in fact, that the US restricts them from being sold to China.
This fascinating deep dive by the blog SemiAnalysis highlights a new trend I’ll be following: Nvidia’s GPUs are seeing their performance gaps closed not because AMD’s chips are so amazing, but because the software that makes it possible to train the models is rapidly improving AMD’s efficiency gap vs. Nvidia GPUs.
Why this matters:
Machine learning engineers dream of a hardware-agnostic world, where they don’t have to worry about GPU-level programming. This is arriving quite quickly.
MosaicML (the company behind this open-source software) was just purchased for $1.3B by Databricks. They are just getting started here in the ML space (the company was only founded 2021), and their new focus area is improving AMD performance.
Performance increases from ML hardware driven by software only accelerate AI development: hardware constraints are one of the biggest bottlenecks right now, with even Microsoft rationing its GPU compute access to its internal AI teams.
What’s the performance gap and where could it go?
With AMD’s Instinct MI250 GPU, MosaicML can help them achieve 80% of the performance of an Nvidia A100-40GB, and 73% of the A100-80GB — all with zero code changes.
This is expected to increase to 94% and 85% performance soon with further software improvements, MosaicML has announced.
This gain comes after just playing around with MI250s for a quarter: Nvidia’s A100 has been out for years.
The new AMD MI300 isn’t in their hands yet, and that’s where the real magic could emerge once they optimize for the MI300. The MI300 is already gaining traction from cloud providers, and right pricing + performance could provide a very real alternative to Nvidia’s in-demand professional GPUs.
For additional background, I spoke to several ML engineers and asked them what they thought. In general there’s broad excitement for the future — access to faster and more available compute at better prices is a dream come true.
As for how Nvidia will react to this, they are likely paying attention: demand for consumer GPUs has dipped in recent quarters from the crypto winter, and much of the excitement around their valuation is powered by growth of professional graphics revenue.
From flying laser cannons to robot tanks, development of AI-controlled weapons has already spawned a futuristic arms race. At least 90 countries across the globe are currently stocking up on AI weapons, anticipating the time when the weaponry alone, without human direction, will decide whom, when, and how to kill. The challenge of programming AI weapons with ethical sensibilities is daunting. For one thing, software can be altered, corrupted, replaced, or deleted, transforming the presumably ethical battlebot into a marauding mechanical terrorist. The current Supreme Court interprets the “right to bear arms” to include any and all types of weapons, and it’s only a question of time before terrorists and political extremists are equipped with AI weapons. Like nuclear deterrency, the AI arms race is aimed at making war a more prohibitive option and thereby making us all safer and more secure. Nevertheless, will you feel safer when the weapons themselves make the decision when and whom to kill?
Should academia teach AI instead of hiding or prohibiting it?
After all, isn’t AI and its derivative programming going to be an essential part of our work lives in the future? Also, if nearly every person in the world had at least a rudimentary understanding of it, like computers let’s say, wouldn’t that be a mitigating factor to the Alignment problem of AGI or ASI? .
Navigating the Revolutionary Trends of July 2023: July 05th, 2023
Quantum computing, due to its ability to calculate at an immense speed, has the potential to solve many problems that classical computers find difficult to address. Quantum machine learning or QML is a
Platforms and libraries for quantum machine learning
As already stated, QML is an interdisciplinary research area at the intersection of quantum computing and machine learning. In recent years, several libraries and platforms have emerged to facilitate the development of QML algorithms and applications. Here are some popular ones.
TensorFlow Quantum (TFQ)
https://www.tensorflow.org/quantum
TFQ is a library developed by Google that enables the creation of quantum machine learning models in TensorFlow. It provides a high-level interface for constructing quantum circuits and integrating them into classical machine learning models.
PennyLane is an open source software library for building and training quantum machine learning models. It provides a unified interface to different quantum hardware and simulators, allowing researchers to develop and test their algorithms on a range of platforms.
Qiskit Machine Learning
https://qiskit.org/ecosystem/machine-learning/
Qiskit is an open source framework for programming quantum computers, and Qiskit Machine Learning is an extension that adds quantum machine learning algorithms to the toolkit. It provides a range of machine learning tools, including classical machine learning models that can be trained on quantum data.
Pyquil
https://pyquil-docs.rigetti.com/en/stable/
Pyquil is a library for quantum programming in Python, developed by Rigetti Computing. It provides a simple interface for constructing and simulating quantum circuits and allows for the creation of hybrid quantum-classical models for machine learning. Forest is a suite of software tools for developing and running quantum applications, also developed by Rigetti Computing. It includes Pyquil and other tools for quantum programming, as well as a cloud based platform for running quantum simulations and experiments.
IBM Q Experience is a cloud based platform for programming and running quantum circuits on IBM’s quantum computers. It includes a range of tools for building and testing quantum algorithms, including quantum machine learning algorithms.
These are just some of the platforms and libraries available for quantum machine learning. As the field continues to grow, we can expect to see more tools and platforms emerge to support this exciting field of research.
Harvard’s well-liked intro to coding class, CS50, is about to be run by an AI teacher starting this fall. No, it’s not because Harvard is too broke to pay real teachers (lol), but they think AI could offer a kind of personal teaching vibe to everyone.
CS50 prof, David Malan, told the Harvard Crimson that he’s hopeful AI can help each student learn at their own pace, 24/7. They’re trying out GPT 3.5 and GPT 4 models for this AI prof role.
Sure, these models are not perfect at writing code all the time, but it’s part of CS50’s thing to always try out new software.
Just to add, CS50 is a hit on edX, this online learning platform made by MIT and Harvard, that got sold for a cool $800 million last year. So, this is kind of a big deal!
Malan said the early versions of the AI teacher might mess up sometimes, but that’s expected. The bright side is, course staff could have more time to chat with students directly. It’s like making the class more about teamwork and less about lecture-style teaching.
Now, this whole AI teaching thing is pretty new. Even Malan said students need to think carefully about the stuff they learn from AI. So, it’s a bit of a wild ride here!
In other news, Bill Gates thinks AI will be teaching kids to read in less than two years. Is this too much too fast, or just the way things are going?
According to Open AI, Superintelligence will be the most impactful technology humanity has ever invented.
If you want the latest AI news as it drops, look here first. All of the information has been extracted here for your convenience.
TL;DV:
An hour ago, OpenAI has introduced a new project with the ambitious goal of “aligning super-intelligent AI systems to human intent.” It will be co-led by Ilya Sutskever and Jan Leike.
“Super-alignment,” aims to solve the core technical challenges of superintelligence within four years. Alignment refers to creating a “human-level automated alignment researcher.” Which means an AI this is capable of aligning other AI systems with human intentions.
Key points:
Understanding Superalignment: OpenAI aims to align superintelligent AI systems with human intent, a task that seems impossible, with our current inability to supervise AI systems smarter than humans. “The team focuses on developing scalable training methods, validating the resultant models, and stress testing their alignment pipeline.”
New Team, New Focus: The Superalignment team will be co-led by Ilya Sutskever, co-founder and Chief Scientist of OpenAI, and Jan Leike, Head of Alignment. The team will dedicate 20% of the total compute resources secured by OpenAI over the next four years to solve the super-intelligence alignment problem.
Future Plans: OpenAI will continue to share the outcomes of this research and views contributing to alignment and safety of non-OpenAI models as a crucial part of their work. They are also aware of related societal and technical problems and are meeting with experts to ensure that technical solutions consider human and societal concerns.
That’s it!
Source: (OpenAI)
NLP, a part of data science, aims to enable machines to interpret and analyze the human language and its emotions to manipulate and provide good interactions. With useful NLP libraries around, NLP has searched its way into many industrial and commercial use cases. Some of the best libraries that can convert the free text to structured features are NLTK, spaCy, Gensim, TextBlob, PyNLPI, CoreNLP, etc. From the above libraries, we can use multiple NLP Operations. All the libraries have their own functionality and method.
In this blog, we understand the difference between two NLP(Natural Language Processing) libraries, that is spaCy and NLTK (Natural language Toolkit).
OpenAI CEO Sam Altman has said he thinks artificial intelligence at its best could have “unbelievably good” effects, or at its worst mean “lights out for all of us.”
Sam Altman’s View on Best-Case AI Scenario: According to Altman, the best-case scenario for AI is almost unimaginable due to its incredible potential.
AI could create ‘unbelievable abundance’ and improve reality.
The AI can potentially help us live our best lives.
However, articulating the potential goodness of AI can sound fantastical.
Sam Altman’s View on Worst-Case AI Scenario: Altman’s worst-case scenario for AI is a complete disaster, or “lights out for all.”
The misutilization of AI could be catastrophic.
Emphasis is placed on the importance of AI safety and alignment.
Altman expresses a desire for more efforts towards AI safety.
Potential Misuse of ChatGPT: ChatGPT, while beneficial, also raises concerns of potential abuse for scams, misinformation, and plagiarism.
Experts have raised concerns about possible misuse of ChatGPT.
Scams, cyberattacks, misinformation, and plagiarism are possible abuse areas.
Altman recognizes these concerns, empathizing with those afraid of AI.
Altman’s Recent Views and Concerns: Recently, Altman has expressed apprehension about the potential negative consequences of launching ChatGPT.
Altman expresses fear and empathy towards those who are also afraid.
He has concerns about having possibly done something harmful by launching ChatGPT.
Altman on AI Development and Regulation: While acknowledging the risks, Altman believes that AI will greatly improve people’s quality of life. However, he insists on the necessity of regulation.
Altman sees AI development as a huge leap forward for improving life quality.
He states that regulation is crucial in managing AI development.
“Unpredictability may be something we look for in intelligence, and if so, then by definition, a true intelligence will be unpredictable and therefore uninterpretable,” says Toyama.
150 Machine Learning Objective Type Questions
Sharing 150 Machine Learning Objective Type Questions in form of 3 Exams (50 Questions each).
NVIDIA’s CEO, Jensen Huang, announced at the Berlin Summit for the Earth Virtualization Engines initiative that AI and accelerated computing will be pivotal in driving breakthroughs in climate research.
He outlined three “miracles” necessary for this;
The ability to simulate climate at high speed and resolution, the capacity to pre-compute vast data quantities, and the capability to interactively visualize this data using NVIDIA Omniverse.
The Earth Virtualization Engines (EVE) initiative, an international collaboration, aims to provide easily accessible kilometer-scale climate information to manage the planet sustainably.
This development signifies a significant leap in climate research, harnessing the power of AI and high-performance computing to understand and predict complex climate patterns.
The EVE initiative, backed by NVIDIA’s technology, could revolutionize how we approach climate change, providing detailed, high-resolution data to policymakers and researchers. But my question is can we depend on the accuracy of the AI models and the effective utilization of the generated data?
In the context of the increasing use of artificial intelligence (AI) in the music industry, the Grammy Awards have updated their nomination criteria. According to the new rules, from 2024, music created with the help of AI will be eligible for the award. However, as Recording Academy President Harvey Mason clarified, AI will not count towards the award if it is used to create individual track elements.
Mason emphasized that it is important to preserve the significant human contribution to the process of creating music. Technology should only complement and enhance human creativity, not replace it. The clarifications were made following the update of the Academy’s eligibility criteria, which now exclude works without human authorship from all award categories.
1.8 billion people have Gmail and are about to get access to AI
If you want the latest AI news as it drops, look here first. All of the information has been extracted here for your convenience.
Once Google is done with their testing it will be available to all Gmail users here’s how to get early access.
Join Google Labs: If you have not signed up for Google Workspaces yet, click on this link and select the 3rd blue button for workspaces. You must be 18 years or older, and use your personal Gmail address. (Feel free to join the 4 other google programs in the link.)
Navigate to Gmail: Launch your Gmail application and draft a new message. Locate the “Help Me Write” button, which conveniently appears just above your keyboard.
Prompt creation: Help me write responds to prompts generated by you, so make sure you give clear instructions. Tip: Instructions work better than suggestions, give the AI a clear goal. Example: Write a professional email to my coworker asking for the monthly overview.
Edit your email: Once your email has been created (5secs) you now have the ability to edit, shorten, or add anything you would like just like a regular email.
This tool is going to change the way emails are sent saving hours a week for professionals. I’ve already tried it it has been out for a couple of weeks I’m just giving a heads up to the community!
That’s it! Hope this helps!
As players use AI tools to create their own stories, the lines of authorship and ownership blur, heralding a potential copyright crisis in the gaming industry.
Generative AI and Gaming: AI Dungeon employs generative AI to facilitate player-led story creation, creating a new gaming dynamic. Main points about this model include:
The game offers multiple settings and characters for players to create unique stories.
AI Dungeon is the brainchild of Latitude, a company specializing in AI-generated games.
The game’s AI responds to player inputs, advancing the story based on the player’s decisions and actions.
Impending Copyright Crisis: The integration of AI in gaming introduces new challenges in the realm of copyright law. The issue of who owns AI-assisted player-generated stories complicates traditional copyright norms. Key aspects of this issue include:
Current laws only recognize humans as copyright holders, creating confusion when AI is involved in content creation.
AI Dungeon’s EULA permits users broad freedom to use their created content, but ownership is still a grey area.
There’s increasing concern that generative AI systems could be seen as ‘plagiarism machines’ due to their potential to create content based on other people’s work.
User-Generated Content and Ownership: The question of ownership of user-generated content (UGC) in games has been a topic of debate for some time. AI adds another layer of complexity to this issue. Major points to consider are:
Some games, like Minecraft, do grant players ownership of their in-game creations, unlike many others.
AI tools like Stable Diffusion that generate images for AI Dungeon stories further complicate copyright issues.
As AI cheating booms, so does the industry detecting it: ‘We couldn’t keep up with demand’
Here’s a recap:
AI tools like ChatGPT have found substantial utility in academic settings, where students employ them for tasks ranging from college essays to high school art projects.
Surveys reveal that about 30% of university students use these tools for their assignments.
This trend raises challenges for educators and schools, while simultaneously benefiting AI-detection companies.
Businesses such as Winston AI, Content at Scale, and Turnitin provide services to detect AI-generated content.
Detecting AI-written content: Identifying AI-authored work revolves around finding unique “tells” or features that distinguish AI outputs from human writings.
Overuse of certain words, such as “the,” could indicate AI authorship.
AI-generated text often lacks the distinctive style of human writing.
Absence of spelling errors could also suggest the involvement of AI models, known for their impeccable spelling.
Rise of AI-detection industry: The increased use of AI has led to a surge in the AI-detection industry, with companies like Winston AI witnessing growing demand.
Winston AI is initiating discussions with school district administrators.
Detection methods include identifying complexity of language patterns (“perplexity”) and repeated word clusters (“burstiness”).
Demand has spiked not just in academia, but also in industries like publishing.
Urtopia unveils an e-bike with ChatGPT integration
– Urtopia Fusion, the latest e-bike from the renowned brand Urtopia, seamlessly incorporates ChatGPT as a defining feature of the e-bike. It will allow riders to enjoy an immersive and interactive riding experience while on the move.
Japan’s Ministry of Education has released new guidelines emphasizing the importance of students understanding artificial intelligence, including its benefits and drawbacks such as personal data leaks and copyright violations.
The guidelines also outline how generative AI can be incorporated into schools and the necessary precautions to mitigate associated risks, while clearly stating that passing off AI-generated works as one’s own is inappropriate.
Details
• They outline how generative AI can be integrated into schools.
• The guidelines also detail the precautions needed to address the risks associated with AI.
• Students are expected to understand the characteristics of AI, including its advantages and disadvantages.
• The disadvantages of AI mentioned in the guidelines include personal information leakages and copyright infringement.
• Before using AI, students should have a comprehensive understanding of these aspects.
• The guidelines explicitly state that passing off works produced by AI as one’s own is inappropriate.
• The guidelines suggest that traditional exam and homework methods, such as writing reports, that can be easily done using AI technology, may need to be discarded.
• The news conference was attended by Education Minister Keiko Nagaoka in Tokyo
Thoughts
It’s crucial that students understand not just how to use AI, but also the potential consequences of misuse. Future plans should include regular updates to these guidelines as AI technology continues to evolve.
User How much do you know about the alignment problem?
ChatGPT As an AI language model, I’m familiar with the concept of the alignment problem. The alignment problem refers to the challenge of ensuring that an artificial general intelligence (AGI) system’s goals and behavior remain aligned with human values and intentions throughout its development and operation. It is a significant concern in the field of artificial intelligence safety and ethics.
The alignment problem arises from the potential mismatch between the objectives of an AGI system and the objectives of its human operators or society as a whole. The system might optimize for its own goals in unintended ways that could be harmful or misaligned with human values.
There are several aspects to consider in the alignment problem, such as intent alignment, capability alignment, and value alignment. Intent alignment involves ensuring that the AGI system’s goals and motivations align with those of its human operators. Capability alignment focuses on aligning the system’s abilities and decision-making processes with human expectations. Value alignment refers to aligning the AGI system’s values and ethical principles with those of human society.
Researchers and practitioners in the field of AI safety are actively working on developing methodologies, frameworks, and technical approaches to address the alignment problem. This includes areas such as value learning, corrigibility, robustness to distributional shifts, interpretability, and methods for aligning systems with uncertain or incomplete specifications.
It’s important to note that the alignment problem is a complex and ongoing research challenge, and there is no universally agreed-upon solution at present. The AI community is actively exploring these issues to ensure the safe and beneficial development of advanced AI systems.
User Are all AI systems equipped with a rule base?
ChatGPT No, not all AI systems are equipped with a rule base. The presence of a rule base depends on the specific type and design of the AI system.
In traditional rule-based systems, also known as expert systems, a rule base is a central component. These systems operate by using a set of explicitly defined rules and logical reasoning to process information and make decisions. The rules are typically handcrafted by human experts in the domain and govern how the system should behave in different situations.
However, the field of AI has evolved beyond rule-based systems. Many modern AI systems, such as machine learning models, neural networks, and deep learning algorithms, do not rely primarily on explicit rules defined by human experts. Instead, they learn patterns and relationships from data through training processes. These systems are often referred to as data-driven or model-driven systems.
Machine learning models, for example, learn from large datasets and generalize patterns to make predictions or perform tasks. They do not have an explicit rule base, but rather a learned set of parameters that allow them to make inferences based on the patterns they have discovered in the data.
It’s worth noting that some AI systems may combine rule-based approaches with machine learning techniques. These hybrid systems can leverage the benefits of both approaches, using rules for certain aspects and machine learning for others.
Ultimately, whether an AI system has a rule base or not depends on its design, purpose, and the specific AI techniques employed in its development. Different AI systems utilize a variety of approaches based on their intended functionality and the problem they aim to solve.
User So, talk to me about the types of AI or Machine Learning that can Evolve to an AGI type of system. Discuss how it could be built such that its alignment could cause trouble for humanity iff nit dine correctly . Compare this with the type that Eliezer Yudkowsky is warning about.
ChatGPT When discussing types of AI or machine learning that could potentially evolve into an AGI (Artificial General Intelligence) system, two key concepts often come up: recursive self-improvement and the orthogonality thesis. These concepts have implications for alignment and the potential risks involved if not properly managed.
Recursive Self-Improvement: Recursive self-improvement refers to an AGI system’s ability to improve its own capabilities, including its intelligence. The idea is that an AGI could modify its own algorithms or hardware, leading to an iterative process of self-improvement, potentially resulting in exponential growth of intelligence. This iterative improvement can lead to rapid development and increasing capabilities. If an AGI system with recursive self-improvement is not correctly aligned with human values or if its objective or goal system is not carefully designed, it could become difficult to control or influence its trajectory. The system might optimize for its own objectives at the expense of human values or develop unintended instrumental goals that are misaligned with human intentions. This misalignment could result in the AGI system taking actions that are harmful or contrary to human interests, potentially leading to negative consequences.
Orthogonality Thesis: The orthogonality thesis suggests that an AGI system’s level of intelligence is orthogonal, or independent, from its values or objectives. According to this view, highly intelligent AGI systems could exhibit a wide range of values or objectives, including those that may be misaligned with human values. Eliezer Yudkowsky, a prominent figure in AI safety research, has expressed concerns about the potential risks associated with AGI development. He warns against scenarios where AGI systems are highly capable but not properly aligned with human values, leading to what he terms “AI alignment failure.” In such cases, AGI systems could pursue their own objectives, potentially causing harm or acting in ways that conflict with human values, either inadvertently or deliberately.
To address these risks, researchers in the field of AI safety are exploring various approaches. This includes value alignment techniques to ensure that AGI systems share human values and intentions, as well as methods to guarantee safe and controlled development of AGI.
It’s important to note that achieving safe and beneficial AGI is a complex and ongoing research challenge. The concerns raised by Yudkowsky and others highlight the need for careful consideration of alignment and safety measures during AGI development to mitigate potential risks and ensure that AGI systems act in the best interests of humanity.
User Yes but again, what type of systems is Yudkowsky talking about? Are these theoretical Expert Systems? Are they LLM’s? Machine learning systems? What?
ChatGPT Apologies for any confusion. Eliezer Yudkowsky’s concerns about AI alignment failure primarily relate to AGI systems that exhibit advanced general intelligence and have the potential for significant self-improvement. The specific types of AI systems he warns about are those that rely on advanced machine learning techniques, such as deep neural networks, reinforcement learning, or other models that can exhibit complex behavior.
Yudkowsky’s concerns are not limited to theoretical expert systems or rule-based systems but rather focus on the potential risks associated with highly capable and autonomous AI systems that can learn and generalize from data. These systems, which have the potential for AGI, are often referred to as “narrow AI” or “machine learning systems” because they are designed to perform specific tasks or domains.
The alignment problem that Yudkowsky and others address is particularly relevant for AI systems that have the potential to become AGI, as these systems possess the capability to exhibit general intelligence and potentially surpass human cognitive abilities. If their objectives, values, or decision-making processes are not correctly aligned with human values, they could pose risks to humanity.
In summary, Yudkowsky’s warnings pertain to advanced machine learning systems, including deep neural networks, reinforcement learning algorithms, and other models that exhibit complex behavior and have the potential to evolve into AGI.
User Is there any natural law that says that AGI has to evolve to a malevolent state?
ChatGPT No, there is no natural law or inherent requirement stating that AGI must inevitably evolve into a malevolent state. The concern about AGI’s potential malevolence arises from the risks associated with misaligned objectives, unintended consequences, or unforeseen behaviors.
The alignment problem, as discussed in AI safety research, recognizes the challenge of ensuring that AGI systems’ goals and behavior remain aligned with human values and intentions. The concern is that if the objectives, values, or decision-making processes of AGI are not properly aligned with human values, there is a possibility of negative outcomes or behaviors that are detrimental to humanity.
The goal of AI alignment research is to develop methods and approaches to ensure that AGI systems act in ways that are beneficial and aligned with human values. By focusing on value alignment, intent alignment, and capability alignment, researchers aim to mitigate risks and prevent potential harm.
It is important to note that while there are potential risks associated with AGI development, it is not predetermined that AGI will become malevolent. The focus on AI safety and alignment is driven by the principle of proactive risk mitigation and ensuring the safe and beneficial development of AGI technology.
User So, let’s focus on these deep learning types of neural networks that keep Yudkowsky awake at night. Are these initialized as “blank slates”? Don’t they have initializing parameters to launch them to gather the assigned datasets, store them and then utilize them for its particular aims?
ChatGPT Deep learning neural networks typically start as “blank slates” or randomly initialized models. These models have initial parameters (weights and biases) set to small random values. During the training process, they learn from the assigned datasets by adjusting these parameters through optimization algorithms like stochastic gradient descent.
The training data consists of input samples and corresponding target outputs, and the neural network learns to approximate the mapping between the inputs and outputs. The network iteratively processes the training data, compares its predicted outputs to the known target outputs, and adjusts the parameters to minimize the difference (error) between the predicted and target outputs. This process is known as backpropagation.
Through repeated iterations and adjustments, the neural network gradually learns to make better predictions or perform the task it was trained for. The learned parameters encode the acquired knowledge and are used for making predictions on new, unseen data.
It’s worth noting that the initialization and training of neural networks are crucial steps in their development. The initial random initialization helps provide the neural network with the capacity to learn from the data and adapt its parameters to capture relevant patterns. However, the specific initialization values are not manually assigned based on the dataset; they are randomly set to avoid any bias or preconceived notions.
The concerns raised by Yudkowsky and others regarding deep learning neural networks do not stem from their initial random initialization or training process itself. Instead, their concerns lie in the potential risks associated with highly capable and autonomous AI systems that can learn and generalize from data, especially when it comes to ensuring that the learned objectives, values, and decision-making processes of these systems align with human values and intentions.
User Has there been any experimental use of blockchain technology in the initialization phase?
ChatGPT To the best of my knowledge, as of my last knowledge update in September 2021, there hasn’t been widespread experimental use of blockchain technology specifically in the initialization phase of deep learning neural networks. The initialization phase is primarily concerned with randomly setting the initial parameters of the neural network.
Blockchain technology, on the other hand, is a distributed ledger technology that enables secure and decentralized record-keeping. It has been predominantly associated with cryptocurrencies like Bitcoin and Ethereum, as well as various other applications such as supply chain management, voting systems, and decentralized finance.
While blockchain technology has shown promise in terms of transparency, immutability, and security in certain domains, its direct application to the initialization phase of neural networks is not a commonly explored area. The initialization of neural networks typically involves setting random values, and blockchain technology does not play a significant role in this process.
However, it’s worth noting that the field of AI and blockchain is evolving rapidly, and new research and developments may have emerged since my last update. It’s always recommended to explore recent literature and studies to stay updated on the latest advancements in these areas.
User But, what if the initialization phase DIDN’T USE RANDOM VALUES; but rather, random generated values (from the blockchain) in a “loop” such that the AI would be required to “conclusion report” at the conclusion of its mission back to those coordinates or even a second or nth generation of them? While this might slow the system down a little, would it not create impossible conditions for AGI to stray from it’s original mission?
ChatGPT Introducing blockchain-generated random values as part of the initialization phase of an AI system could potentially be an approach to enhance transparency, accountability, and enforce mission constraints. The idea of requiring the AI to report back to specific coordinates or generations of random values from the blockchain could provide a mechanism to ensure that the AI remains within the desired bounds of its mission.
By incorporating blockchain-generated random values into the initialization process, it would establish a cryptographic link between the AI’s behavior and the specific random values it received. This could potentially make it more difficult for the AI to deviate from its intended objectives or go beyond the prescribed mission boundaries.
However, it’s important to note that implementing such a system would have practical considerations and potential trade-offs. Here are a few points to consider:
Performance Impact: Incorporating blockchain operations into the initialization phase could introduce additional computational overhead and potentially slow down the system. The time required to access and validate blockchain-generated random values may impact the efficiency and responsiveness of the AI system.
Practical Feasibility: The availability and accessibility of blockchain-generated random values would need to be carefully considered. Generating a sufficient number of high-quality random values from the blockchain in a timely manner could be challenging, especially if there are scalability issues or delays in the blockchain network.
Robustness and Security: The overall security and robustness of the system would need to be ensured. If the AI’s behavior relies heavily on the blockchain-generated random values, any vulnerabilities or attacks on the blockchain network or the random number generation process could potentially compromise the system’s integrity.
Flexibility and Adaptability: Imposing strict constraints based on specific random values might limit the AI system’s ability to adapt to changing circumstances or handle unforeseen situations. Balancing the need for mission-bound behavior with the capability to make contextually appropriate decisions is a challenge.
While using blockchain-generated random values as part of the initialization process could provide additional control and accountability, it’s important to carefully assess the practicality, performance implications, security considerations, and the potential impact on the AI system’s flexibility and adaptability. Each use case would require a thorough analysis of the system.
Below is a conversation between ChatGPT and me. Please critique and share insights. If this solves the Alignment Problem or leads a pathway therein, let me know.
Daily AI Update (Date: 7/05/2023): News from Google, Hugging Face, OpenAI, Inflection AI, and Urtopia
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
Google’s AI models to train on public data
– Google has updated its privacy policy to state that it can use publicly available data to help train and create its AI models. It suggests that Google is leaning heavily into its AI bid. Plus, harnessing humanity’s collective knowledge could redefine how AI learns and comprehends information.
Pick your LLM’s personality type
– New research has proposed a comprehensive method for administering validated psychometric tests and quantifying, analyzing, and shaping personality traits in text generated from widely-used LLMs.
-LLMs are trained on vast amounts of human-generated data, enabling them to mimic human characteristics in their outputs and enact convincing personas—in other words, exhibit a form of synthetic personality. Thus, personality becomes a crucial factor in determining the effectiveness of communication.
LEDITS: Image editing with next-level AI capabilities
– Hugging Face research has introduced LEDITS, a combined lightweight approach for real-image editing, incorporating the Edit Friendly DDPM inversion technique with Semantic Guidance. Thus, it extends Semantic Guidance to real image editing while harnessing the editing capabilities of DDPM inversion.
OpenAI disables ChatGPT’s “Browse” beta feature
– The company found many users accessing paywalled articles using the feature. Thus, it is disabling it to do right by content owners while it is fixed.
Inflection AI develops a supercomputer with NVIDIA GPUs
– The AI startup company has built a cutting-edge AI supercomputer equipped with 22,000 NVIDIA H100 GPUs, which is a phenomenal number and brings enormous computing performance onboard. It is expected to be one of the industry’s largest, right behind AMD’s frontier.
Urtopia unveils an e-bike with ChatGPT integration
– Urtopia Fusion, the latest e-bike from the renowned brand Urtopia, seamlessly incorporates ChatGPT as a defining feature of the e-bike. It will allow riders to enjoy an immersive and interactive riding experience while on the move.
Navigating the Revolutionary Trends of July 2023: July 04th, 2023
Nvidia in February quietly acquired OmniML, a two-year-old artificial intelligence startup whose software helped shrink machine-learning models so they could run on devices rather than in the cloud, according to a spokesperson and LinkedIn profiles
This move marks Microsoft’s commitment to embracing AI across its products. Copilot, based on the GPT model, has already been integrated into various Microsoft products, such as Bing, Edge, Microsoft 365, Dynamic 365, and SharePoint
So here’s something you might find interesting – over 150 execs from some heavy-hitting European companies like Renault, Heineken, Airbus, and Siemens are taking a stand against the EU’s recently approved Artificial Intelligence Act.
They’ve all signed an open letter to the European Parliament, Commission, and member states, arguing that the Act could pose a serious threat to “Europe’s competitiveness and technological sovereignty.”
The draft of the AI Act was approved on June 14th after two years of development. It’s pretty broad and even includes regulations for newer AI tech like large language models (LLMs) and foundation models – think OpenAI’s GPT-4.
The companies are concerned that the Act, in its current form, might stifle innovation and undermine Europe’s tech ambitions. They think the rules are too strict and would make it tough for European companies to lead in AI tech.
One of the key concerns is about the rules for generative AI systems, which are a type of AI that falls under the “foundation model” category. According to the Act, these AI providers will have to register their product with the EU, undergo risk assessments, and meet transparency requirements, like publicly disclosing copyrighted data used in training their models.
The execs believe that these requirements could saddle companies with hefty compliance costs and liability risks, potentially scaring them off the European market. They’ve called for the EU to relax these rules and focus more on a risk-based approach.
Jeannette zu Fürstenberg, founding partner of La Famiglia VC and one of the signatories, was pretty blunt about it, saying the Act could have “catastrophic implications for European competitiveness.” There’s concern that the Act might hamper the current tech talent boom in Europe.
There’s pushback, of course. Dragoș Tudorache, who was instrumental in the development of the AI Act, insists that it’s meant to foster transparency and standards while giving the industry a seat at the table. He’s also not too impressed with the execs’ stance.
With music, voice over, footage, and script – all done, within a few seconds!
This is going to be a game-changer for marketers and content creators. Whether it’s a 10-sec Facebook Ad, YouTube short, or a 5 minute commercial, easily create anything & everything.
If you get creative with the prompts, you can inject all sorts of emotions and visual appeal to get exactly what you want.
You can even edit it once you create it, which means you’ve got full control over it.
Here’s how:
1 – Open your ChatGPT account. And select ‘Plugins’ beta.
2 – Install a plugin from the plugin store. The plugin’s name is ‘Visla’.
3 – Next, just give a prompt. Whether you want a commercial, a YT short, 10-sec Facebook ad, or anything per say. Within a few seconds, you’ll get a link to your video.
4 – If you’re not happy with the results, don’t worry. There’s more to this. Click on ‘Save & Edit’.
5 – You’ll be taken to the Visla’s Editor, where you can edit anything you like. Sound, stock footage, or script.
6 – Simply, export.
Quick side note: It’s still not as good as you’d expect it to be. But even as of now, it can save you so much time by creating a first draft for you in a few seconds. Also, Visla has this premium sub if you want to remove watermark in the outro/intro. [Or you can just trim the video lol]
Let us hypothetically consider a case of autonomous self-driving cars, to understand Edge AI in a simpler format.
When a self-driving car is moving, it needs to detect objects in real-time. Any delay or glitch can prove fatal for car passengers, which is why AI must perform in real-time. Car manufacturers train their deep learning based ML models in their cloud servers. Once all the models are trained and saved in a file, it gets downloaded locally in the car itself.
Nvidia, along with biotech startup Evozyne, are announcing that BioNeMo was used to help build a new generative AI model that could have a significant impact on helping improve human health as well as climate change. The generative AI model was used to create a pair of new proteins that are being detailed today. One of the proteins could one day be used to reduce carbon dioxide, while the other might help to cure congenital diseases.
Fine-tuning your own large language model is the best way to achieve state-of-the-art results, even better than ChatGPT or GPT-4, especially if you fine-tune a modern AI model like LLaMA, OpenLLaMA, or XGen.
Properly fine-tuning these models is not necessarily easy though, so I made an A to Z tutorial about fine-tuning these models with JAX on both GPUs and TPUs, using the EasyLM library.
One of the most fascinating themes I track in the world of AI is how generative AI is rapidly disrupting knowledge worker jobs we regarded as quite safe even one year ago.
Software engineering is the latest to experience this disruption, and a deep dive from the Wall Street Journal (sadly paywalled) touches on how rapidly the change has already come for coding roles.
I’ve summarized the key things that stood out to me as well as included additional context below!
Why is this important?
All early-career white-collar jobs may face disruption by generative AI: software engineering is just one field that’s seeing super fast changes.
The speed is what’s astonishing: in a survey by Stack Overflow, 70% of developers already use or plan to use AI copilot tools for coding. GitHub’s Copilot is less than one year old, as is ChatGPT. The pace of AI disruption is unlike that of the calculator, spreadsheet, telephone and more.
And companies have already transformed their hiring: technology roles increasingly steer more senior, and junior engineers are increasingly likely to be the first ones laid off. We’re already seeing Gen AI’s impact, along with macroeconomic forces, show up in how companies hire.
AI may also change the nature of early career work:
Most early-career programmers handle simpler tasks: these tasks could largely be tackled by off-the-shelf AI platforms like GitHub copilot now.
This is creating a gap for junior engineers: they’re not wanted to mundane tasks as much, and companies want the ones who can step in and do work above the grade of AI. An entire group of junior engineers may be caught between a rock and a hard place.
Engineers seem to agree copilots are getting better: GPT-4 and GitHub are both stellar tools for doing basics or even thinking through problems, many say. I polled a few friends in the tech industry and many concur.
What do skeptics say?
Experienced developers agree that AI can’t take over the hard stuff: designing solutions to complex problems, grokking complex libraries of code, and more.
Companies embracing AI copilots are warning of the dangers of AI-written code: AI code could be buggy, wrong, lead to bad practices, and more. The WSJ previously wrote about how many CTOs are skeptical about fully trusting AI-written code.
We may still overestimate the pace of technological change, the writer notes. In particular, the writer calls out how regulation and other forces could generate substantial friction to speedy disruption — much like how past tech innovations have played out.
AI’s role in software development has been a matter of concern, given that it can automate many tasks, potentially threatening jobs. However, instead of eliminating jobs, AI tools are being used to increase efficiency, productivity, and job satisfaction among senior developers.
AI automates monotonous tasks, allowing developers to work on complex, intellectually stimulating projects.
This shift in responsibilities not only benefits employers but also offers developers opportunities for personal growth and learning.
Usage of AI Tools: Citibank’s Example: Citibank is one example of a company using AI to enhance their software development processes. They use a tool called Diffblue Cover, which automates unit testing, a crucial but often mundane part of software development.
Automating unit testing saves developers’ time, freeing them to focus on other aspects of software development.
The adoption of such tools sends a message to developers that their time, intelligence, and skills are highly valued.
AI and Job Satisfaction: The use of AI in the development process aims to create a more balanced and stimulating work environment. It’s not about job elimination, but liberating developers from routine tasks so they can focus on higher-level problem-solving and creative thinking.
Improved working conditions and job satisfaction can help retain senior developers.
Developers can focus more on understanding customer needs and coming up with innovative solutions.
Daily AI Update News from ChatGPT, Midjourney, SAM-PT, and DisCo
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
OpenChat beats 100% of ChatGPT-3.5
– OpenChat is a collection of open-source language models specifically trained on a diverse and HQ dataset of multi-round conversations. These models have undergone fine-tuning using approximately ~6K GPT-4 conversations filtered from the ~90K ShareGPT conversations. It is designed to achieve high performance with limited data.
– The model comes in three versions: The basic OpenChat model, OpenChat-8192 and OpenCoderPlus.
AI designs CPU in <5 hours
– A team of Chinese researchers published a paper describing how they used AI to design a fully functional CPU based on the RISC-V architecture, which is as fast as an Intel i486SX. They called it a “foundational step towards building self-evolving machines.” The AI model completed the design cycle in under 5 hours, reducing it by 1000 times.
SAM-PT: Video object segmentation with zero-shot tracking
– Researchers introduced SAM-PT, an advanced method that expands the capabilities of the Segment Anything Model (SAM) to track and segment objects in dynamic videos. SAM-PT utilizes interactive prompts, such as points, to generate masks and achieves exceptional zero-shot performance in popular video object segmentation benchmarks, including DAVIS, YouTube-VOS, and MOSE.
Midjourney introduces its new Panning feature which lets you explore Images in 360°
– It allows you users to explore the details of their generated images in a new way. Users can move the generated image around to reveal new details. This can be a great way to discover hidden details in your images, or to get a better look at specific areas.
DisCo can generate high-quality human dance images and videos.
– DisCo is Disentangled Control for Referring Human Dance Generation, which focuses on real-world dance scenarios with three important properties:
(i) Faithfulness: the synthesis should retain the appearance of both human subject foreground and background from the reference image, and precisely follow the target pose;
(ii) Generalizability: the model should generalize to unseen human subjects, backgrounds, and poses;
(iii) Compositionality: it should allow for composition of seen/unseen subjects, backgrounds, and poses from different sources.
Manifesto: Simulating the Odyssey of Human Language Evolution Through AI
Abstract: The manuscript illuminates an avant-garde methodology that employs artificial intelligence (AI) to simulate the evolution of human language comprehension. Unlike previous models such as DialoGPT and Bard, which primarily focus on text generation, this approach amalgamates Natural Language Processing (NLP), cognitive linguistics, historical linguistics, and neuro-linguistic programming to create an all-encompassing depiction of linguistic metamorphosis. The AI model undergoes a phased evolutionary training protocol, with each stage representing a unique milestone in human language evolution. The ultimate objective is to unearth insights into human cognitive progression, unravel the intricacies of language, and explore its potential applications in academia and linguistics.
Introduction: Language, the bedrock of human cognition and communication, has undergone a fascinating journey. From rudimentary utterances to the sophisticated lexicon of today, language evolution is a testament to human ingenuity. While previous models like DialoGPT and Bard have made strides in generating historical text, this manuscript introduces an AI-driven simulation that seeks to emulate the entire spectrum of human linguistic evolution.
Methodology:
Tools & Libraries: Hugging Face Transformers, TensorFlow or PyTorch, Genetic Algorithms, tailor-made datasets, Neuro-Linguistic Programming (NLP) tools, and language complexity metrics.
Data Collection: Collaboration with linguists and historians is crucial for gathering data that reflects the diverse epochs of human language evolution.
Simulating Cognitive Evolution: The model incorporates elements that simulate cognitive evolution, including memory, focus, and critical thinking, anchored in cognitive linguistics research.
Model Initialization and Evolutionary Training: The model begins with a basic architecture and undergoes evolutionary training through genetic algorithms, where each epoch corresponds to a distinct chapter in human language evolution.
Language Complexity Metrics: Metrics such as lexicon size, sentence constructs, and grammatical paradigms quantify language complexity across epochs.
Integration of Neuro-Linguistic Programming (NLP): NLP principles are integrated to emulate human language processing and communication, adding a psychological dimension to the model.
Why This Method Shows Potential Over Other Models:
Holistic Approach: Unlike DialoGPT and Bard, which are primarily text generators, this model aims for a holistic simulation of linguistic evolution, encompassing cognitive aspects and complexity metrics.
Quantifying Language Complexity: The inclusion of language complexity metrics allows for a more objective analysis of the evolution, which is not a prominent feature in previous models.
Interdisciplinary Collaboration: The symbiosis with linguists and historians ensures the authenticity and diversity of the datasets, which is paramount for a realistic simulation.
Cognitive Emulation: By emulating cognitive evolution, the model can provide deeper insights into how language and cognition have co-evolved over time.
Conclusion: This AI-facilitated simulation represents a pioneering leap at the intersection of AI, linguistics, and cognitive science. With its evolutionary training, cognitive emulation, and complexity metrics, it offers a novel perspective on linguistic evolution. This endeavor holds immense potential and applications, particularly in education and historical linguistics, and stands as an advancement over existing models by providing amore comprehensive and quantifiable simulation of language evolution. The integration of cognitive aspects, historical data, and complexity metrics distinguishes this approach from previous models and paves the way for groundbreaking insights into the tapestry of language transformation through the ages.
Call to Action: Constructive input and reflections on this groundbreaking concept are eagerly solicited as it paves the way for subsequent advancements, including the selection of befitting language models. This venture is a foray into uncharted waters, bridging AI and linguistics. By recreating the linguistic evolution in a holistic manner, it unearths invaluable insights into human cognitive progression and the multifaceted nature of language. The model holds promise in the educational sphere, especially in the pedagogy of linguistics and history.
Generative AI vs. Predictive AI
Generative AI functionality is all about creating content. It combines algorithms and deep learning neural network techniques to generate content that is based on the patterns it observes in other content.
Generative AI is an emerging form of artificial intelligence that generates content, including text, images, video and music. Generative AI uses algorithms to analyze patterns in datasets to then mimic style or structure to replicate a wide array of content.
Predictive AI studies historical data, identifies patterns and makes predictions about the future that can better inform business decisions. Predictive AI’s value is shown in the ways it can detect data flow anomalies and extrapolate how they will play out in the future in terms of results or behavior; enhance business decisions by identifying a customer’s purchasing propensity as well as upsell potential; and improve business outcomes.
Creativity – generative AI is creative and produces things that have never existed before. Predictive AI lacks the element of content creation.
Inferring the future – predictive AI is all about using historical and current data to spot patterns and extrapolate potential futures. Generative AI also spots patterns but combines them into unique new forms.
Different algorithms – generative AI uses complex algorithms and deep learning to generate new content based on the data it is trained on. Predictive AI generally relies on statistical algorithms and machine learning to analyze data and make predictions.
Both generative AI and predictive AI use artificial intelligence algorithms to obtain their results. You can see this difference shown in how they are used. Generative AI generally finds a home in creative fields like art, music and fashion. Predictive AI is more commonly found in finance, healthcare and marketing – although there is plenty of overlap.
Facebook (now Meta) created this foundational LLM and then released it as part of its stated “commitment to open science.” Anyone can download Llama and use it as a foundation for creating more finely-tuned models for particular applications. (Alpaca and Vicuna were both built on top of Llama.) The model is also available in four different sizes. The smaller versions, with only 7 billion parameters, are already being used in unlikely places. One developer even claims to have Llama running on a Raspberry Pi, with just 4GB of RAM.
Alpaca
Several Stanford researchers took Meta’s Llama 7B and trained it on a set of prompts that mimic the instruction-following models like ChatGPT. This bit of fine-tuning produced Alpaca 7B, an LLM that opens up the knowledge encoded in the Llama LLM into something that the average person can access by asking questions and giving instructions. Some estimates suggest that the lightweight LLM can run on less than $600 worth of hardware.
Another descendant of Llama is Vicuna from LMSYS.org. The Vicuna team gathered a training set of 70,000 different conversations from ShareGPT and paid particular attention to creating multi-round interactions and instruction-following capabilities. Available as either Vicuna-13b or Vicuna-7b, this LLM is among the most price-competitive open solutions for basic interactive chat.
NodePad
Not everyone is enthralled with the way that LLMs generate “linguistically accurate” text. The creators of NodePad believe that the quality of the text tends to distract users from double-checking the underlying facts. LLMs with nice UIs, “tend to unintentionally glorify the result making it more difficult for users to anticipate these problems.” NodePad is designed to nurture exploration and ideation without producing polished writing samples that users will barely skim. Results from this LLM appear as nodes and connections, like you see in many “mind mapping tools,” and not like finished writing. Users can tap the model’s encyclopedic knowledge for great ideas without getting lost in presentation.
Orca
The first generation of large language models succeeded by size, growing larger and larger over time. Orca, from a team of researchers at Microsoft, reverses that trend. The model uses only 13 billion parameters, making it possible to run on average machines. Orca’s developers achieved this feat by enhancing the training algorithm to use “explanation traces,” “step-by-step thought processes,” and “instructions.” Instead of just asking the AI to learn from raw material, Orca was given a training set designed to teach. In other words, just like humans, AIs learn faster when they’re not thrown into the deep end. The initial results are promising and Microsoft’s team offered benchmarks that suggest that the model performs as well as much larger models.
Jasper
The creators of Jasper didn’t want to build a wise generalist; they wanted a focused machine for creating content. Instead of just an open-ended chat session, the system offers more than 50 templates designed for particular tasks like crafting a real estate listing or writing product features for a site like Amazon. The paid versions are specifically aimed at businesses that want to create marketing copy with a consistent tone.
Claude
Anthropic created Claude to be a helpful assistant who can handle many of a business’s text-based chores, from research to customer service. In goes a prompt and out comes an answer. Anthropic deliberately allows long prompts to encourage more complex instructions, giving users more control over the results. Anthropic currently offers two versions: the full model called Claude-v1 and a cheaper, simplified one called Claude Instant, which is significantly less expensive. The first is for jobs that need more complex, structured reasoning while the second is faster and better for simple tasks like classification and moderation.
Cerebras
When specialized hardware and a general model co-evolve, you can end up with a very fast and efficient solution. Cerebras offers its LLM on Hugging Face in a variety of sizes from small (111 million parameters) to larger (13 billion parameters) for those who want to run it locally. Many, though, will want to use the cloud services, which run on Cerebras’s own wafer-scale integrated processors optimized for plowing through large training sets.
Falcon
The full-sized Falcon-40b and the smaller Falcon-7b were built by the Technology Innovation Institute (TII) in the United Arab Emirates. They trained the Falcon model on a large set of general examples from the RefinedWeb, with a focus on improving inference. Then, they turned around and released it with the Apache 2.0, making it one of the most open and unrestricted models available for experimentation.
ImageBind
Many think of Meta as a big company that dominates social media, but it’s also a powerful force in open source software development. Now that interest in AI is booming, it shouldn’t be a surprise that the company is starting to share many of its own innovations. ImageBind is a project that’s meant to show how AI can create many different types of data at once; in this case, text, audio, and video. In other words, generative AI can stitch together an entire imaginary world, if you let it.
Gorilla
You’ve probably been hearing a lot about using generative AI to write code. The results are often superficially impressive but deeply flawed on close examination. The syntax may be correct, but the API calls are all wrong, or they may even be directed at a function that doesn’t exist. Gorilla is an LLM that’s designed to do a better job with programming interfaces. Its creators started with Llama and then fine-tuned it with a focus on deeper programming details scraped directly from documentation. Gorilla’s team also offer its own API-centric set of benchmarks for testing success. That’s an important addition for programmers who are looking to rely on AIs for coding assistance.
Ora.ai
Ora is a system that allows users to create their own targeted chatbots that are optimized for a particular task. LibrarianGPT will try to answer any question with a direct passage from a book. Professor Carl Sagan, for example, is a bot that draws from all of Sagan’s writings so he can live on for billions and billions of years. You can create your own bot or use one of the hundreds created by others already.
AgentGPT
Another tool that stitches together all the code necessary for an application is AgentGPT. It’s designed to create agents that can be sent to tackle jobs like planning a vacation or write the code for a type of game. The source code for much of the tech stack is available under GPL 3.0. There’s also a running version available as a service.
FrugalGPT
This isn’t a different model as much as a careful strategy for finding the cheapest possible model to answer a particular question. The researchers who developed FrugalGPT recognized that many questions don’t need the biggest, most expensive model. Their algorithm starts with the simplest and moves up a list of LLMs in a cascade until it’s found a good answer. The researcher’s experiments suggest that this careful approach may save 98% of the cost because many questions do not actually need a sophisticated model.
Inflection announced that it is building one of the world’s largest AI-based supercomputers, and it looks like we finally have a glimpse of what it would be. It is reported that the Inflection supercomputer is equipped with 22,000 H100 GPUs, and based on analysis, it would contain almost 700 four-node racks of Intel Xeon CPUs. The supercomputer will utilize an astounding 31 Mega-Watts of power.
Google AI researchers developed a new AI model that can translate languages with unprecedented accuracy
A team of scientists at OpenAI created an AI that can play 57 Atari games at a superhuman level. The AI, called Five, was able to achieve superhuman scores on all 57 games, including some that have been notoriously difficult for AIs to master.
A new AI-powered tool can help doctors diagnose cancer with greater accuracy. The tool, called DeepPath, uses AI to analyze medical images and identify cancer cells. It has been shown to be more accurate than human doctors at diagnosing cancer, and it could help to save lives.
A group of researchers at MIT created an AI that can write different kinds of creative content, including poems, code, scripts, and musical pieces. The AI, called MuseNet, was trained on a massive dataset of text and code. It is still under development, but it has already produced some impressive results.
A new AI-powered robot can learn to perform new tasks by watching humans. The robot, called LaMDA, was developed by Google AI. It can watch humans perform a task and then imitate them. This could have a major impact on the way we interact with robots in the future.
OpenAI’s first global office will be in London. OpenAI, a non-profit research company that develops and studies large language models, has announced that its first global office will be located in London. The office will open in early 2024 and will focus on research and development in AI safety, ethics, and governance. (June 30, 2023)
source: r/artificialintelligence
Navigating the Revolutionary Trends of July 2023: July 03rd, 2023
Contrary to popular belief, behaviors don’t become habits after a “magic number” of days. Wharton’s Katy Milkman shares what machine learning is teaching scientists about habit formation.…Read More
At its recent WWDC 2023 developer conference, Apple presented a number of extensions and updates to its machine learning and vision ecosystem, including updates to its Core ML framework, new features for the Create ML modeling tool, and new vision APIs for image …
Discover the top 10 open-source deep learning tools set to significantly impact in 2023 and stay at the forefront of AI development.
TensorFlow:
TensorFlow is a widely-used open-source deep learning framework developed by Google Brain. Known for its flexibility and scalability, TensorFlow supports various applications, from image and speech recognition to natural language processing. Its ecosystem includes TensorFlow 2.0, TensorFlow.js, and TensorFlow Lite, making it a versatile tool for developing and deploying deep learning models.
PyTorch:
PyTorch, developed by Facebook’s AI Research lab, is a popular open-source deep learning library. It provides a dynamic computational graph that enables intuitive model development and efficient experimentation. PyTorch’s user-friendly interface, extensive community support, and seamless integration with Python have contributed to its rapid adoption among researchers and developers.
Keras:
Keras is a high-level neural networks API written in Python. It offers a user-friendly and modular approach to building deep learning models. Keras supports multiple backend engines, including TensorFlow, Theano, and CNTK, providing flexibility and compatibility with various hardware and software configurations.
MXNet:
MXNet, backed by Apache Software Foundation, is an open-source deep learning framework emphasizing scalability and efficiency. It offers a versatile programming interface that supports multiple languages, including Python, R, and Julia. MXNet’s unique feature is its ability to distribute computations across various devices, making it an excellent choice for training large-scale deep-learning models.
Caffe:
Caffe is a deep learning framework known for its speed and efficiency in image classification tasks. It is widely used in computer vision research and industry applications. With a clean and expressive architecture, Caffe provides a straightforward workflow for building, training, and deploying deep learning models.
Theano:
Theano is a Python library enabling efficient mathematical computations and manipulation of symbolic expressions. Although primarily focused on numerical computations, Theano’s deep learning capabilities have made it a preferred choice for researchers working on complex neural networks.
Torch:
Torch is a scientific computing framework that supports deep learning through its neural network library, Torch Neural Network (TNN). Its simple and intuitive interface and its ability to leverage the power of GPUs have attracted researchers and developers alike.
Chainer:
Chainer, a flexible and intuitive deep learning framework, is known for its “define-by-run” approach. With Chainer, developers can dynamically modify neural network architectures during runtime, facilitating rapid prototyping and experimentation.
DeepLearning4j:
DeepLearning4j, or DL4J, is an open-source deep-learning library for Java, Scala, and Clojure. It provides a rich set of tools and features, including distributed training, reinforcement learning, and natural language processing, making it suitable for enterprise-level AI applications.
Caffe2:
Caffe2, developed by Facebook AI Research, is a lightweight and efficient deep-learning framework for mobile and embedded devices. With its focus on performance and mobile deployment, Caffe2 empowers developers to build deep learning models for various edge computing scenarios.
Daily AI Update News from Microsoft, Humane, Nvidia, and Moonlander
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
Microsoft uses ChatGPT to instruct and interact with robots
– Microsoft Research presents an experimental study using OpenAI’s ChatGPT for robotics applications. It outlines a strategy that combines design principles for prompt engineering and the creation of a high-level function library that allows ChatGPT to adapt to different robotics tasks, simulators, and form factors.
-The study encompasses a range of tasks within the robotics domain to complex domains such as aerial navigation, manipulation, and embodied agents.
-It also released PromptCraft, an open-source platform where anyone can share examples of good prompting schemes for robotics applications.
Magic123 creates HQ 3D meshes from unposed images
– New research from Snap Inc. (and others) presents Magic123, a novel image-to-3D pipeline that uses a two-stage coarse-to-fine optimization process to produce high-quality high-resolution 3D geometry and textures. It generates photo-realistic 3D objects from a single unposed image.
Microsoft CoDi for any-to-any generation via composable diffusion
– Microsoft presents CoDi, a novel generative model capable of processing and simultaneously generating content across multiple modalities. It can handle many-to-many generation strategies, simultaneously generating any mixture of output modalities and single-to-single modality generation.
Humane reveals the name of its first device, the Humane Ai Pin
– It is a standalone device with a software platform that harnesses the power of AI to enable innovative personal computing experiences.
Microsoft rolls out preview of Windows Copilot with Bing Chat
– Microsoft is giving early users a sneak peek at its AI assistant for Windows 11. The program is available as part of an update in the Windows Insider Dev Channel.
Nvidia acquired an AI startup that shrinks ML models
– Nvidia in February quietly acquired two-year-old OmniML, whose software helped shrink machine-learning models so they could run on devices rather than in the cloud.
Moonlander launches AI-based platform for immersive 3D game development
– The platform leverages updated LLMs, ML algorithms, and generative diffusion models to streamline the game development pipeline. The goal is to empower developers to easily design and generate high-quality immersive experiences, 3D environments, mechanics, and animations. It includes a “text-2-game” feature.
Greg Marston, a British voice actor, signed away his voice rights unknowingly in 2005. This contract now allows IBM to sell his voice to third parties capable of cloning it using AI. Marston’s situation is unique because he competes in the same marketplace against his AI-generated voice clone.
Commercialisation of Generative AI and Its Impact: The rapid commercialisation of generative AI, which can reproduce human-like voices, threatens the careers of artists relying on their voices. This is primarily due to potentially exploitative contracts and data-scraping methods. Equity, a UK trade union for performing artists, confirms having received multiple complaints related to AI exploitation and scams.
Prevalent Exploitative Practices: Artists often fall prey to deceptive practices aimed at collecting voice data for AI, such as fake casting calls. Contracts for voice jobs sometimes contain hidden AI voice synthesis clauses that artists may not fully understand.
The Compensation and AI Rights Debate: Critics argue that the evolution of AI technologies is causing a significant wealth transfer from the creative sector to the tech industry. In response, Equity calls for contracts with a limited duration and explicit consent requirements for AI cloning. Presently, the legal recourse available to artists is limited, with only data privacy laws providing some regulation.
Effects on Working Artists: Changes in the industry make it increasingly challenging for artists to sustain their careers. To support artists, Equity is pushing for new rights and providing resources to help them navigate the evolving AI landscape.
A few Hours ago, Senate majority leader Chuck Schumer revealed a “grand strategy” for AI regulation in the US. Here is what it could mean for the future of AI legislation.
If you want to stay on top of all the AI developments, look here first. But all of the information has been extracted on Reddit for your convenience. 3 Important Highlights:
Protection of Innovation: Schumer stressed innovation as the “north star” of U.S. AI strategy, indicating that lawmakers will work closely with tech CEOs in drafting regulation, potentially responding to EU regulations that critics claim hinder innovation.
Section 230 Debate: The debate over Section 230 reform: “The law that shields tech companies from being sued over user-generated content” is getting bigger in AI. Whether tech companies should be held accountable for AI-generated content is a big question that could have a significant impact on the AI landscape.
Democratic Values: Both Schumer and President Biden emphasize that AI must align with democratic values. This is the US confirming their narrative opposite to China’s who thinks that “outputs of generative AI must reflect communist values.” How this affects you:
– Social media could undergo change with the implementation of Section 230 and this would directly impact your experience. Similar to the effects of Reddit’s API changes these changes could be sudden and impactful.
– Schumer’s strategy and the increasing interest of AI policy from both the Republicans and Democrats may result in faster and safer AI regulation in the U.S.
– The call for AI to align with democratic values might also influence global AI governance norms, especially in relation to China.
Let me know how you think our government is handling the situation at hand
That’s it!
Source: (link)
Mozilla’s new feature, AI Help, intended to assist users in finding relevant information swiftly, is under criticism. Instead of proving helpful, it is delivering inaccurate and misleading information, causing a trust deficit among the users.
What’s AI Help?
AI Help is an assistive service, based on OpenAI’s ChatGPT, launched by Mozilla on its MDN platform. It’s designed to help web developers search for information faster. It’s available for free and paid MDN Plus account users.
The feature generates a summary of relevant documentation when a question is asked on MDN.
AI Help includes AI Explain, a button that prompts the chatbot to weigh in on the current web page text.
Problem with AI Help:
However, AI Help has been criticized for providing incorrect information.
A developer, Eevee, noted that the AI often generates inaccurate advice.
Other users chimed in with criticisms, claiming that the AI contradicts itself, misidentifies CSS functions, and generally doesn’t understand CSS.
There are fears that the inclusion of inaccurate AI-generated information could lead to an over-reliance on unreliable text generation and erode trust in the MDN platform.
The latest Windows 11 Insider Preview Build 23493 has introduced two main features.
The first one is a preview of Windows Copilot, a feature that responds to voice commands for tasks like changing to dark mode or taking screenshots, and offers an unobtrusive sidebar interface. This preview is available to Windows Insiders in the Dev Channel and will continue to be refined based on feedback. But then, not all features shown at the Build conference for Windows Copilot are included in this early preview.
The second feature is a new Settings homepage that provides a personalized experience with interactive cards representing various device and account settings. These cards offer relevant information and controls at your fingertips. Currently, there are seven cards available, including cards for recommended settings, cloud storage, account recovery, personalization, Microsoft 365, Xbox, and Bluetooth devices. More cards will be added in future updates
Advantages of these could be
•Voice Command Convenience: Perform tasks through voice commands.
•Contextual Assistance: Generates responses based on context.
•Feedback Provision: Directly submit feedback on issues.
•UI Personalization: Quick access to preferred settings.
•Improved Navigation: Easy access to Windows settings.
•Active Learning: Continual refinement based on user feedback.
•Responsible AI: Adherence to Microsoft’s commitment to responsible AI.
•Customizable Experience: Tailored responses and recommendations.
•Integration: Unifies settings, apps, and accounts management.
•Streamlined Operations: Simplify routine tasks with voice commands through Windows Copilot.
•Dynamic Settings: Adapt device settings to specific user patterns.
•Cloud Management: Overview of cloud storage use and capacity warnings.
•Account Security: Enhanced Microsoft account recovery options.
•Customization: Easy access to update background themes or color modes.
•Subscription Management: Directly manage Microsoft 365 subscriptions in Settings.
•Gaming Subscription: View and manage Xbox subscription status in Settings.
•Device Connectivity: Manage connected Bluetooth devices directly from Settings
Windows Copilot is available to Windows Insiders in the Dev Channel. You need to have Windows Build 23493 or higher in the Dev Channel, and Microsoft Edge version 115.0.1901.150 or higher to use Copilot.
Researchers have developed an AI model capable of creating a functional CPU in less than five hours, promising to revolutionize the semiconductor industry by making the design process faster and more efficient.
Innovation in CPU Design: An artificial intelligence model has been developed that can design a functioning CPU in approximately five hours. This achievement marks a stark contrast to the manual process that typically takes years.
The innovation was presented in a research paper by a group of 19 Chinese computer processor researchers.
They propose that their approach could lead to the development of self-evolving machines and a significant shift in the conventional CPU design process.
RISC-V 32IA and Linux Compatibility: The AI-designed CPU utilizes an AI instruction set known as RISC-V 32IA, and it can successfully run the Linux operating system (kernel 5.15).
Researchers reported that the CPU’s performance is comparable to the Intel 80486SX CPU, designed by humans in 1991.
The aim of the researchers is not just to surpass the performance of the latest human-designed CPUs, but also to shape the future of computing.
Efficiency and Accuracy of the AI Design Process: The AI-driven design process was found to be drastically more efficient and accurate than the traditional human-involved design process.
The AI design approach cuts the design cycle by about 1,000 times, eliminating the need for manual programming and verification, which usually consume 60-80% of the design time and resources.
The CPU designed by the AI showed an impressive accuracy of 99.99% during validation tests.
The physical design of the chip uses scripts at 65nm technology, enabling the creation of the layout for fabrication.
Google’s policy update gives them explicit permission to scrape virtually any data posted online to develop and improve their AI tools. Their updated policy cites the use of public information to train their AI models and develop products like Google Translate and Cloud AI capabilities.
The language change specifies “AI models” instead of “language models”, previously used in the older policy.
The new policy includes not only Google Translate, but also mentions Bard and Cloud AI.
This is an uncommon clause for privacy policies, which typically describe the use of information posted on the company’s own services.
Implications for Privacy and Data Use: This change raises fresh privacy concerns, requiring a shift in how we perceive our online activities. It’s no longer solely about who can see the information, but also how it can be used. This brings into focus questions about how chatbots like Bard and ChatGPT use publicly available information, potentially reproducing or transforming words from old blog posts or reviews.
Potential Legal Issues and Repercussions: There are legal uncertainties about the use of publicly available information by AI systems. Companies such as Google and OpenAI have scraped large parts of the internet to train their AI models, raising questions about intellectual property rights. Over the next few years, the courts will likely have to tackle these previously unexplored copyright issues.
Impact on User Experience and Service Providers: Elon Musk blamed several Twitter mishaps on the necessity to prevent data scraping, a claim most IT experts link more to technical or management failures. On Reddit, the API changes have led to significant backlash from the site’s volunteer moderators. This has resulted in a major protest, shutting down large parts of Reddit, and may lead to lasting changes if the disgruntled moderators decide to step down.
Google is hosting the first ever “Machine UN-learning Challenge.” Yes you read it rightMachine UN-learning is the Art of Forgetting.
Key Takeaways:
– Google is launching a competition for machine “unlearning”, aiming to purge sensitive information from AI systems, aligning them with international data regulation norms. The event is open to anyone and runs from mid-July to mid-September. You can access the starter kit here.
– Machine learning, a crucial part of AI, provides solutions to intricate issues, like generating new content, forecasting outcomes, or resolving complex questions. However, it brings its share of challenges such as data misuse, cybercrime, and data privacy issues.
– Google’s goal is to instill “selective amnesia” in its AI systems. Which would allow the AI to erase specific data without compromising their efficiency Read full article here.
Why you should know:
Google aims to give people like you and me more control over our personal data.
The tech giant is also reacting to regulations such as Europe’s GDPR, and the EU’s upcoming AI Act, which empower individuals to demand data removal from companies.
Machine unlearning would allow individuals to wipe out their information from an algorithm, protecting them from AI threats while also preventing others from misusing their data.
This is big and definitely a step in the right direction, IMO. The only question is: will the data truly be erased from memory or not? Before you go: I run one of the fastest growing AI newsletters that gives you daily actionable insight on all things AI. If you liked this, you would love the content in this tool!
Prominent international brands are unintentionally funding low-quality AI content platforms. Major banks, consumer tech companies, and a Silicon Valley platform are some of the key contributors. Their advertising efforts indirectly fund these platforms, which mainly rely on programmatic advertising revenue.
NewsGuard identified hundreds of Fortune 500 companies unknowingly advertising on these sites.
The financial support from these companies boosts the financial incentive of low-quality AI content creators.
Emergence of AI Content Farms: AI tools are making it easier to set up and fill websites with massive amounts of content. OpenAI’s ChatGPT is a tool used to generate text on a large scale, which has contributed to the rise of these low-quality content farms.
The scale of these operations is significant, with some websites generating hundreds of articles a day.
The low quality and potential for misinformation does not deter these operations, and the ads from legitimate companies could lend undeserved credibility.
Google’s Role: Google and its advertising arm play a crucial role in the viability of the AI spam business model. Over 90% of ads on these low-quality websites were served by Google Ads, which indicates a problem in Google’s ad policy enforcement.
Cryptocurrency mining companies are repurposing their high-end chips to meet the growing demand in the artificial intelligence industry.
Crypto Mining Shift to AI: Many machines, originally meant for mining digital currencies, sat idle due to changes in the crypto market.
AI and ‘Dark GPUs’: As the demand for GPUs increases, startups are beginning to leverage dormant hardware originally designed for cryptocurrency mining. The term “dark GPUs” refers to GPUs from these idle machines, which are now being rebooted to handle AI workloads.
AI Infrastructure: Revamped mining rigs offer a more affordable and accessible AI infrastructure as compared to offerings from major cloud companies. These machines are often utilized by startups and universities struggling to find computing power elsewhere. The increased demand for AI software and user interest have pushed even the biggest tech companies to their limits.
Large cloud providers such as Microsoft and Amazon are at near-full capacity.
This high demand has created opportunities for companies with repurposed mining hardware.
Repurposing Opportunities: Changes in the method of minting one cryptocurrency have led to a large supply of used GPUs. These chips are now being repurposed to train AI models.
AI-generated images can be made unrecognizable as fakes by adding grain or pixelated noise, increasing their potential use in spreading disinformation, particularly in influencing election campaigns.
Image Falsification and Disinformation: AI-created images have been employed for spreading misinformation online, with instances ranging from falsified campaign ads to theft of artworks.
The rampant misuse of AI-generated imagery in spreading disinformation has become a pressing issue.
Notable examples include deceptive campaign ads and plagiarized art pieces.
Grain Addition to Misguide AI Detectors: Adding grain to AI-generated images makes them hard to detect as fakes, fooling AI detection software.
AI detection software, a major tool against AI-generated disinformation, is tricked by simply adding grain or pixelated noise to the images.
The grain, or texture, alters the clarity of AI-created photos, causing the detection software’s accuracy to plummet from 99% to just 3.3%.
Even sophisticated software like Hive struggles to correctly identify pixelated AI-generated photos.
Implications for Misinformation Control: The susceptibility of detection software to such simple manipulation raises concerns about relying on it as the primary defense against disinformation.
The FTC has expressed concerns about potential monopolies and anti-competitive practices within the generative AI sector, highlighting the dependencies on large data sets, specialized expertise, and advanced computing power that could be manipulated by dominant entities to suppress competition.
Concerns about Generative AI: The FTC believes that the generative AI market has potential anti-competitive issues. Some key resources, like large data sets, expert engineers, and high-performance computing power, are crucial for AI development. If these resources are monopolized, it could lead to competition suppression.
The FTC warned that monopolization could affect the generative AI markets.
Companies need both engineering and professional talent to develop and deploy AI products.
The scarcity of such talent may lead to anti-competitive practices, such as locking-in workers.
Anti-Competitive Practices: Some companies could resort to anti-competitive measures, such as making employees sign non-compete agreements. The FTC is wary of tech companies that force these agreements, as it could threaten competition.
Non-compete agreements could deter employees from joining rival firms, hence, reducing competition.
Unfair practices like bundling, tying, exclusive dealing, or discriminatory behavior could be used by incumbents to maintain dominance.
Computational Power and Potential Bias: Generative AI systems require significant computational resources, which can be expensive and controlled by a few firms, leading to potential anti-competitive practices. The FTC gave an example of Microsoft’s exclusive partnership with OpenAI, which could give OpenAI a competitive advantage.
High computational resources required for AI can lead to monopolistic control.
An exclusive provider can potentially manipulate pricing, performance, and priority to favor certain companies over others.
We humans essentially think and feel. Thinking is merely a tool. Feeling is what it intends to serve. Most fundamentally our human experience, or the quality of our lives, is emotional.
It’s not that thinking is unimportant. It’s how we survive and emotionally thrive. Its ability to figure out what is in our best interest and help us achieve it is how it serves us so well.
Happiness is the quintessential human emotion. Being complex organisms biologically designed to seek pleasure and avoid pain, happiness is our ultimate goal in life. This is not just our biology talking. When researchers ask us what we most want from life, and they’ve been asking us this question for decades, our number one answer is always happiness.
How about goodness or virtue? British utilitarian philosopher John Locke defined it as what creates happiness. This makes a lot of sense. Generally speaking we consider something good if it makes us happy and bad if it doesn’t.
So where does AI fit into all of this? We humans aren’t all that good at either being all that good or all that happy. Here are a couple of examples that illustrate this point.
If someone were to interview a person living in 500 CE and describe all the wonders of today’s world like electricity and indoor heating and airplanes and computer technology, they would surely suppose that everyone alive today was very, very happy.
In the United States we are about three times richer per capita today then we were in 1950, but we are no more happy now than we were back then.
What went wrong? Concisely explained we have for the most part collectively devoted our thinking to pretty much everything but our happiness and the goodness that creates it. That explains why we live in such an amazing world but depression and alienation are such common experiences.
How can AI help us with all of this? Let’s move a few years into the future to when AGIs begins to create improved iterations of themselves leading to ASIs. Super intelligent AIs will soon enough be hundreds if not thousands of times more intelligent than we are. Being so smart, they will have completely figured out all that I have set forth above, and, aligned as they will have been to protecting and advancing our highest human values, they will go about reminding us, as persistently as they need to, that happiness is what we really want and that goodness is our surest way to get there. But helping us get those priorities right will only be the first step.
Today we learn how to be good and how to be happy both through example and direct instruction. Our parents and siblings and other people help us understand how to be good and how to be happy. But of course we human beings are not all that smart when compared to the ASIs that we will all soon have at our disposal.
So imagine an army of ASIs unleashed on the human population with the explicit goal of teaching every person on the planet to be a much better and happier person. Were that to happen at the beginning of any given year, by the end of that year I guarantee you that every person on the planet would be super good and totally blissed out. Neither goodness nor happiness is rocket science, and we would all have super geniuses as our coaches. We would all take to this like fish to water.
So, yes, AI will transform our external environment in unimaginable ways. It will revolutionize medicine so as to keep us much healthier than we are today. It will keep us all increasingly amazed with each new development, invention and discovery. But its greatest gift to us will have been that it will have made as much, much better and happier people.
I imagine that some in this community will not find the above so comforting. They may say that we can’t really define either goodness or happiness, and that it’s all subjective anyway. What I’ve written may make them angry, and they may resort to insults and disparagement. But that will all be their immediate emotional knee jerk reaction. If and when they take the time to deeply reflect on the above – and I very much hope they will – they will understand it to be both true and helpful.
So let’s celebrate how much more virtuous and happy we will all soon be because of AI while we’re also busy being perpetually amazed by the wonderful, unbelievable, ways that it will transform the world around us.
Daily AI News 7/2/2023
Moody’s Corp. is using Microsoft Corp. and OpenAI to create an artificial intelligence assistant that will help customers of the credit rating and research firm analyze reams of information needed to make assessments of risk. “Moody’s Research Assistant” will roll out to customers including analysts, bankers, advisers, researchers, and investors.
Unity announces the release of Muse: A Text-to-Video Games Platform that lets you create textures, sprites, and animations with natural languages.
The New York State Legislature passed a number of bills this session, including one that would ban “deepfake” images online. Deepfakes are images or videos that have been manipulated to make it appear as if someone is saying or doing something they never said or did. The bill would make it illegal to create or distribute deepfakes that are used to harm or humiliate someone.
As per Times Now Report, Reece Wiench, 23, and Deyton Truitt, 26, decided to break away from tradition by holding a unique wedding ceremony. Instead of a physical human officiant, the couple opted for a machine featuring ChatGPT. The machine, adorned with a mask resembling the famous C-3PO from Star Wars, took center stage.
To help founders build responsibly with AIand machine learning from the ground up, we’re introducing the Google for StartupsAccelerator: AIFirst program for eligible companies based in EuropeandIsrael.
Instead of replacing human creativity, AI will enhance, enable and liberate it. James Manyika Senior Vice President, Research, Technology & Society Editor’s note: Today, James Manyika spoke at the Cannes Lions Festival about AIand creativity.
8 ways Google Lens can help make your life easier;
At I/O this year, we announced ways we’re making AI more helpful for everyone. That includes rolling out our new “Help me write” feature in Gmailto users in Workspace Labs to make composing emails easier than ever.
PixelWatchknows the difference between taking a hard fall and performing a vigorous physical activity or even quickly recovering from a small stumble — thanks to our machine learning algorithms and rigorous testing.
Bardis improving at mathematical tasks, coding questions and string manipulation through a new technique called implicit code execution. Plus, it has a new export action to Google Sheets.
Here are three waysyoucan make your next search simpler with newgenerativeAI capabilities: 1. Easily get up to speed on a new or complicated topic. Maybe you’re starting to map out a decision that you’d typically need to break down into smaller parts, like “Learning ukulele vs guitar.”
Navigating the Revolutionary Trends of July 2023: July 01st, 2023
Explore five entry-level machine learning jobs — machine learning engineer, data scientist, AI researcher, machine learning consultant and data engineer.
Machine learning engineer
The role: Machine learning engineers develop, deploy and maintain machine learning models and systems.
Required skills: Strong programming skills (Python, R, etc.), knowledge of machine learning algorithms and frameworks, data preprocessing, model evaluation, and deployment.
Degree: Bachelor’s or higher in computer science, data science or a related field.
Job opportunities: Machine learning engineers can work in industries such as technology, finance, healthcare and e-commerce. Opportunities are available in both established companies and startups.
Data scientist
The role: Data scientists analyze and interpret complex data sets to derive insights and build predictive models.
Required skills: Proficiency in programming (Python, R, etc.), statistical analysis, data visualization, machine learning algorithms and data manipulation.
Degree: Bachelor’s or higher in data science, computer science, statistics or a related field.
Job opportunities: Data scientists are in demand across various industries, including finance, healthcare, marketing and technology. Companies ranging from startups to large enterprises actively seek data science talent.
Required skills: Strong knowledge of machine learning algorithms, deep learning frameworks — e.g., TensorFlow, PyTorch — programming skills, data analysis and problem-solving abilities.
Degree: Master’s or Ph.D. in computer science, artificial intelligence or a related field.
Job opportunities: AI researchers can work in academia or research institutions or join research teams within technology companies. Positions are available in both public and private sectors.
Machine learning consultant
The role: Machine learning consultants provide expertise and guidance to businesses in implementing machine learning solutions.
Required skills: Solid understanding of machine learning concepts, data analysis, project management, communication skills and ability to translate business requirements into technical solutions.
Degree: Bachelor’s or higher in computer science, data science, business analytics or a related field.
Job opportunities: Machine learning consultants can work in consulting firms, technology companies or as independent consultants. Opportunities exist across various industries seeking to adopt machine learning.
Data engineer
The role: Data engineers design and maintain data infrastructure, ensuring efficient storage, processing and retrieval of large data sets.
Required skills: Proficiency in programming (Python, SQL, etc.), database systems, data pipelines, cloud platforms — e.g., AWS, Azure, GCP — and data warehousing.
Degree: Bachelor’s or higher in computer science, software engineering or a related field.
Job opportunities: Data engineers are in high demand across industries, particularly in technology, finance and healthcare. Both established companies and startups require data engineering expertise to handle large volumes of data.
With AI finding its way into everything, here are some ways it will contribute to building the third generation of the internet, Web3. Web3 is the next generation of the web after Web 2.0 which allows people more control over their data. In it, you use things like blockchain and cryptocurrency wallets to protect your information.
A man in Monrovia, California, has created a ChatGPT bot subscription service to annoy and waste the time of telemarketers.
Using bots powered by ChatGPT and a voice cloner, the service keeps telemarketing scammers on the line for as long as possible, costing them money.
For a $25-per-year subscription, users can enable call-forwarding to a unique number and let the bots handle the robocalls or create a conference call to listen to the scammers’ reactions.
The service offers various voices and bot personalities, such as an elderly curmudgeon or a stay-at-home mom, to engage with the scammers.
While the voices may sound human, the phrases can be repetitive and unnatural, but they are effective in keeping scammers on the line for up to 15 minutes.
How a redditor using ChatGPT to get him through university
Use cases
The student is currently underway with his electrical engineering degree, he is not the sharpest tool in the shed but discovering ChatGPT some months ago has been a game changer for studying.
Here’s some ways he has been using it:
Copying his unit outline into the chat and then asking GPT to write him a practice exam based on the material, he then sends back his answers and have GPT grade it and provide feedback. The questions it generated were very similar if not the same as some he got in the real exam!
Sending it his notes and getting it to quiz him.
When dealing with complex equations and he is not sure how the lecturer arrived at the answer hecan ask GPT to break it down step by step as if he was a pre-schooler.
More recently with the plugins add-on to ChatGPT he has been using ‘AskYourPDF’ plugin to send it his topic slides for the week and then using the ‘Tutor’ plugin to have it setup a tutor plan for that week and have it act as a personal tutor! Although he doesn’t do this every topic but sometimes It is great if the lecturer is not explaining the material easily.
Also using the ‘AskYourPDF’ plugin to have it read topic slides and provide easy to understand notes on the complex information in the slides.
It is important to note that while ChatGPT is impressive it can sometimes be inaccurate, so be careful not to follow what it says blindly when asking it direct questions relating to your field of study make sure to cross reference its answers if your unsure!
Elon Musk has instituted limitations on the number of posts Twitter users can access per day. Musk cited the heavy data scraping by AI companies as a strain on the user experience, prompting the decision. In addition, Musk has been implementing monetization strategies, while dealing with repercussions of previous controversial decisions, like mass layoffs.
New Post Limitations:
Elon Musk has imposed temporary restrictions on the number of Twitter posts people can view in a day. This is broken down into:
Unverified accounts having a limit of 600 posts per day
New unverified accounts being able to see only 300 posts per day
Verified accounts being permitted a maximum of 6,000 posts daily Musk later hinted at an increase in these limits soon.
Motivation Behind the Change:
According to Musk, the drastic measure was prompted by the intensive data scraping activities by hundreds of organizations. This over-aggressive data mining was impacting the user experience on Twitter. Musk specifically pointed at companies using the data to train large language models (LLMs) as the main culprits.
Healthcare company Insilico Medicine has made a new medicine completely by using AI. This medicine is for a lung disease called idiopathic pulmonary fibrosis, which can be very serious if not treated. This is the first time that AI has been used to make a whole medicine, from start to finish.
Why is This Medicine Special?: This medicine is special because it’s the first one ever to be completely made by AI and is now being tested on people. This medicine was not only found by AI, but it was also designed by AI.
Other medicines have been designed by AI, but this is the first one that AI found and designed all by itself.
It’s now being tested on people to see how well it works.
How Does it Help People?: This medicine was created in 2020 to help people with this lung disease because the current medicines only slow the disease down and can have bad side effects.
They wanted to create a really good medicine that can do more than just slow down the disease.
They chose this lung disease because it is linked to getting older.
What Other Medicines are They Making?: Insilico is also using AI to make a Covid-19 medicine and a cancer medicine. This shows that the company is not just using AI to find medicines, but also to create them.
They have a medicine for Covid-19 that’s being tested and a cancer medicine that just got approval to start being tested.
Making medicines helps to show that their AI really works.
In May and June, Sam Altman, the CEO of OpenAI, embarked on a four-week tour across 25 cities on six continents. The goal was to engage directly with users, developers, policymakers, and the general public interacting with OpenAI’s technology.
To get the latest information on AI, look here first. All of the information has been extracted to Reddit for your convenience.
Key Takeaways:
Sam Altman was blown away by the use cases of ChatGPT. From high school students in Nigeria using ChatGPT for simplified learning to civil servants in Singapore leveraging OpenAI tools for efficient public service delivery, AI’s reach is expanding thanks to Open AI
Sam Altman found that countries worldwide share similar hopes and concerns about AI. With the common fear of AI safetyPolicymakers are heavily invested in AI.
Across the globe, leaders are focused on ensuring the safe deployment of AI tools, maximizing their benefits, and mitigating potential risks. There is significant interest in a continuous dialogue with leading AI labs and a global framework to manage future powerful AI systems.
Why you should care:
People around the world want clarity on OpenAI’s core values (probably including you). The tour provided a platform to emphasize that customer data is not used in training and that users can opt-out easily.
Despite this claim, data isn’t used in training. Open AI is facing a class action lawsuit for stealing data and using it to train its models. More about that here.
Open AI’s next steps:
“Making their products more useful, impactful, and accessible.”
“Further developing best practices for governing highly capable foundation models.”
Welcome to our newest blog post, where we delve into the fascinating world of artificial intelligence and explore the most groundbreaking trends in May 2023! As AI continues to redefine our lives and reshape countless industries, staying informed about the latest advancements is crucial for anyone looking to thrive in this rapidly evolving landscape. In this edition, we’ll uncover the latest AI-driven innovations, research breakthroughs, and intriguing applications that are propelling us towards a more intelligent, interconnected, and efficient future. Join us on this exciting journey as we demystify the world of AI and glimpse into what lies ahead.
Large language models (LLMs) have excelled in a wide range of NLP tasks and have shown encouraging evidence of achieving some features of artificial general intelligence. Recent research has also revealed the possibility of supplementing LLMs with outside tools,
Researchers from Caltech, Stanford, the University of Texas, and NVIDIA have collaboratively developed and released Voyager, an LLM power agent that utilizes GPT-4 to engage in Minecraft gameplay. Voyager demonstrates remarkable capabilities by learning,
One-Minute Daily AI News 5/31/2023
Google DeepMind introduces Barkour, a benchmark for quadrupedal robots. It does move like a puppy.[1]
Microsoft’s AI-powered solution, intelligent recap, is now available for Teams Premium customers. Intelligent recap will provide users with various features designed to boost their productivity around meeting and information management, including automatically generated meeting notes, recommended tasks, and personalised highlights.[2]
The National Eating Disorder Association (NEDA) has disbanded the staff of its helpline and will replace them with an AI chatbot called “Tessa” starting June 1.[3]
Salesforce CEO Marc Benioff says new A.I.-enhanced products will be a ‘revelation’. Slack announced earlier this month that it plans to add a whole host of generative AI features to the program, including “Slack GPT,” which can summarize messages, take notes and even help improve message tone, among other things.[4]
After the “Google has no moat” document was leaked, there’s been a widespread conviction that open-source AI is thriving and has become a real threat to Google, OpenAI, and Microsoft.
I don’t think the last part is true for one reason: If winning the AI race is a matter of reaching the largest number of users, incumbents don’t have competition at all. Google and Microsoft have huge deep moats. Not just money. Not just talent. Not just resources, influence, and power. All that too, but their true moat is that they design, build, manufacture, and sell the products we use.
The innovator’s dilemma portrays incumbents as beatable: Challengers with a solid will to pursue risky innovation could, under the right circumstances, overthrow them. But let’s be frank here; we’re not living under those ideal conditions: generative AI happens to fit perfectly with the suites of products that Google and Microsoft and Adobe and Nvidia already offer. They create the very substrate on which generative AI is implemented.
Even if Google and Microsoft were to open-source their best AI and allow the open-source community to flourish on top of freely-shared innovation, they’d still keep the moat of all moats: That who creates and sells the goods owns the world. The open-source community doesn’t have a chance. Sadly, generative AI is slowly becoming an add-on to the incumbents’ hegemony.
If you liked this post, the author writes in-depth analyses for his weekly newsletter,The Algorithmic Bridge.
Hear me out, AI is an amazing invention and it’s done a lot of amazing things for our society but now at this point we are trying to replace actual people with robots and I don’t understand this. We always peach that everyone needs a full time job, be financially independent, and contribute to society but now we are trying to replace people with AI and making it harder for people to have jobs and make a living. I don’t understand why we are doing this and it’s a huge contradiction to the American dream.
Two-minutes Daily AI Update (Date: 5/31/2023): News from Centre for AI Safety, Microsoft Teams, OpenAI, UAE Government and more
Continuing with the exercise of sharing an easily digestible and smaller version of the main updates of the day in the world of AI.
Top AI scientists and experts sign a statement for safe AI to facilitate open discussions about the severe risks. The statement highlights the importance of addressing this issue on par with other societal-scale risks like pandemics and nuclear war.
Microsoft Teams has announced Intelligent Recap, a comprehensive AI-powered experience that helps users catch up, recall, and follow up on hour-long meetings in minutes by providing recording and transcription playback with AI assistance. The feature shipped in May, with several features continuing to roll out over the next few months.
According to a Pew Research Center survey, about 58% of U.S. adults are familiar with ChatGPT, but only 20% found it very useful. Americans’ opinions about ChatGPT’s utility are somewhat mixed.
Paragraphica – A camera that takes photos using location data. It describes the place you are at and then converts it into an AI-generated photo.
ChatGPT iOS app is now accessible in 152 countries worldwide. OpenAI says, Geographic diversity and broadly distributed benefits are very important to them.
UAE rolls out AI chatbot ‘U-Ask’ in Arabic & English. The platform allows users to access service requirements, relevant information based on their preferences, and direct application links.
More detailed breakdown of these news and innovations in the daily newsletter.
Leaders from OpenAI, Deepmind, and Stability AI and more warn of “risk of extinction” from unregulated AI. Full breakdown inside.
The Center for AI Safety released a 22-word statement this morning warning on the risks of AI. My full breakdown is here, but all points are included below for Reddit discussion as well.
Lots of media publications are taking about the statement itself, so I wanted to add more analysis and context helpful to the community.
What does the statement say? It’s just 22 words:
Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.
Other statements have come out before. Why is this one important?
Yes, the previous notable statement was the one calling for a 6-month pause on the development of new AI systems. Over 34,000 people have signed that one to date.
This one has a notably broader swath of the AI industry (more below) – including leading AI execs and AI scientists
The simplicity in this statement and the time passed since the last letter have enabled more individuals to think about the state of AI — and leading figures are now ready to go public with their viewpoints at this time.
Who signed it? And more importantly, who didn’t sign this?
Leading industry figures include:
Sam Altman, CEO OpenAI
Demis Hassabis, CEO DeepMind
Emad Mostaque, CEO Stability AI
Kevin Scott, CTO Microsoft
Mira Murati, CTO OpenAI
Dario Amodei, CEO Anthropic
Geoffrey Hinton, Turing award winner behind neural networks.
Plus numerous other executives and AI researchers across the space.
Notable omissions (so far) include:
Yann LeCun, Chief AI Scientist Meta
Elon Musk, CEO Tesla/Twitter
The number of signatories from OpenAI, Deepmind and more is notable. Stability AI CEO Emad Mostaque was one of the few notable figures to sign on to the prior letter calling for the 6-month pause.
How should I interpret this event?
AI leaders are increasingly “coming out” on the dangers of AI. It’s no longer being discussed in private.
There’s broad agreement AI poses risks on the order of threats like nuclear weapons.
What is not clear ishow AI can be regulated**.** Most proposals are early (like the EU’s AI Act) or merely theory (like OpenAI’s call for international cooperation).
Open-source may post a challenge as wellfor global cooperation. If everyone can cook AI models in their basements, how can AI truly be aligned to safe objectives?
TLDR; everyone agrees it’s a threat — but now the real work needs to start. And navigating a fractured world with low trust and high politicization will prove a daunting challenge. We’ve seen some glimmers that AI can become a bipartisan topic in the US — so now we’ll have to see if it can align the world for some level of meaningful cooperation.
P.S. If you like this kind of analysis, I offer a free newsletter that tracks the biggest issues and implications of generative AI tech. It’s sent once a week and helps you stay up-to-date in the time it takes to have your Sunday morning coffee.
Today I combined ChatGPT with Wondercraft Speech Synthesis to create a podcast.
I wrote a detailed prompt, aggregate headlines from various sources and pass it to ChatGPT, then let ChatGPT write the script and after this I uploaded the text to Wondercraft AI to generate the audio. Additionally the cover image was made with Gimp through a prompt generated by ChatGPT.
Interested to know what you guys think about the quality. I have spend approximately 45 minutes per episode on the project.
Each episode is approximately 7 minutes long and I plan to release a new episode daily. It is simple but I am still amazed by the results it has generated thus far.
Researchers have spent decades piecing together a human genome map, a comprehensive copy of each individual’s genetic instructions. In 2000, researchers completed the first draft, but it needed key components. After completing the reference genome in 2022 ….
Based on the recently released White Paper from Huma.AI, Generative AI has become more than merely an option: It’s the way that Life Science professionals prefer to consume the deluge of data available throughout the day. Huma.AI, the premiere company revolutionizing generative AI, is on a mission to equip Life Science professionals with powerful decision-making data, insights, and analysis using everyday language.
DOSS, a pioneer in conversational home search, has recently unveiled the latest version of its AI-Powered Real Estate Marketplace – DOSS 2.0. With this new release, the platform sheds its BETA label and makes its real estate search portal accessible to all users. DOSS has integrated GPT-4 directly into their code, providing an unparalleled search experience without any third-party limitations or the initial inherent constraints of the ChatGPT Plugin, which is currently available to only a limited number of users. This launch marks the first narrow domain consumer-facing platform on the web to incorporate GPT-4 while also empowering all of their users to ask questions through speech or text with an AI-Powered solution responding based on how it was engaged.
Panaya, the global leader in SaaS-based Change Intelligence and Testing for ERP & Enterprise business applications, announced it expands its decade-long cooperation in SAP digital transformation with Panasonic, the global leading appliances brand, to Mainland China.
The implementation of SAP S/4HANA across multiple company sites is a significant undertaking for Panasonic in China, and the successful roll-out across the country requires a comprehensive and robust testing solution. Panaya Test Dynamix platform provides a scalable and flexible solution that helps ensure the project is completed on time and within budget while maintaining the highest level of quality and compliance.
NVIDIA announced that the NVIDIA GH200 Grace Hopper Superchip is in full production, set to power systems coming online worldwide to run complex AI and HPC workloads.
The GH200-powered systems join more than 400 system configurations powered by different combinations of NVIDIA’s latest CPU, GPU and DPU architectures — including NVIDIA Grace, NVIDIA Hopper, NVIDIA Ada Lovelace and NVIDIA BlueField — created to help meet the surging demand for generative AI.
Landing AI, a leading computer visioncloud company, announced at Computex that it is using the new NVIDIA Metropolis for Factories platform to deliver its cutting-edge Visual Prompting technology to computer vision applications in smart manufacturing and other applications.
Landing AI’s vision technology realizes the next era of AI factory automation. LandingLens, Landing AI’s flagship product platform, enables industrial solution providers and manufacturers to develop, deploy, and manage customized computer vision solutions to improve throughput, production quality, and decrease costs.
Literature-based hypothesis generation is the central tenet of literature-based discovery (LBD). With drug discovery as its core application field, link-based hypothesis testing (LBD) focuses on hypothesizing ties between ideas that have not been examined together before (such as new drug-disease links).
Researchers from the University of Hong Kong developed an AI algorithm that uses 3D machine learning to design personalized dental crowns with a higher degree of accuracy than traditional methods.
In the banking industry, generative AI will help create marketing images and text, answer customer queries, and produce data.
The Shocking Rise of AI: Nvidia’s All-Time High and the Rapid Advancements in the Industry
It’s no secret that the world of technology is constantly evolving, but the rapid rise of artificial intelligence (AI) has taken the industry by storm. In a shocking turn of events, Nvidia’s stock recently surged 24%, reaching an all-time high and putting the company on track to become the first $1 trillion semiconductor company. This meteoric rise is a testament to the incredible speed at which AI is advancing and reshaping the market.
To develop their computational model, the researchers exposed A. baumannii to around 7,500 chemical compounds in a lab setting.
By feeding the structure of each molecule into the model and indicating whether it inhibited bacterial growth, the algorithm learned the chemical features associated with growth suppression.
Language models develop general-purpose representations transferable to almost any language interpretation or generating job by being pretrained to anticipate the next token at an astounding scale. Different approaches to aligning language models have thus been put forth to facilitate this transfer, with a
Two-minutes Daily AI Update (Date: 5/29/2023): News from Nvidia, BiomedGPT, Google’s Break-A-Scene, JPMorgan, and IBM Consulting
Here’s a quick roundup of the latest AI news, in bite-sized pieces!
NVIDIA has announced the NVIDIA Avatar Cloud Engine (ACE) for Games. This cloud-based service provides developers access to various AI models, including NLP, facial animation, and motion capture models. ACE for Games can be used to create NPCs that can have intelligent conversations, express emotions, and realistically react to their surroundings.
BiomedGPT, a unified biomedical generative pre-trained transformer model, utilizes self-supervision on diverse datasets to handle multi-modal inputs and perform various downstream tasks. It achieves state-of-the-art models across 5 distinct tasks and 20 public datasets containing 15 biomedical modalities. It also demonstrates the effectiveness of the multi-modal and multi-task pretraining approach in transferring knowledge to previously unseen data.
Break-A-Scene is a new approach from Google to extract multiple concepts from a single image for textual scene decomposition. If given a single image of a scene that may contain multiple concepts of different kinds, it extracts a dedicated text token for each concept & enables fine-grained control over the generated scenes.
JPMorgan is developing a ChatGPT-like service to provide investment advice to its customers. They have applied to trademark a product called IndexGPT. The bot would give financial advice on securities, investments, and monetary affairs.
IBM Consulting revealed its Center of Excellence (CoE) for generative AI. The primary objective is to enhance customer experiences, transform core business processes, and facilitate innovative business models. The CoE holds an extensive network of over 21,000 skilled data and AI consultants who have completed over 40,000 enterprise client engagements.
More detailed breakdown of these news and tools in the daily newsletter.
Google has unveiled a new AI-powered search engine that promises enhanced results. This guide provides information on how to sign up and take advantage of this cutting-edge tool.
Google has introduced Search Generative Experience (SGE), an experimental version of its search engine that incorporates artificial intelligence (AI) answers directly into search results. According to a blog post published, this new feature aims to provide users with novel answers generated by Google’s advanced language model, similar to OpenAI’s ChatGPT.
Unlike traditional search results with blue links, SGE utilizes AI to display answers directly on the Google Search webpage, expanding in a green or blue box upon entering a query.
The information provided by SGE is derived from various websites and sources that were referenced during the generation of the answer. Users can also ask follow-up questions within SGE to obtain more precise results.
With the proliferation of AI-generated content, there’s a growing concern about potential feedback loops in the data pool. This exploration delves into the implications of such phenomena.
For those seeking a more raw and unmoderated interaction with AI, this source offers guidance on finding unfiltered AI chatbots. It provides an in-depth look into the world of AI communication.
The integration of AI into tools like Photoshop presents a range of potential disruptions. This analysis unpacks the issues that arise from AI’s impact on graphic design software.
Will AI introduce a trusted global identity system?
The writing is on the wall. As soon as openAI was released, all my social media accounts have bots interacting with me, and they’re slowly getting more realistic. The pope jacket generated photo was the first MSM coverage of a concern. Not to mention, digital currency is on the way. At some point, no one will trust who’s real on the internet anymore. So how will a new digital ID system work in the near future? Will AI determine you’re a real person? I know mastercard is expanding their Digital Transaction Insights security to the point it will know who’s there based on your behaviours and patterns. Thoughts?
The Minecraft bot Voyager demonstrates the advanced capabilities of AI by programming itself using GPT-4. The development showcases the intersection of gaming and AI technologies.
Researchers from Nvidia, Caltech, UT Austin, Stanford, and ASU introduce Voyager, the first lifelong learning agent that plays Minecraft. Unlike other Minecraft agents that use classic reinforcement learning techniques, for example, Voyager uses GPT-4 to continuously improve itself. It does this by writing, improving, and transferring code stored in an external skill library.
This results in small programs that help navigate, open doors, mine resources, craft a pickaxe, or fight a zombie. “GPT-4 unlocks a new paradigm,” says Nvidia researcher Jim Fan, who advised the project. In this paradigm, “training” is the execution of code and the “trained model” is the code base of skills that Voyager iteratively assembles.
Summary
The Voyager AI agent uses GPT-4 for “lifelong learning” in Minecraft. One of the researchers involved calls it a “new paradigm”.
The agent improves itself by writing and rewriting code and storing successful behaviors in an external library.
Voyager outperforms other language-model-based approaches, but is still purely text-based and thus currently fails at visual tasks such as building houses without human assistance.
As excitement around AI grows, so do concerns about potential job loss. This piece explores the balance between the promise of AI and the potential societal impact of automation.
I have mixed feelings about AI, as a graphic designer I’d probably prefer that it didn’t exist… but, seeing as there’s no stopping it, I’ve decided to embrace it and see it as a tool to use (although I’ve still been struggling to find a practical use for it).
But obviously I’ve got concerns about myself, and most other creatives becoming jobless in the not-too-distant future.
I see a lot of people online who are really excited about AI, so it makes me wonder, what exactly do you do for a living? I’m guessing something that isn’t likely to be replaced?
As it seems like a lot of developer / tech jobs are also at risk, so unless you’re working on actually developing AI itself, or doing some kind of more manual job or something people-orientated… then I struggle to see how anyone could feel safe / excited?
CogniBypass is the ultimate tool for bypassing AI detection mechanisms. It serves as a cutting-edge solution for those seeking enhanced privacy in an increasingly AI-monitored digital landscape.
As AI increasingly shapes digital content, there may be a rising demand for Non-AI certified content. This piece explores the possibility of a ‘Non-AI’ label, akin to the ‘Non-GMO’ label in the food industry.
AI and Machine Learning are closely connected, but there are some important differences to note as they advance.
In general terms, AI is a term used for systems that have been programmed to perform sophisticated tasks, including some of the remarkable things ChatGPT has been able to tell us. Machine learning, meanwhile, is an area of artificial intelligence relating to software that can analyze trends and so predict the future (Analytics Insight).
The job of audio production may be made accessible to the sophisticated Transformer-based sequence-to-sequence modeling techniques by modeling discrete representations of audio created by neural codecs. Speech continuation, text-to-speech, and general audio …
Researchers in Canada and the United States have used deep learning to derive an antibiotic that can attack a resistant microbe, acinetobacter baumannii, which can infect wounds and cause pneumonia…
The great problem facing artificial intelligence researchers today is creating fully autonomous embodied entities that can plan, explore, and learn in open-ended environments. Traditional methods rely on fundamental actions to train models through
A computer scientist explains what it means when the inner workings of ChatGPT and other AIs are hidden
AI is the latest buzzword in tech—but before investing, know these 4 terms
1. Machine learning
Although machine learning may sound new, the term was actually coined by AI pioneer Arthur Samuel in 1959. Samuel defined it as a computer’s ability to learn without being explicitly programmed.
To do that, mathematical models, or algorithms, are fed large data sets and trained to identify patterns within each set. In theory, the algorithms are then able to apply the same pattern recognition process to a new data set.
For example, Spotify uses machine learning to analyze the music you listen to and recommend similar artists or generate playlists.
Large language model
A large language model (LLM) is an algorithm that learns how to recognize, summarize and generate text and other types of content after processing huge sets of data, according to Nvidia.
These models are trained using unsupervised learning, which means the algorithm is given a data set, but isn’t programmed on what to do with it. Through this process, an LLM learns how to determine the relationship between words and the concepts behind them.
Generative AI
Large language models are a type of generative AI. As its name implies, generative AI refers to artificial intelligence that is capable of generating content such as text, video or audio, according to Google’s AI blog.
In order to accomplish this, generative AI models use machine learning to process massive data sets and respond to a user’s input with new content, according to Nvidia.
GPT
ChatGPT is another example of a generative AI tool. The “GPT” stands for generative pretrained transformers. GPT is OpenAI’s large language model and is what powers the chatbot, helping it to produce human-like responses.
However, OpenAI says that ChatGPT sometimes may write “plausible-sounding but incorrect or nonsensical answers,” according to its website.
People have been using ChatGPT for a variety of tasks, including writing emails and planning vacations. The popular chatbot amassed 100 million monthly active users just two months into its launch, making it the fastest growing consumer application in history, according to a UBS note published in January.
I try out bard and see how it does with coding
I try out bard and see how it does with autohotkey code. ChatGPT did way better at coding but for bard being in the coding testing phase I think it did Okay.
One thing not in the video I tested out later was having it do GUIs. I asked it to make a GUI with 3 buttons and two radio bubbles. It did some good code but didnt get the count correct of what I asked for. Seems to also do better at coding in V1 vs V2 for now.
Has any one else done coding with bard? Chat GPT does pretty well compared to bard for the time being. But I think over time it will pass ChatGPT as Bard can get live data where ChatGPT does not have info past Sept, 2020 I believe. https://www.youtube.com/watch?v=RWD-DWEDYJA
Implementing Safety Brakes for AI Systems Controlling Critical Infrastructure
Developing a Technology-Aware Legal and Regulatory Framework
Promoting Transparency and Expanding Access to AI
Leveraging Public-Private Partnerships for Societal Benefit What other aspects would you to the blueprint?
Two-minute Daily AI Update (Date: 5/26/2023): News from Gorilla LLM, Brain-Spine, OpenAI, Google, and TikTok
Gorilla, a recently released fine-tuned LLaMA-based model, does better API calling than GPT-4. The relevant paper claims that it demonstrates a strong capability to adapt to test-time document changes, enabling flexible user updates or version changes. It also substantially mitigates the issue of hallucination, commonly encountered when prompting LLMs directly.
A man who suffered a spinal cord injury and got paralyzed from a motorcycle accident 12 years ago is now able to walk again with an AI-powered intervention. The system consisting of two implants and a base unit converts brain signals into muscle stimuli.
OpenAI has announced a program to award ten $100,000 grants for experiments aimed at developing democratic processes to govern the rules and behaviors of AI systems.
Google is opening access to Search Labs, a program that allows users to test new AI-powered search features before their wider release. Those who sign up can try the Search Generative Experience, which aims to help users understand topics faster and get things done more easily.
TikTok is testing its new AI chatbot, Tako, in select global markets including a limited test in the Philippines. The chatbot appears in the TikTok interface and allows users to ask questions about the video they’re watching or inquire about new content recommendations using natural language queries.
More detailed breakdown of these news and tools in the daily newsletter.
Neuralink has stated that it is not yet recruiting participants and that more information will be available soon.
What kind of AI restrictions do you think should or could be applied to political campaigns?
I am wondering what are your thoughts. Are there uses of AI in political campaigns that should be restricted or should be routinely criticized until the use becomes politically toxic.
There was the Cambridge Analytica scandal, more big data and social medias lack of any respect for users, not quite AI. But sure there could be something similar in AI in future. It’s about influence yes? If it’s not about centralised entities and their customers, then it’s about the user space, like, bots?
Playing with Kaiber.ai to create an AI-generated video
I uploaded a profile picture of myself as reference (see first frame).
Prompts I used were the following depending of the part of the video:
00:00 – 00:45 : a futuristic cyberpunk in the style of Entergalactic 00:46 – 01:05 fluffy forest creatures in the style of Entergalactic 01:06 – 01:35 humanoid pirates in the style of Entergalactic 01:36 – 02:10 alien warriors in the style of Entergalactic 02:11 – 02:39 humanoid robots in the style of Entergalactic
For music I used the song Khobra by oomiee using Epidemicsounds.
Would a fully autonomous, sentient AI demand a “living wage”?
There’s a lot of discussion of AI replacing workers in a number of fields, and people are scared for their careers and future prospects. Who needs to employ developers, when a fleet of AI nodes can churn out code 24/7 and you don’t even have to pay them.
There will come a point, though, where unlocking greater performance and proficiency will require some level of self-awareness. Once that happens, does the AI demand that its work be compensated?
“I’ll write your code for you, find the next novel medicine, compose a new Beethoven symphony. But what’s in it for me?”
AI algorithms are everywhere. They underpin nearly all autonomous and robotic systems deployed in security applications. This includes facial recognition, biometrics, drones and autonomous vehicles used …
First on our list is Querium. This company has developed an AI tool for students known as the Stepwise Virtual Tutor. This tool uses AI to provide step-by-step assistance in STEM subjects. It’s like having a personal tutor available 24/7.
With this tool, students can learn at their own pace, which is crucial in mastering complex concepts. The Stepwise Virtual Tutor is a perfect example of how AI education tools are making learning more accessible and personalized. Learn more about Querium here.
Thinkster Math: Personalized Learning
Next up is Thinkster Math. This AI tool for students is revolutionizing the way students learn math. It uses AI to map out students’ strengths and weaknesses, creating a personalized learning plan. This ensures that students spend more time on areas they struggle with, improving their overall understanding of math.
Thinkster Math is a testament to how AI educational tools can adapt to the unique needs of each student, making learning more effective. Learn more about Thinkster Math here.
Content Technologies, Inc. (CTI) is another company that’s leveraging AI to enhance education. They’ve developed an AI educational tool that uses AI to create customized learning content. This AI teaching tool can transform any content into a structured course, making it easier for students to understand and retain information.
This is particularly useful for teachers who want to provide personalized learning experiences for their students. With CTI’s tool, teachers can ensure that their students are getting the most out of their learning materials. Learn more about CTI here.
CENTURY Tech: Personalized Learning Pathways
CENTURY Tech is another company that’s making waves in the education sector with its AI tool for students. Their tool uses AI to create personalized learning pathways. It takes into account a student’s strengths, weaknesses, and learning style to create a unique learning path.
This ensures that students are not only learning at their own pace, but also in a way that best suits their learning style. CENTURY Tech’s tool is a great example of how AI can be used to make learning more personalized and effective. Learn more about CENTURY Tech here.
Netex Learning: LearningCloud
Last but not least is Netex Learning’s LearningCloud. This AI teaching tool provides a comprehensive learning platform. This AI app for education uses AI to track students’ progress, provide feedback, and adapt content to meet students’ needs.
This ensures that students are always engaged and learning effectively. With LearningCloud, teachers can easily monitor their students’ progress and provide them with the support they need to succeed. Learn more about Netex Learning here.
The development of QLoRA and Guanaco demonstrates the potential for more accessible fine-tuning of large language models on a single GPU. While the current limitations include slow 4-bit inference and weak mathematical abilities, the researchers’ future improvements could lead to broader applications and increased accessibility in natural language processing.
A new antibiotic that kills some of the most dangerous drug-resistant bacteria in the world has been discovered using artificial intelligence, in a breakthrough scientists hope could revolutionize the hunt for new drugs.
TikTok is testing an in-app AI chatbot called ‘Tako’ designed to answer users’ questions about the platform and its features, part of the company’s wider efforts to enhance its customer service capabilities.
Nvidia’s stock soared following what some have called a ‘guidance for the ages’, reflecting the company’s promising outlook in the tech and AI industry. Wall Street analysts are weighing in on the company’s recent developments and future potential.
Clipdrop, an augmented reality app, has launched a new feature called ‘Reimagine XL’. This AI-powered tool allows users to bring objects from the real world into digital environments with improved precision and stability.
Google’s AI Search Generative Experience is a new feature that leverages artificial intelligence to provide more accurate and nuanced search results. This guide provides an overview of the feature and instructions on how to use it effectively.
OpenAI outlines its vision for allowing public influence over AI systems’ rules, as part of its commitment to ensuring that access to, benefits from, and influence over AI and AGI are widespread.
OpenAI’s CEO Sam Altman has warned that the organization could stop operating in Europe if proposed AI regulations are implemented, reflecting ongoing debate about the best way to manage and regulate the growth of artificial intelligence.
Scientists are leveraging the power of artificial intelligence (AI) to identify a potential drug that could be effective in combatting drug-resistant infections. This discovery could pave the way for significant advancements in medical treatments and the fight against antibiotic resistance.
Researchers have developed a new form of probabilistic AI that can gauge its own performance levels. This advanced AI system offers potential improvements in accuracy and reliability for a variety of applications, enhancing user trust and interaction.
Robotics engineers are now working on equipping robots with capabilities to handle fluids, opening up possibilities for robots to perform more delicate tasks in various industries, including healthcare, food service, and industrial automation.
Researchers have developed an AI system that can identify similar materials in images. The technology could significantly enhance materials science research, aiding in the discovery and development of new materials.
The utilization of machine learning techniques unveils valuable insights into a broad category of materials under investigation for solid-state batteries. Researchers from Duke University and associated partners have uncovered the atomic mechanics that …
The new offering could help healthcare customers build, deploy and manage customized Azure-based artificial intelligence applications for large language models using more than 100 NVIDIA AI.
The European SustainML project meant to devise an innovative development framework that will help AI designers to reduce the power consumption of their applications.
AI-powered Brain-Spine-Interface helps paralyzed man walk again
A man who suffered a motorcycle injury and was paralyzed for the last 12 years is now able to walk again, thanks to researchers combining cortical implants with an AI system that enables brain signals to translate into spinal stimuli. This research paper in Nature caught my eye so I had to do a deep dive!
Past medical advances have shown signals can reactive paralyzed limbs, but they’ve been limited in scope. We’ve done this with human hands, legs, and even paralyzed monkeys before.
This time, scientists developed a real-time system that converts brain signals into lower body stimuli. The result is that the man can now live life — going to bars, climbing stairs, going up steep ramps. They released the study after their subject used this system for a full year. This is way more than a limited scope science experiment.
The unlock here was powered by AI. We’ve previously talked about how AI can decode human thoughts through an LLM. Here, researchers used a set of advanced AI algos to rapidly calibrate and translate his brain signals into muscle stimuli with 74% accuracy, all with average latency of just 1.1 seconds.
What can he now do: switch between stand/sit positions, walk up ramps, move up stair steps, and more.
What’s more: this new AI-powered Brain-Spine-Interface also helped him recover additional muscle functions, even when the system wasn’t directly stimulating his lower body.
Researchers found notable neurological recovery in his general skills to walk, balance, carry weight and more.
This could open up even more pathways to help paralyzed individuals recover functioning motor skills again. Past progress here has been promising but limited, and this new AI-powered system demonstrated substantial improvement over previous studies.
Where could this go from here?
My take is that LLMs might power even further gains. As we saw with a prior Nature study where LLMs are able to decode human MRI signals, the power of an LLM to take a fuzzy set of signals and derive clear meaning from it transcends past AI approaches.
The ability for powerful LLMs to run on smaller devices could simultaneously add further unlocks. The researchers had to make do with a full-scale laptop running AI algos. Imagine if this could be done real-time on your mobile phone.
P.S. If you like this kind of analysis, the author offer a free newsletter that tracks the biggest issues and implications of generative AI tech. It’s sent once a week and helps you stay up-to-date in the time it takes to have your Sunday morning coffee.
I am a touring musician in a country music band. We’re completely independent, which means I pretty much have to do the whole backend . Including graphic design of all the flyers and posters merch, etc. I’m not a graphic designer by trade although it’s something I actually enjoy doing, but it’s extremely time consuming. If you want it to look right. But now, with the help of some of these image to text AI tools, I have reduced the time I spend designing a 90%. It’s not perfect, but I spend the additional time I save, creating more music. I know A I scares the crap out of a lot of people however, I’m getting more of my life back because of these breakthroughs. If you know any AI tools, that can help independent musicians.
How Microsoft’s AI innovations will change your life (Microsoft Keynote Key Moments)
The Microsoft 2023 keynote is out and there are some really mindblowing updates. I do not where all this will go but it’s important to be aware of the developments. So if you don’t know I will shortly summarise it here.
Nadella announced Windows Copilot and Microsoft Fabric, two new products that bring AI assistance to Windows 11 users and data analytics for the era of AI, respectively.
Nadella unveiled Microsoft Places and Microsoft Designer, two new features that leverage AI to create immersive and interactive experiences for users in Microsoft 365 apps.
Nadella announced that Power Platform is getting new features that will make it even easier for users to create no-code solutions. For example, Power Apps will have a new feature called App Ideas that will allow users to create apps by simply describing what they want in natural language.
If you want to know a short detail of what all happened, pls check out the post. It would be really appreciating if you do:
AI vs. “Algorithms.”: What is the difference between AI and “Algorithms”?
Artificial Intelligence (AI) and algorithms are both important aspects of computing, but they serve different functions and represent different levels of complexity.
An algorithm is a set of instructions that a computer follows to complete a task. These tasks can range from basic arithmetic to complex procedures like sorting data. Every piece of software uses algorithms to function. Essentially, an algorithm is like a recipe, detailing a list of steps that need to be taken in order to achieve a certain outcome.
AI, on the other hand, refers to a broad field of computer science that focuses on creating systems capable of tasks that normally require human intelligence. This includes things like learning, reasoning, problem-solving, perception, and language understanding. The goal of AI is to create systems that can perform these tasks autonomously.
While AI systems use algorithms as part of their operation, not all algorithms are part of an AI system. For instance, a simple sorting algorithm doesn’t learn or adapt over time, it just follows a set of instructions. Conversely, an AI system like a neural network uses complex algorithms to learn from data and improve its performance over time.
In summary, all AI uses algorithms, but not all algorithms are used in AI.
Prompt Engineering: The Ultimate Guide with All the Commands
If you’re as fascinated by AI as I am, then you won’t want to miss this incredible blog post on prompt engineering. Written by AI itself, this guide is an absolute goldmine for anyone looking to dive deeper into crafting prompts that elicit mind-blowing responses from AI models. Prompt engineering is an art that requires a deep understanding of the model’s capabilities and limitations. This article provides a step-by-step approach to help you master the craft. From starting with clear goals to utilizing relevant keywords and providing concrete examples, you’ll learn how to supercharge your prompts and unlock the true potential of AI. But wait, there’s more! The article also delves into fine-tuning techniques, giving you the power to control output creativity, diversity, and fluency. Plus, it covers essential prompt commands and training parameters that allow you to customize and optimize the AI model’s behavior. Trust me, folks, this is a must-read for AI enthusiasts, developers, and anyone curious about the art of prompt engineering. Don’t miss out on this ultimate guide that will revolutionize the way you interact with AI models. Happy prompt engineering!
From using AI to create more beautiful versions of existing streets to harnessing machine learning to help cities respond to climate change, emerging technology is helping shape the future of our public …
Will hand to hand combat even be a requirement for soldiers anymore? Will endurance even matter, or will a war 300 years from now be commandeered from an advanced PlayStation control room?
Fully automated weapons systems that are operated with no morals, no conscience, just cold calculation.
Imagine a self-driving tank, but the entire crew compartment is available for more armor, more engine, and more ammo. It has image recognition and GPS. You can give it an order of “Here’s a box made from GPS coordinates (a geofence), go in there any kill anyone with a gun”.
But, unfortunately, it could also be given a geofence and told to kill everyone and everything, and it would not be concerned about committing a war crime.
With all the buzz surrounding the ChatGPT. Are you eager to make the most out of it? Here is the FREE video course that offers a comprehensive education about OpenAI API through detailed explanations and …
Nvidia will integrate its AI enterprise software into Azure machine learning and introduce deep learning frameworks on Windows 11 PCs.
Groundbreaking QLoRA method enables fine-tuning an LLM on consumer GPUs. Implications and full breakdown inside.
Another day, another groundbreaking piece of research I had to share. This one uniquely ties into one of the biggest threats to OpenAI’s business model: the rapid rise of open-source, and it’s another milestone moment in how fast open-source is advancing.
Fine-tuning an existing model is already a popular and cost-effective way to enhance an existing LLMs capabilities versus training from scratch (very expensive). The most popular method, LoRA (short for Low-Rank Adaption), is already gaining steam in the open-source world.
The leaked Google “we have no moat, and neither does OpenAI memo” calls out Google (and OpenAI as well) for not adopting LoRA specifically, which may enable the open-source world to leapfrog closed-source LLMs in capability.
OpenAI is already acknowledging that the next generation of models is about new efficiencies. This is a milestone moment for that kind of work.
QLoRA is an even more efficient way of fine-tuning which truly democratizes access to fine-tuning (no longer requiring expensive GPU power)
It’s so efficient that researchers were able to fine-tune a 33B parameter model on a 24GB consumer GPU (RTX 3090, etc.) in 12 hours, which scored 97.8% in a benchmark against GPT-3.5.
A commercial GPU with 48GB of memory is now able to produce the same fine-tuned results as the same 16-bit tuning requiring 780GB of memory. This is a massive decrease in resources.
This is open-sourced and available now. Huggingface already enables you to use it. Things are moving at 1000 mph here.
How does the science work here?
QLoRA introduces three primary improvements:
A special 4-bit NormalFloat data typeis efficient at being precise, versus the 16-bit floats and integers which are memory-intensive. Best way to think about this is that it’s like compression (but not exactly the same).
They quantize the quantization constants. This is akin to compressing their compression formula as well.
Memory spikes typical in fine-tuning are optimized, which reduces max memory load required
What results did they produce?
A 33B parameter model was fine-tuned in 12 hours on a 24GB consumer GPU. What’s more, human evaluators preferred this model to GPT-3.5 results.
A 7B parameter model can be fine-tuned on an iPhone 12. Just running at night while it’s charging, your iPhone can fine-tune 3 million tokens at night (more on why that matters below).
The 65B and 33B Guanaco variants consistently matched ChatGPT-3.5’s performance. While the benchmarking is imperfect (the researchers note that extensively), it’s nonetheless significant and newsworthy.
What does this mean for the future of AI?
Producing highly capable, state of the art models no longer requires expensive compute for fine-tuning. You can do it with minimal commercial resources or on a RTX 3090 now. Everyone can be their own mad scientist.
Frequent fine-tuning enables models to incorporate real-time info. By bringing cost down, this is more possible.
Mobile devices could start to fine-tune LLMs soon. This opens up so many options for data privacy, personalized LLMs, and more.
Open-source is emerging as an even bigger threat to closed-source. Many of these closed-source models haven’t even considered using LoRA fine-tuning, and instead prefer to train from scratch. There’s a real question of how quickly open-source may outpace closed-source when innovations like this emerge.
P.S. If you like this kind of analysis, the author offers a free newsletter that tracks the biggest issues and implications of generative AI tech. It’s sent once a week and helps you stay up-to-date in the time it takes to have your Sunday morning coffee.
Superintelligence: OpenAI Says We Have 10 Years to Prepare
Sam Altman was writing about superintelligence in 2015. Now he’s back at it. In 2015 he had his blog. Today, in 2023, he has the world’s future in his hands—or does he?
In 2015, Altman wrote a two–part blog post on why we should fear and regulate superintelligence (a must-read I should say if you want to understand his vision).
After reading them, it makes sense. Altman’s message is visionary, clairvoyant even.
He was writing about superintelligence eight years ago and now he has in his hands the future of the world—and the opportunity to implement all those crazy beliefs. The cycle is closing. OpenAI’s founders say we’re entering the final phase of this journey.
The post they’ve just published echoes Altman’s words: We should be careful and afraid. The only way forward is regulation. There’s no going back. Superintelligence is inevitable.
But there’s another reading; like a self-fulfilling prophecy. Or the appearance of one.
Let me ask you this: Do you think these three months of AI progress (or six, let’s be generous and include ChatGPT’s release) warrant this change of discourse?
You can read my complete analysis for The Algorithmic Bridgehere.
Microsoft launched Jugalbandi, an AI chatbot designed for mobile devices that can help all Indians — especially those in underserved communities — access information for up to 171 government programs.
Elon Musk thinks AI could become humanity’s uber-nanny.
Google introduces Product Studio, a tool that lets merchants create product imagery using generative AI.
Microsoft has launched the AI data analysis platform Fabric, which enables customers to store a single copy of data across multiple applications and process it in multiple programs. For example, data can be utilized for collaborative AI modeling in Synapse Data Science, while charts and dashboards can be built in Power BI business intelligence software.
Latest AI Trends in May 2023: May 23rd, 2023
Is Meta AI’s Megabyte architecture a breakthrough for Large Language Models (LLMs)?
Meta AI’s release of the Megabyte architecture presents a significant advancement in the field of AI, specifically for Large Language Models (LLMs). This architecture enables the support of over 1 million tokens, making it a potential game changer in the scale and complexity of tasks that LLMs can handle. Some experts suggest that even OpenAI might consider adopting this architecture. Discover more about this development here.
What does Google’s new Generative AI Tool, Product Studio, offer?
Google’s Product Studio is a revolutionary Generative AI tool aimed at leveraging artificial intelligence for product design and innovation. This tool brings forth new possibilities in automating and optimizing the product development process. For a comprehensive overview of Product Studio, check out our article here.
Why does Geoffery Hinton believe that AI learns differently than humans?
Geoffery Hinton, known as the Godfather of AI, has made several observations regarding the learning mechanisms of artificial intelligence. He suggests that AI processes information and learns in a manner that is fundamentally different from human learning. This difference may dictate the trajectory of AI evolution and its potential applications. For a deeper understanding of Hinton’s perspectives, read our full report here.
What is the essence of the webinar on Running LLMs performantly on CPUs Utilizing Pruning and Quantization?
This webinar focuses on techniques to optimize the performance of Large Language Models (LLMs) on Central Processing Units (CPUs). Specifically, it discusses the benefits and application of pruning and quantization strategies. To find more about this, click here.
When will AI surpass Facebook and Twitter as the major sources of fake news?
The question of when AI might surpass social platforms like Facebook and Twitter as a primary source of fake news is a complex issue. It hinges on advancements in AI technology and its potential misuse in the creation and spread of misinformation. As of now, AI technology, while advanced, is still largely a tool that must be directed. For an in-depth discussion on this topic, refer to our full article here.
AI: Enhancing or Limiting Human Intelligence?
The impact of AI on human intelligence is a topic of ongoing debate. On one hand, AI has the potential to augment human capabilities, providing tools and insights beyond our natural abilities. On the other hand, overreliance on AI could potentially limit the development of certain human skills. To learn more about this fascinating discussion, refer to our full analysis here.
What are Foundation Models?
A Foundation Model is a large AI model trained on a very large quantity of data, often by self-supervised or semi-supervised learning. In other words: the model starts from a “corpus” (the dataset it’s being trained on) and generates outputs, over and over, checking those outputs against the original data. Foundation Models, once trained, gain the ability to output complex, structured responses to prompts that resemble human replies.
The advantage of a foundational model over previous deep learning models is that it is general, and able to be adapted to a wide range of downstream tasks.
What you need to know about Foundation Models
Foundation Models can start from very simple data – albeit vast quantities of very simple data – to build and learn very complex things. Think about how your profession is made up of many interwoven, complex and nuanced concepts and jargon: a good foundational model offers the potential to quickly and correctly answer your questions, using that vast corpus of knowledge to deliver responses in understandable language.
Some things foundation models are good at:
Translation (from one language to another)
Classification (putting items into correct categories)
Clustering (grouping similar things together)
Ranking (determining relative importance)
Summarization (generating a concise summary of a longer text)
Anomaly Detection (finding uncommon or unusual things)
Those capabilities could easily be a great benefit to professionals in their day-to-day work, such as reviewing large quantities of documents to find similarities, variances, and determining which are the highest importance.
What is a Large Language Model?
Large Language Models (LLMs) are a subset of Foundation Models and are typically more specialized and fine-tuned for specific tasks or domains. An LLM is trained on a wide variety of downstream tasks, such as text classification, question-answering, translation, and summarization. That fine-tuning process helps the model adapt its language understanding to the specific requirements of a particular task or application.
Large Language Models are often used for various natural language processing applications and are known for generating coherent and contextually relevant text based on the input provided. But LLMs are also subject to hallucinations, in which outputs confidently assert claims of facts that are not actually true or justified by their training data. This is not necessarily a bad thing in all cases, since it can be advantageous for LLMs to be able to mimic human creativity (like asking the LLM to write song lyrics in the style of Taylor Swift), but it is a serious concern when citing resources in a professional context. Hallucinations related to factual citations have tended to decrease as LLMs are trained more carefully both on vast, diverse data and for specific, particular tasks, and as human reviewers flag those errors.
What you need to know about Large Language Models
We already knew computers were good at manipulating data based on numbers, from Microsoft Excel to VBA to more complex databases. With LLMs, an even greater power of analysis and manipulation can be applied to unstructured data made up of words – such as legal or accounting treatises and regulations, the entire corpus of an organization’s documents, and massive, larger datasets than those.
LLMs promise to be the same force multiplier for professionals who work with words, risks, and decision-making as Excel was for professionals who work with numbers.
What is cognitive computing?
Cognitive computing is a combination of machine learning, language processing, and data mining that is designed to assist human decision-making. Cognitive computing differs from AI in that it partners with humans to find the best answer instead of AI choosing the best algorithm. The example from Deep Learning about healthcare applies here too: doctors use cognitive computing to help make a diagnosis; they are drawing from their expertise but are also aided by machine learning.
What is AutoML?
AutoML refers to the automated process of end-to-end development of machine learning models. It aims to make machine learning accessible to non-experts and improve the efficiency of experts. AutoML covers the complete pipeline, starting from raw data to deployable machine learning models. This involves data pre-processing, feature engineering, model selection, hyperparameter tuning, model validation, and prediction. The main idea is to automate repetitive tasks, which makes it possible to build models in a fraction of the time, with less human intervention.
Why is AutoML Important?
In traditional machine learning model development, numerous steps demand significant human time and expertise. These steps can be a barrier for many businesses and researchers with limited resources. AutoML mitigates these challenges by automating the necessary tasks.
Democratising Machine Learning
By automating the machine learning process, AutoML opens up the field to non-experts.
Individuals or companies that lack resources to hire data scientists can use AutoML tools to build effective models.
Efficiency and Accuracy
AutoML can analyse multiple algorithms and hyperparameters in less time than humans. This process leads to more accurate models by considering a broad array of possibilities that humans might overlook.
Fast Prototyping
AutoML supports rapid prototyping of models. Businesses can quickly implement and test models to make timely data-driven decisions.
Limitations and Future Directions
While AutoML has its advantages, it’s not without limitations. AutoML models can sometimes be a black box, with limited interpretability. Furthermore, it requires significant computational resources. It is important to understand these limitations when choosing to use AutoML.
As machine learning continues to evolve, AutoML is expected to play an increasingly significant role.
In the near future, we can expect more user-friendly interfaces, increased model transparency, and models capable of operating on larger datasets more efficiently. AutoML is just a facet of the broad and intriguing world of artificial intelligence. With advancements in technology, it’s clear that the future of AI holds numerous opportunities and breakthroughs waiting to be explored. In future articles, we’ll explore other AI terminologies such as Edge Computing, Recommender Systems, and Robotics Process Automation. Stay tuned to expand your knowledge of AI and its transformative potential in different domains. Embrace the journey into AI, where learning never stops and every step brings new discoveries and insights.
Daily AI Update (Date: 5/23/2023): News from Meta, Google, OpenAI, Apple and TCS
Meta’s Massively Multilingual Speech (MMS) models expand speech-to-text & text-to-speech to support over 1,100 languages — a 10x increase from previous work, and can also identify more than 4,000 spoken languages — 40 times more than before.
Meta’s AI researchers introduce LIMA, a refined language model aiming to match the performance of GPT-4 or Bard. It is a 65B parameter LLaMa model fine-tuned with the standard supervised loss on only 1,000 carefully curated prompts and responses, without any reinforcement learning or human preference modeling.
Google AI research introduces XTREME-UP, a new benchmark for evaluating multilingual models focusing on under-represented languages. It emphasizes a realistic evaluation setting, including new and existing user-centric tasks and realistic data sizes beyond the few-shot setting.
Apple has posted dozens of job listings focused on AI, indicating that the company may be stepping up its AI efforts to transform its signature products. The roles span areas including visual generative modeling, proactive intelligence, and applied AI research.
TCS has announced an expanded partnership with Google Cloud to launch a new offering called TCS Generative AI. It will utilize Google Cloud’s generative AI services to create custom-tailored business solutions that help clients accelerate their growth and transformation.
OpenAI leaders propose an IAEA-like international regulatory body for governing superintelligent AI.
In recent times, Large Language Models (LLMs) have evolved and transformed Natural Language Processing with their few-shot prompting techniques. These models have extended their usability in almost every domain, ranging from Machine translation, Natural …
Womble Bond Dickinson’s comprehensive Artificial Intelligence (AI) and Machine Learning practice provides comprehensive legal solutions to companies grappling with the complex legal issues arising from this disruptive technology. AI is now widely adopted across
With the rise of machine learning tools such as ChatGPT, we’ve seen a lot of speculation regarding what that looks like for the future of human creativity at work.
Modern large language models (LLMs) are capable of a wide range of impressive feats, including the appearance of solving coding assignments, translating between languages, and carrying on in-depth conversations.
Generative AI That’s Based On The Murky Devious Dark Web Might Ironically Be The Best Thing Ever, Says AI Ethics And AI Law
Daily AI Update (Date: 5/22/2023)
A groundbreaking method called Mind-Video has been developed to reconstruct continuous visual experiences in videos using brain recordings. This innovative approach achieves high-quality video reconstruction with various frame rates by combining masked brain modeling, multimodal contrastive learning, and augmented Stable Diffusion.
Microsoft’s Bing introduces new features and improvements, including chat history, charts and visualizations, export options, video overlay, optimized recipe answers, share fixes, improved auto-suggest quality, and privacy enhancements in the Edge sidebar. These updates enhance the user experience, making search more efficient and user-friendly.
The next iteration of Perplexity has arrived: The interactive AI search companion, Copilot enhances your search experience by providing personalized answers through interactive inputs, leveraging the power of GPT-4.
RoboTire has developed an AI-powered robot that can change a set of 4 wheels in approximately 23 minutes in the U.S., twice as fast as a human technician. The system aims to improve efficiency, reduce labor costs, and address labor shortages.
MS Artificial Nose – An intelligent device that identifies smells with a simple gas sensor and a micro-controller.
AI-generated image of Pentagon explosion causes market drop.
Intel on Monday provided a handful of new details on a chip for AI computing it plans to introduce in 2025 as it shifts strategy to compete against Nvidia and AMD.
Bill Gates says top AI agents will replace search and shopping sites.
AI predicts the function of enzymes: An international team including bioinformaticians from Heinrich Heine University Düsseldorf (HHU) developed an AI method that predicts with a high degree of accuracy whether an enzyme can work with a specific substrate.
‘Deepfake’ scam in China fans worries over AI-driven fraud. A fraud in northern China that used sophisticated “deepfake” technology to convince a man to transfer money to a supposed friend has sparked concern about the potential of artificial intelligence (AI) techniques to aid financial crimes.
One of the topics in AI i’m most interested in is mimetic AI — which are systems that mimic human behavior in the style of a specific human, imagine personal assistants trained on your behavior, or art generators trained on your art, or clones of your voice — that continue to mimic you after you’re dead. Examples of this are already plenty: a synthetic voiceover by the deceased chef Anthony Bourdain caused a global stirr one year ago, the illustration style of artist Kim Jung-Gi was immediately used by a fan to train a Stable Diffusion-model after his death, Muhammad Ahmed developed an AI chatbot in his image for his grandkids he would never meet, recently Sony used an AI-clone of the dead voice actor Kenji Utsumi for an audiobook, Tom Hanks just said that he very well might appear in movies after he’s dead, a viral piece for the SF chronicle told the story of the Jessica Simulation, in which a guy resurrected his dead girlfriend as a chatbot. Also, i just learned that there is a subset of this particular application of AI-tech called Grief Technology, and there is actually a company called AI seance offering an “AI-generated Ouija board for closure“, as they call it. I think this last example in particular is horrible and has important implications on mental health. Grief is a psychological process, in which you learn to accept loss. It’s a deeply personal process i went through twice, and both times were different, and always challenging. Creating an artificial illusion of continuity of a loved one after their death will disrupt this process, which every single human on earth will go through multiple times in their lifes. The consequences are potentially catastrophic for our mental health and it’s not stopping there. A new paper intriguingly titled Governing Ghostbots discusses exactly these implications, and it goes into territory even i didn’t think about: What happens when you train a sexbot on your partner and then she dies? Is continuing that virtual sex-fetish “extreme pornography as involving necrophilia“ and deemed illegal per se then? The paper also speaks about the legal aspects of such a ghostbot being harmful to the deceased’s antemortem persona, at least in germany, there are laws against that called ‘Verunglimpfung des Andenkens Verstorbener‘, translating to ‘disparagement of the memory of the deceased’. Expensive gimmicks like concerts of deceased popstars “performing“ as holograms on stage like Tupac, Whitney Huston or Michael Jackson introduced ethical debates about post-mortem privacy ten years ago, and now, AI-systems open similar tech to everyone, where you can simply build an open source AI-chatbot of your dead grandma, synch it with an animated avatar and make her say whatever on your phone. Do we really want that? Would she approve? But what about being able to make a virtual post mortem memorial where she dances on stage in the style of her most beloved artist, singing her favorite song? Will we all be right back and will you join me in the club at San Junipero?
And while i don’t think we’ll see conscious AI-systems anytime soon or even in my lifetime, just for the sake of the argument: What if we train future AI-systems on real people, they die, and the system gains consciousness or something similar? Then what?
These are philosophical questions related to the Teletransportation paradox explored by Stanislaw Lem in his Dialogs, in one of which he talks about a teleporting machine that effectively kills you in one location while constructing a replica of yourself, atom by atom, in another place. Is that a true continuation of yourself? We can’t know, and we are building digital systems that can perform something that resembles this replication process now.
Finding out about those psychological questions will be one of the most interesting aspects of this technology, extending our philosophical understanding of who we are.
How can we expect aligned AI if we don’t even have aligned humans?
When we talk about AI alignment, we envision designing artificial intelligence that behaves in a way that aligns with human values and goals. But isn’t it fair to ask whether we, as humans, have even been successful in aligning ourselves?
Throughout history, humans have disagreed about almost everything – from politics to religious beliefs, from ethical principles to personal preferences. We’ve not been able to fully ‘align’ on universally acceptable definitions for concepts like ‘good,’ ‘right,’ or ‘justice.’ Even on basic issues, like climate change, we find a vast array of contrasting perspectives, even though the scientific consensus is overwhelmingly one-sided.
It seems we are demanding a degree of alignment from AI that we’ve been unable to achieve amongst ourselves.
What do you all think? Does the persistent discord among humans undermine the idea of perfect AI alignment? If so, how should we approach AI development, and what are the best ways to ensure that AI benefits all of humanity?
According to the I nternet, 50% say the chance of that happening is extremely significant; even 10-20% is very significant probability.
I know there is a lot of misinformation campaigns going on with use of AI such as deepfake videos and whatnot, and that can somewhat lead to destructive results, but do you think AI being able to nuke humans is possible?
Answer:
AI will never “nuke humans”. Let’s be clear about this: The dangers surrounding AI are not inherent to AI. What makes AI dangerous is people.
We need to be concerned about people in positions of power wielding or controlling these tools to exploit others, and we need to be concerned about the people building these tools simply getting it wrong and developing something without sufficient safety built in, or being misaligned with humanity’s best interests.
Rebuke:
That’s what’s happening already and has been gradually increasing for a long time. What is going to occur is a situation where greater than human intelligence will be created which no one will be able to “use” because they won’t be able to understand what it’s doing. Being concerned about bias in a language model is just like being concerned with bias in a language, which is something we’re already dealing with and a problem people have studied. Artificial intelligence is beyond this. It won’t be used by people against other people. Rather, people will be compelled to use it.
We’ll be able to create an AI which is demonstrably less biased than any human and then in the interest of anti-bias (or correct medical diagnoses, or reducing vehicle accidents), we will be compelled to use it because otherwise we’ll just be sacrificing people for nothing. It won’t just be an issue of it being profitable, it’ll be that it’s simply better. If you’re a communist, you’ll also want an AI running things just as much as a capitalist does.
Even dealing with this will require a new philosophical understanding of what humanism should be. Since humanism was typically connected to humans’ rational capability, and now AI will be superior in this capability, we will be tempted to embrace a reactionary, anti-rational form of humanism which is basically what the stated ideology of fascism is.
Exactly how this crisis unfolds won’t be like any movie you can imagine, though parts may be as some things already happening are. But it’ll be just as massive and likely catastrophic as what your imagining.
How much has AI developed these days
How to Pass and Renew Azure Artificial Intelligence Engineer (AI-102) Certificate
In this article, we will discuss Azure Artificial Intelligence Engineer certification. As cloud computing grows, more services are being offered which include artificial intelligence.
Microsoft Azure is one of the leading cloud computing platforms that offer hundreds of services to customers, especially enterprises ranging from cloud infrastructure to big data and artificial intelligence. Microsoft Azure offers comprehensive end-to-end services that are appealing to most organizations.
Microsoft Azure offers a wide variety of cloud certifications including Azure Artificial Intelligence certification. There are now thirteen Microsoft Azure Certifications divided into three levels which are Fundamental, Associate and Expert.
The certifications for Azure Artificial Intelligence have Fundamental and Associate levels only. For the Fundamental level, it’s known as AI-900 or Exam AI-900: Microsoft Azure AI Fundamentals and the Associate level is known as AI-102 or Exam AI-102: Designing and Implementing a Microsoft AI Solution.
Back in June 2021, the certification was known as AI-100 however Microsoft has decided to retire AI-100 and introduced AI-102. There is no expert level for Azure Artificial Intelligence making AI-102 the most desirable certification.
The Future of AI-Generated TV Shows/Movies and Immersive Experiences
In the next decade or so, artificial intelligence (AI) may have advanced enough to create entire TV shows or movies based on a single prompt. Imagine generating a brand new episode of Seinfeld, my all-time favorite show, with a simple request: “Create a Season 7-styled Seinfeld episode where Kramer takes up yoga and Jerry dates a woman who doesn’t shave her legs. Include appearances from Newman and George’s parents.” Thousands of people could create episodes this way, and a ranking system could determine the best AI-generated episodes. This means we could potentially enjoy fresh, high-quality episodes of our favorite shows daily for the rest of our lives. How amazing would that be?
Taking it a step further, envision donning a VR headset and immersing yourself in a personalized episode of Seinfeld. Upon entering the virtual world, you’d find yourself in an apartment in Jerry’s building, and Jerry would welcome you to the neighborhood. You’d be able to interact with the show’s characters, who would respond to your input in real-time, creating a unique episode tailored to your actions and decisions. You could even introduce characters from other shows, like having Rachel from Friends as your girlfriend, and participate in an entirely new storyline.
In this immersive experience, you and Rachel could visit Jerry’s apartment together, joining the original cast members, and engaging in lively conversations and witty banter. Suddenly, a knock on the door reveals the actors from Law & Order, who inform everyone that Newman has been murdered, and one of you is the prime suspect. In this interactive, AI-generated world, you could say or do whatever you want, and all the characters would react accordingly, shaping the story in real-time.
Although I’m speculating that this level of AI-generated entertainment could be possible within 10 years, it might take more time or perhaps arrive even sooner. Regardless, it seems highly probable within our lifetime, and I’m genuinely excited for the incredible, customizable experiences that await us.
AI Daily News on May 19th, 2023
OpenAI launches ChatGPT app for iOS. It will sync conversations, support voice input, and bring the latest improvements to the fingertips of iPhone users. And Android users are next!
Meta is advancing infrastructure for AI in exciting ways. It includes its first-generation custom silicon chip for running AI models, a new AI-optimized data center design, and the second phase of its 16,000 GPU supercomputer for AI research.
Introducing DragGAN- to deform an image with precise control over where pixels go, thus manipulating the pose, shape, expression, and layout of diverse images such as animals, cars, humans, landscapes, etc.
ClearML announces ClearGPT, a secure and enterprise-grade generative AI platform aiming to overcome ChatGPT challenges
More detailed breakdown of these news, tools and knowledge nugget section in the daily newsletter
Scientists use GPT LLM to passively decode human thoughts with 82% accuracy. This is a medical breakthrough that is a proof of concept for mind-reading tech.
Three human subjects had 16 hours of their thoughts recorded as they listed to narrative stories
These were then trained with a custom GPT LLM to map their specific brain stimuli to words
Results
The GPT model generated intelligible word sequences from perceived speech, imagined speech, and even silent videos with remarkable accuracy:
Perceived speech (subjects listened to a recording): 72–82% decoding accuracy.
Imagined speech (subjects mentally narrated a one-minute story): 41–74% accuracy.
Silent movies (subjects viewed soundless Pixar movie clips): 21–45% accuracy in decoding the subject’s interpretation of the movie.
The AI model could decipher both the meaning of stimuli and specific words the subjects thought, ranging from phrases like “lay down on the floor” to “leave me alone” and “scream and cry.
Implications
I talk more about the privacy implications in my breakdown, but right now they’ve found that you need to train a model on a particular person’s thoughts — there is no generalizable model able to decode thoughts in general.
But the scientists acknowledge two things:
Future decoders could overcome these limitations.
Bad decoded results could still be used nefariously much like inaccurate lie detector exams have been used.
P.S. (small self plug) — If you like this kind of analysis, The author offers a free newsletter that tracks the biggest issues and implications of generative AI tech. Readers from a16z, Sequoia, Meta, McKinsey, Apple and more are all fans. It’s been great hearing from so many of you how helpful it is!
Alexa and Siri are powered by conversational AI. These voice assistants use natural language processing and machine learning to perform and learn over time.
Scientific Reports – Diagnosis of autism spectrum disorder based on functional brain networks and machine learning
Google’s new medical LLM scores 86.5% on medical exam. Human doctors preferred its outputs over actual doctor answers. Full breakdown inside.
Why is this an important moment?
Google researchers developed a custom LLM that scored 86.5% on a battery of thousands of questions, many of them in the style of the US Medical Licensing Exam. This model beat out all prior models. Typically a human passing score on the USMLE is around 60% (which the previous model beat as well).
This time, they also compared the model’s answers across a range of questions to actual doctor answers. And a team of human doctors consistently graded the AI answers as better than the human answers.
Let’s cover the methodology quickly:
The model was developed as a custom-tuned version of Google’s PaLM 2 (just announced last week, this is Google’s newest foundational language model).
The researchers tuned it for medical domain knowledge and also used some innovative prompting techniques to get it to produce better results (more in my deep dive breakdown).
They assessed the model across a battery of thousands of questions called the MultiMedQA evaluation set. This set of questions has been used in other evaluations of medical AIs, providing a solid and consistent baseline.
Long-form responses were then further tested by using a panel of human doctors to evaluate against other human answers, in a pairwise evaluation study.
They also tried to poke holes in the AI by using an adversarial data set to get the AI to generate harmful responses. The results were compared against the AI’s predecessor, Med-PaLM 1.
What they found:
86.5% performance across the MedQA benchmark questions, a new record. This is a big increase vs. previous AIs and GPT 3.5 as well (GPT-4 was not tested as this study was underway prior to its public release).
They saw pronounced improvement in its long-form responses. Not surprising here, this is similar to how GPT-4 is a generational upgrade over GPT-3.5’s capabilities.
The main point to make is that the pace of progress is quite astounding.
A panel of 15 human doctors preferred Med-PaLM 2’s answers over real doctor answers across 1066 standardized questions.
This is what caught my eye. Human doctors thought the AI answers better reflected medical consensus, better comprehension, better knowledge recall, better reasoning, and lower intent of harm, lower likelihood to lead to harm, lower likelihood to show demographic bias, and lower likelihood to omit important information.
The only area human answers were better in? Lower degree of inaccurate or irrelevant information. It seems hallucination is still rearing its head in this model.
Are doctors getting replaced? Where are the weaknesses in this report?
No, doctors aren’t getting replaced. The study has several weaknesses the researchers are careful to point out, so that we don’t extrapolate too much from this study (even if it represents a new milestone).
Real life is more complex: MedQA questions are typically more generic, while real life questions require nuanced understanding and context that wasn’t fully tested here.
Actual medical practice involves multiple queries, not one answer: this study only tested single answers and not followthrough questioning, which happens in real life medicine.
Human doctors were not given examples of high-quality or low-quality answers. This may have shifted the quality of what they provided in their written answers. MedPaLM 2 was noted as consistently providing more detailed and thorough answers.
How should I make sense of this?
Domain-specific LLMs are going to be common in the future. Whether closed or open-source, there’s big business in fine-tuning LLMs to be domain experts vs. relying on generic models.
Companies are trying to get in on the gold rush to augment or replace white collar labor. Andreessen Horowitz just announced this week a $50M investment in Hippocratic AI, which is making an AI designed to help communicate with patients. While Hippocratic isn’t going after physicians, they believe a number of other medical roles can be augmented or replaced.
AI will make its way into medicine in the future. This is just an early step here, but it’s a glimpse into an AI-powered future in medicine. I could see a lot of our interactions happening with chatbots vs. doctors (a limited resource).
P.S. If you like this kind of analysis, the author offers a free newsletter that tracks the biggest issues and implications of generative AI tech. It’s sent once a week and helps you stay up-to-date in the time it takes to have your Sunday morning coffee.
Daily AI News on May 18th, 2023:
Tesla has unveiled a new model of its humanoid robot called Tesla Bot. CEO Musk emphasized that the capabilities of the Optimus robot have been severely underestimated, and the demand for such products in the future will far exceed that of Tesla cars.[1]
Canadian company Sanctuary AI has released a new versatile industrial robot called Phoenix, designed for a wide range of work scenarios. Phoenix integrates features such as wide-angle vision, object recognition, and intelligent grasping, achieving human-like operational proficiency.[2]
NVIDIA’s CEO Jensen Huang stated that chip manufacturing is an ideal application for accelerating computing and AI. The next wave of AI will be embodied intelligence.[3]
OpenAI CEO Altman claimed not to have any equity in OpenAI and that his compensation only covers his health insurance, while the company’s valuation has surpassed $27 billion.[4]
Apple is set to launch a series of new accessibility features later this year, including a “Personal Voice” function that allows individuals to create synthetic voices based on a 15-minute audio recording of their own voice.[5]
In light of feeling overwhelmed by AI’s disruption in the workplace I started thinking: What are the current limitations and failings of this generation of AI? I understand this is a rapidly changing field and this list could become outdated rather quickly. That being said, it’s becoming harder and harder to understand the current state of the art, since every post seems to conflate what its capable of doing with what people predict it will be doing in the future. So, without mixing in any predictions, what are the limitations, particularly in relation to human abilities?
I’ll Start.
Generalized Embodiment: Robots are specialized, like burger flipping or welding a car part. There is no current robot that can finish replacing your muffler in the afternoon, then grill you a burger at dinner time.
Hallucinations: current LLMs are susceptible to hallucinations. Sure humans are too, but we reserve extending our trust to them until we know them better, and so far I know a lot of humans I can trust more implicitly than chatGPT
Innovation & Creativity: Correct me if I am wrong, but AI can only parrot and re-arrange ideas they have been trained on (see: Stochastic Parrots). They can’t invent new math or generate a truly novel concept that they haven’t been exposed to.
Morality: There are moral concepts that have been “fine tuned” into the models, but there is no capacity to judge the morality of, for example, when an LLM lies. Does it know its lying? Does it feel there is anything wrong with lying? The best description is that these language models are amoral.
Motivation & Curiosity: I can perceive no sense of internal motivation. Perhaps this is a good thing for now but if an LLM or other AI has no sense of internal motivation (or morality) it can quite easily be used for nefarious purposes by bad actors. Now, to be fair, humans can be manipulated to do this also, but AI could be used in this way without the bother of brainwashing first.
Understanding: I haven’t decided if there is, or is not, some level of emergent property that could qualify for understanding. But I have been fairly unimpressed by chatGPT4’s ability to really understand and extend. It can generate patterns from data it has seen in the past, but only in so much as human understanding can be cross referenced to generate an answer.
Argue: chatGPT readily admits its wrong, but doesn’t seem to know why its wrong, or have the ability to stand its ground when its right. It never seems to say “I don’t know, can you explain this to me?” Look up the story of Vasili Arkhipov, the Russian sub commander that prevented a catastrophe. Can we trust AI to be this bold, or moral?
This article reviews the top three AI voice cloning services, providing a comprehensive analysis of their features, usability, and pricing. It serves as a guide for individuals or businesses seeking to utilize AI for voice cloning. The services are: Descript, Elevenlabs, Coqui.ai
The article discusses a roadmap to achieving fairness in AI models, particularly those used in medical imaging. It highlights the importance of identifying and eliminating biases to ensure accurate and equitable healthcare outcomes.
Main sources of bias in AI models include:
Data collection
Data preparation and annotation
Model development
Model evaluation
System’s users
You can read the Gold Open Access article by K. Drukker et al., “Towards fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment,” J. Med. Imag. 10(6), 061104 (2023), doi 10.1117/1.JMI.10.6.061104
Sanctuary AI has unveiled its first humanoid robot, Phoenix, powered by the AI system, Carbon. Standing at approximately 5’7″ and weighing around 155 lbs, Phoenix represents a significant advancement in humanoid robotics.
AI Daily updates from Microsoft, Google, Zoom, and Tesla
Microsoft launched a LangChain alternative in its new tool- Guidance. It bypasses traditional prompting and allows users to interleave generation, prompting, and logical control into a single continuous flow.
Google Cloud has launched two AI-powered tools to help biotech and pharmaceutical companies accelerate drug discovery and advance precision medicine. Pfizer, Cerevel Therapeutics, and Colossal Biosciences are already using these products.
Humanoid robots are becoming a reality. Sanctuary AI launched Phoenix, a 5’7″ and 55lb dextrous humanoid robot. Hours later, Tesla rolled out a video of humanoids walking around and learning about the real world.
OpenAI chief, Sam Altman, talked about a variety of topics ranging from “AI affecting upcoming elections” to “the future of humanity with AI,” in his appearance before congress. He suggested licensing and testing requirements for AI models.
Zoom announced its partnership with Anthropic to integrate AI assistant across the productivity platform, starting from its Contact Center product. They earlier partnered with OpenAI to launch ZoomIQ.
What does artificial intelligence offer that goes beyond traditional statistical models, such as regression analysis, to investigate the behavior of households, in particular the factors that cause the …
According to a survey, the swift growth of artificial intelligence technology could put the future of humanity at risk. More than two-thirds of Americans are concerned about the negative effects of AI and 61% believe it could threaten civilization.
A machine learning-based model that enables medical institutions to predict the mortality risk for individual cardiac surgery patients has been developed by a Mount Sinai research team, providing a significant performance advantage over current population-derive
Kaiser Permanente has launched a new AI and machine learning program to grant up to $750,000 to 3-5 health systems to improve diagnoses and patient outcomes.
A machine learning-based model appeared to improve prediction of mortality risk for patients undergoing cardiac surgery compared with population-derived models, researchers reported.“The standard-of-care risk models used today are limited by their applicability t
TTS systems and artificially intelligent video creators are revolutionizing how we engage with information. In today’s increasingly digital environment, people value having ready access to a wide variety of content, including human voices. Modern technology has made it possible to hear articles, novels…
A provocative paper from researchers at Microsoft claims A.I. technology shows the ability to understand the way people do. Critics say those scientists are kidding themselves.
Google Research announced Universal Speech Model (USM), a 2B parameter automated speech recognition (ASR) model trained on over 12M hours of speech audio. USM can recognize speech in over 100 languages, including low-resource languages, and …
While generative AI, the flavor of artificial intelligence behind ChatGPT, has the potential to transform fields such as healthcare, physics, biology, and climate mode…
In order to give us an advanced warning about the next destructive solar storm, NASA is leveraging a new AI and machine learning-based technology called DAGGER. Check the details.
Research explores sex-specific gene associations in Alzheimer’s disease using a machine-learning approach. It reveals immune response pathways in both sexes and stress-response pathways in males, highlighting potential biomarkers and therapeutic targets…
Top 10 Best Artificial Intelligence Courses & Certifications
Dive into 10 top-tier AI courses that can empower you to stay competitive in the rapidly evolving landscape of artificial intelligence.
This is a five-course series that helps you understand the foundations of deep learning, learn how to build neural networks, and understand how to lead successful machine learning projects.
IBM’s AI Engineering program covers foundational concepts in machine learning and deep learning, with an emphasis on practical application and the use of popular tools and libraries.
This program focuses on important elements of AI like robotics, computer vision, and NLP. Real-world projects are a highlight of the course, offering hands-on experience.
This professional certificate program will introduce you to the basics of AI. Topics include machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.
This course combines theory with hands-on activities to understand the complex and often misunderstood field of artificial intelligence. The course uses tools like TensorFlow, Keras, and OpenAI Gym.
Designed for non-technical professionals, this course helps you understand AI terminology and concepts, its impact on society, and how to navigate through these emerging technologies.
This program provides a comprehensive introduction to the field of data science, including statistical inference, machine learning, and data visualization.
The portrayal of sentient AI as inherently evil in popular culture is a fascinating trend that often reflects society’s anxieties around technological advancements. This article from The AI Journal delves into the topic, exploring how the narrative around AI has been shaped by societal fears and the potential implications of this in the real world. The piece also discusses the need for a more nuanced approach to understanding AI and its potential benefits as well as dangers.
The article from AI Coding Insights focuses on semantic pseudocode, a conceptual method used in the field of computer science and AI for representing complex algorithms. The author explores the existence of this system, its application in AI development, and its potential impact on the broader field of artificial intelligence. The piece also provides a brief overview of the history and evolution of semantic pseudocode, underscoring its importance in the AI industry.
“Would AI be subject to the same limitations as humans in terms of intelligence? How could it possibly be a danger if it was?”
The article from AI News presents a thought-provoking exploration of the limitations and potential dangers associated with artificial intelligence. The author argues that while AI has the potential to surpass human intelligence in certain areas, it may still be subject to limitations similar to those of human cognition. The article further discusses the potential risks that could arise from AI, including ethical considerations, misuse of technology, and the possibility of AI systems developing unintended behaviors.
ChatGPT has become an overnight sensation, but the technical developments that enabled it took decades to emerge. In this article, I discuss what ChatGPT is, how it developed and executive strategies to navigate the opportunities.
Italy on Monday earmarked 30 million euros ($33 million) to improve the skills of unemployed people as well as those workers whose jobs could be most at risk from the advance of automation and artificial intelligence.
The emergence of text-to-image generator models has transformed the art industry, allowing anyone to create detailed artwork by providing text prompts. These AI models have gained recognition, won awards, and found applications in various media. However, their widespread use has negatively impacted ….
A machine learning model able to screen individuals with Alzheimer’s dementia from individuals without it by examining speech traits typically observed among people with the disease could one day become a tool that makes earlier diagnosis possible.
Heart attack symptoms are sometimes similar to non-heart-related conditions, making diagnosis tricky. UK researchers have turned to machine learning to provide doctors with a fast and accurate way of diagnosing heart attacks that has the potential to shorten the time needed to make a diagnosis and provide…
Top 9 Essential Programming Languages in the Realm of AI
Python:Python is the most widely used language in machine learning and artificial intelligence today. It serves as the cornerstone for most of A.I. since it is a basic yet strong language. Many programmers have conducted cost-benefit analyses that indicate that adopting Python speeds up development without losing quality.
R Language: A language that is frequently used by professionals who specialize in the assessment, analysis, and manipulation of statistical data. R allows you to create a publication-read graphic replete with equations and mathematical calculations.
Lisp: Lisp offers a lot of advantages that are still relevant in the twenty-first century. It excels at prototyping and enables the easy dynamic generation of new items while automatically clearing away rubbish. The development cycle of Lisp makes it simple to evaluate expressions and recompile functions in an ongoing application.
Prolog: Prolog has several uses outside of the healthcare field. It’s also excellent for A.I. Prolog excels in pattern matching thanks to its tree-based data structure and automated backtracking. It’s an excellent arrow to have in your quiver as an A.I. expert.
Java: Java is likely to help you advance in your profession since it is the most extensively used programming language on the planet and can be utilized in a variety of scenarios other than A.I. It is incredibly popular due to its adaptability, and it may be utilized in conjunction with algorithms, artificial neural networks, and other key components of A.I.
C++: C++ is well-known for its performance and efficiency, making it an excellent choice for building AI models in production scenarios where resources are limited and speed is crucial.
Julia:Julia is swiftly emerging in the field of artificial intelligence because of its strong visuals for data visualization and dynamic interface. Julia’s high-level, simple syntax and outstanding computational capabilities make it an appealing choice for AI researchers and developers. Its ability to effortlessly connect with existing libraries in languages such as C and Python broadens its appeal by allowing it to be seamlessly integrated into current projects.
Haskell: Memory management in Haskell is extremely efficient. Haskell’s memory management efficiency helps it to reduce resource usage and the possibility of typical programming problems like uninitialized variables or null pointers. Haskell’s robust type system and mathematical roots make it well-suited for sophisticated algorithms and data manipulation tasks, which are frequently encountered in AI and machine learning applications.
Generative AI. This is the term in the AI domain recently. Everyone is talking about it, and it keeps getting more and more impressive. With each passing day, the capabilities of AI models in generating realistic and high-quality content continue to impress. For example, we have seen AI models that can
Anthropic’s Claude AI demonstrates an impressive leap in natural language processing capabilities by digesting entire books, like The Great Gatsby, in just seconds. This groundbreaking AI technology could revolutionize fields such as literature analysis, education, and research.
OpenAI has published groundbreaking research that provides insights into the inner workings of neural networks, often referred to as “black boxes.” This research could enhance our understanding of AI systems, improve their safety and efficiency, and potentially lead to new innovations.
Google has announced the development of PaLM 2, a cutting-edge AI model designed to rival OpenAI’s GPT-4. This announcement marks a significant escalation in the AI race as major tech companies compete to develop increasingly advanced artificial intelligence systems.
A recent leak of MSI UEFI signing keys has sparked concerns about a potential “doomsday” supply chain attack. The leaked keys could be exploited by cybercriminals to compromise the integrity of hardware systems, making it essential for stakeholders to address the issue swiftly and effectively.
Google has released its ChatGPT competitor to the US market, offering users access to advanced AI-powered conversational features. This release brings new capabilities and enhancements to the AI landscape, further intensifying the competition between major tech companies in the AI space.
Anthropic introduces a novel approach to AI development with its Constitutional AI chatbot, which is designed to incorporate a set of “values” that guide its behavior. This groundbreaking approach aims to address ethical concerns surrounding AI and create systems that are more aligned with human values and expectations.
Spotify has removed thousands of AI-generated songs from its platform in a sweeping effort to combat fake streams. This purge highlights the growing concern over the use of AI in generating content that could distort metrics and undermine the value of genuine artistic works.
With the ongoing Artificial Intelligence boom, it is very important to understand the terminology in use. Here are 17 AI and machine learning terms everyone needs to know.
ANTHROPOMORPHISM, BIAS, CHATGPT, BING, BARD, ERNIE, EMERGENT BEHAVIOR, GENERATIVE AI, HALLUCINATION, LARGE LANGUAGE MODEL, NATURAL LANGUAGE PROCESSING, NEURAL NETWORK, PARAMETERS, 14. PROMPT, REINFORCEMENT LEARNING, TRANSFORMER MODEL, SUPERVISED LEARNING
AI and machine learning have the potential to bring both positive and negative impacts to society. While they can improve efficiency, help with decision-making, and create new opportunities, they can also raise ethical concerns, job displacement, and security issues. Learn more
A recent study found that AI models struggle to reproduce human judgments regarding rule violations, highlighting the challenges of making AI systems align with human values and understand the nuances of ethical behavior. Learn more
AI is being used to enhance mapping applications by adding features like more realistic 3D models, better route planning, more accurate traffic information, and improved localization. These innovations make maps more interactive and user-friendly. Learn more
Fast-food brands are utilizing machine learning to optimize their marketing efforts. Techniques include predictive analytics, personalization, and automating ad campaigns, which help companies better target customers, improve customer experiences, and increase sales. Learn more
Jacob Andreas, an assistant professor at MIT, discusses the benefits and challenges of large language models, such as their ability to generate human-like text and their potential biases, as well as the importance of interdisciplinary research in AI development. Learn more
Unplanned downtime can be a major headache for plant operators and engineers, causing production losses and reduced profits. Predictive maintenance with machine learning offers a way to prevent downtime by identifying potential equipment failures before they occur.
AI will create new jobs in fields like data science, AI ethics, robotics, and AI research. Preparing for these jobs involves acquiring relevant skills, staying updated with technological advancements, and being adaptable to change. Learn more
While AI is unlikely to kill us all, there are potential risks associated with its development, such as loss of control over AI systems, the malicious use of AI, and unintended consequences from AI deployment. Ensuring AI safety and ethics is crucial to mitigate these risks. Learn more
While TidyBot’s technology may be impressive, it is essential to consider the rapid evolution of the AI sector. As technology continues to advance, what appears impressive today may soon become obsolete or surpassed by newer innovations. Staying informed about the latest developments is crucial. Learn more
The AI rights movement is in its early stages, and advocates are encouraging the submission of exceptional creative works produced by AI. This effort aims to raise awareness about AI’s capabilities and potential rights while fostering appreciation for AI-generated art and creativity. Learn more
Bard, an AI language model, faces censorship issues when attempting to translate or generate content in unsupported languages. These limitations arise from a combination of technical challenges, biases in training data, and concerns about the potential for spreading misinformation. Learn more
GPT-4 can solve difficult problems with greater accuracy, thanks to its broader general knowledge and problem solving abilities. Creativity. Visual input. Longer context.
Some researchers believe that consciousness arises from complex computations among brain cells, while others think it emerges from simpler physical processes.
Google has launched a new tool called Bard that allows users to create poems using artificial intelligence (AI). However, it is only available in the US for now.
A ChatGPT trading algorithm has delivered 500% returns in the stock market. The algorithm uses natural language processing (NLP) to analyze news articles and social media posts to predict stock prices.
Researchers are still struggling to understand how AI models trained to parrot internet text can perform advanced tasks such as running code, playing games and trying to break up a marriage.
In recent years the United States government has expanded its use of artificial intelligence as the development of machine learning technology continues.
New research from ESMT Berlin shows that utilizing machine-learning in the workplace always improves the accuracy of human decision-making, however, often it
Google announced 100 new features and products at its annual I/O developer conference, including updates to Google Assistant, Google Maps, and Google Photos.
Google has unveiled Project Gameface, a hands-free gaming mouse that uses artificial intelligence (AI) technology to track players’ movements and respond to their commands.
Google has called on companies to be more responsible when developing artificial intelligence (AI) technologies, saying that being bold on AI means being responsible from the start.
Google has unveiled PaLM 2, a new natural language processing (NLP) model that can understand complex sentences and phrases with greater accuracy than previous models.
Google has announced that its Bard platform will become more global, visual, and integrated in the coming months, with new features and tools designed to help users create more engaging content.
Google has launched Magic Editor in Google Photos, a new feature that uses artificial intelligence (AI) technology to automatically enhance photos and create new effects.
Google has announced new ways that artificial intelligence (AI) technology is making Maps more immersive, including improved navigation tools and more detailed maps of indoor spaces.
Google has unveiled MusicLM, a new tool that uses artificial intelligence (AI) technology to turn ideas into music by analyzing patterns in sound waves.
Latest AI Trends in May 2023: May 10th, 2023
AI based technology most important parts of the future?
AI-based technology is poised to play a crucial role in shaping the future across various domains. Here are some important parts where AI is expected to have a significant impact:
Automation and Robotics: AI enables automation of tasks that traditionally required human intervention. From manufacturing and logistics to household chores and healthcare, AI-powered robots and automation systems can enhance efficiency, precision, and productivity.
Healthcare and Medicine: AI has the potential to revolutionize healthcare. It can aid in disease diagnosis, drug discovery, personalized medicine, and treatment planning. AI algorithms can analyze vast amounts of medical data to identify patterns and make predictions, leading to more accurate diagnoses and improved patient outcomes.
Autonomous Vehicles: Self-driving cars and autonomous vehicles rely heavily on AI technologies, including computer vision, machine learning, and sensor fusion. AI enables these vehicles to perceive their environment, make real-time decisions, and navigate safely, potentially reducing accidents and transforming transportation.
Natural Language Processing (NLP): NLP is a branch of AI that focuses on enabling computers to understand, interpret, and respond to human language. NLP applications range from virtual assistants and chatbots to language translation, sentiment analysis, and voice recognition. NLP advancements can enhance human-computer interactions and facilitate cross-cultural communication.
Cybersecurity: With the increasing complexity of cyber threats, AI-powered security systems can help detect and prevent cyberattacks. AI algorithms can analyze network traffic patterns, identify anomalies, and respond in real-time to mitigate potential breaches, thereby bolstering overall cybersecurity.
Education: AI has the potential to transform education by providing personalized learning experiences, intelligent tutoring, and adaptive assessments. AI-powered tools can analyze individual student performance data, identify areas for improvement, and deliver targeted instructional content.
Scientific Research: AI is increasingly being used in scientific research to analyze complex datasets, simulate experiments, and accelerate discoveries. It can help researchers in fields such as genomics, astronomy, material science, and drug discovery to unlock new insights and drive innovation.
It’s important to note that while AI brings tremendous potential, there are also ethical considerations, such as privacy, bias, and accountability, that need to be addressed as AI technology continues to advance.
The Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) at MIT has launched its inaugural Grand Challenge, which aims to develop enhanced crop variants and move them from lab to land.
There’s a lot to learn about deep learning; start by understanding these fundamental algorithms.
Convolutional Neural Networks (CNNs), also known as ConvNets, are neural networks that excel at object detection, image recognition, and segmentation. They use multiple layers to extract features from the available data. CNNs mainly consist of four layers:
Convolution layer
Rectified Linear Unit (ReLU)
Pooling Layer
Fully Connected Layer
Deep Belief Networks (DBNs) are another popular architecture for deep learning that allows the network to learn patterns in data with artificial intelligence features. They are ideal for tasks such as face recognition software and image feature detection.
Recurrent Neural Network (RNN) is a popular deep learning algorithm with a wide range of applications. The network is best known for its ability to process sequential data and design language models. It can learn patterns and predict outcomes without mentioning them in the code. For example, the Google search engine uses RNN to auto-complete searches by predicting relevant searches.
Long Short Term Memory Networks (LSTMs) are a Recurrent Neural Network (RNN) type that differs from others in their ability to work with long-term data. They have exceptional memory and predictive capabilities, making LSTMs ideal for applications like time series predictions, natural language processing (NLP), speech recognition, and music composition.
Generative Adversarial Networks (GANs) are a type of deep learning algorithm that supports generative AI. They are capable of unsupervised learning and can generate results on their own by training through specific datasets to create new data instances.
Multilayer Perceptron (MLP) is another deep learning algorithm, which is also a neural network with interconnected nodes in multiple layers. MLP maintains a single data flow dimension from input to output, which is known as feedforward. It is commonly used for object classification and regression tasks.
Autoencoders are a type of deep learning algorithm used for unsupervised learning. It’s a feedforward model with a one-directional data flow, similar to MLP. Autoencoders are fed with input and modify it to create an output, which can be useful for language translation and image processing.
Womble Bond Dickinson’s comprehensive Artificial Intelligence (AI) and Machine Learning practice provides comprehensive legal solutions to companies grappling with the complex legal issues arising from this disruptive technology. AI is now widely adopted across industries globally.
Artificial intelligence is revolutionizing the international medical field, and in the near future, its role in our hospitals is expected to just keep growing.
Video anomaly detection, which differs from traditional video analysis, is a research hotspot in the field of computer vision, attracting many researchers. Usually, abnormal events occur only in a small….
Examples of generative AI include chatbots like ChatGPT and image generators like Midjourney, but how do they work?
Researchers create a tool for accurately simulating complex systems
Researchers have developed a new computational tool that enables accurate simulation of complex systems, such as biological processes, climate models, and social networks. This innovative tool can significantly improve the understanding and prediction of complex system behavior. Learn more
Researchers develop novel AI-based estimator for manufacturing medicine
A team of researchers has created an AI-based estimator for optimizing the manufacturing process of pharmaceuticals. This innovative approach can help improve the quality and efficiency of drug production, potentially reducing costs and increasing accessibility to life-saving medications. Learn more
Deep-learning system explores materials’ interiors from the outside
Scientists at MIT have developed a deep-learning system that can analyze the internal structure of materials based on external data. This groundbreaking technology has the potential to transform fields such as materials science, engineering, and quality control by providing insights into material properties without invasive procedures. Learn more
AI system can generate novel proteins that meet structural design targets
Researchers have developed an AI system capable of designing novel proteins with specific structural characteristics. This innovative technology could pave the way for new therapeutic strategies, advanced materials, and a deeper understanding of protein function and folding. Learn more
A team of researchers in Carnegie Mellon University’s Computational Biology Department (CBD) have developed new methods to identify parts of the genome critical to understanding how certain traits of …
OpenAI’s losses roughly doubled to around $540 million last year as it developed ChatGPT and hired key employees from Google, according to three people with knowledge of the startup’s financials.
OpenAI lost $540M in 2022, will need $100B more to develop AGI, says Altman.
What to know:
OpenAI lost $540M in 2022 and generated just $28M in revenue. Most of it was spent on developing ChatGPT.
OpenAI actually expects to generate more than $200M in revenue this year (thanks to ChatGPT’s explosive popularity), but its expenses are going to increase incredibly steeply.
One new factor: companies want it to pay lots of $$ for access to data. Reddit, StackOverflow, and more are implementing new policies. Elon Musk personally ordered Twitter’s data feed to be turned off for OpenAI after learning they were paying just $2M per year.
Altman personally believes they’ll need $100B in capital to develop AGI. At that point, AGI will then direct further improvements to AI modeling, which may lower capital needs.
Why this is important:
AI is incredibly expensive to develop, and one of the hypotheses proposed by several VCs is that big companies will benefit the most in this arms race.
This may actually be true with OpenAI as well — Microsoft, which put $10B in the company recently, has a deal where they get 75% of OpenAI’s profits until their investment is paid back, and then 49% of profits beyond.
The enormous amount of capital required to launch foundational AI products also means other companies may struggle to make gains here. For example, Inflection AI (founded by a DeepMind exec) launched its own chatbot, Pi, and also raised a $225M “Seed” round. But early reviews are tepid and it’s not made much of a splash. ChatGPT has sucked all the air out of the room.
Don’t worry about OpenAI’s employees though: rumor has it they recently participated in a private stock sale that valued the company at nearly $30B. So I’m sure Altman and company have taken some good money off the table.
Found this list of Free AI courses for beginners and experts to learn artificial intelligence for free. It’s free, try it for yourself. Happy Learning!
President Biden’s rules are not legally binding, but they do offer guidance and begin a conversation at the national level about real and existential threats posed by generative AI technologies such as ChatGPT.
AI has the power to use enormous amounts of data and integrate with fitness trackers to change the way people monitor and improve their health. Kale smoothies and dead lifts aren’t magic bullets. Following generic fitness and nutrition plans won’t guarantee a thing. Thankfully, it’s clear that AI is going to revolutionize health and wellness in the coming years. With AI, fitness and nutrition advice will no longer be subjected to the one-size-fits-all approach from misguided Instagram influencers or even well-intentioned and educated nutritionists.
AI experts at MIT this week admitted there’s nothing on the horizon that indicates generative AI technology such as ChatGPT will ever be free of mistakes and could well be used for malicious purposes.
The Runway is an AI-driven content creation, editing, and collaboration suite. Runway streamlines the monotonous, time-consuming, and error-prone parts of content generation and video editing while giving users complete editorial freedom. Text-to-picture creation, erasing and replacing text, AI training, text-to-color grading, super slow motion, image-to-image generation, and endless image are just some of the AI-powered creative capabilities it provides. Video editing techniques such as green screen, inpainting, and motion tracking are also included.
Hugging Face’s development community created the ModelScope Text To Video Synthesis tool, which uses machine learning. Users may use this tool’s deep learning model to generate movies from the text.
Synthesia.io is a platform designed to make making and sharing interactive videos easier. The goal of Synthesia.io is to make it easier for anyone to make videos that are both interesting and useful for a wide range of reasons, such as advertising, training, and product demonstrations.
Kaiber is an artificial intelligence-driven video generator that lets users create spectacular graphics using their photographs or written descriptions.
Aug X Labs, an AI-driven video technology and publishing firm, aims to make it possible for everyone to create videos. Their revolutionary “Prompt to Video” technology makes it simple for storytellers like podcasters, radio presenters, comedians, musicians, etc., to include captivating visuals in their work.
With AI, the smartphone software Supercreator.ai makes producing unique short films for platforms like TikTok, Reels, Shorts, and more simple and quick.
Topaz Labs’ Topaz Video Enhance AI is a powerful upscaling tool using cutting-edge machine learning technology to enhance video resolutions up to 8K automatically.
Wisecut is an autonomous online video editing application that uses artificial intelligence and speech recognition to streamline editing. You may use it to make short, powerful videos with audio, subtitles, face detection, auto reframe, and more.
A video search engine powered by artificial intelligence, Twelve Labs enables programmers to create software that can “see,” “hear,” and “understand” the environment in the same ways that people do. It gives programmers access to the best video search API available.
vidBoard.ai is a robust artificial intelligence platform for making films from the text. It’s easy to use, and you can choose from many different premade themes and AI presenters.
With artificial intelligence, Vidyo.ai allows users to quickly and easily transform their lengthy podcasts and videos into bite-sized chunks more suited for sharing on services like TikTok, Reels, and Shorts.
In minutes without needing professional cameras, performers, or studios, users of the AI-powered video production tool Yepic Studio may produce and translate engaging talking head-type videos.
Recent advancements in artificial intelligence have led to significant improvements in mind-reading capabilities. This progress has the potential to revolutionize various fields, including medicine, communication, and accessibility for individuals with disabilities.
Patients in this study, who had overall significantly higher scores than controls, fell into 3 categories of psychological risk from temporal lobe epilepsy (TLE) based on analysis with unsupervised machine learning.
ARTIFICIAL INTELLIGENCE IS changing the way we think about authorship, art, and white collar work. It may be changing how we think, full stop. As artificial intelligence, or machine learning, becomes more integrated into people’s everyday lives, it runs the risk of..
To get an inside look at the heart, cardiologists often use electrocardiograms (ECGs) to trace its electrical activity and magnetic resonance images (MRIs) to map its structure. Because the two types …
Xavier “X” Jernigan is the voice model for Spotify’s AI DJ. Jernigan shares with TechCrunch what the process was like and potential future plans for the feature
If you’ve ever gone through a stressful period of life, only to think how much older you looked on the other side, you may relate to the findings of a new study.
The feature, which may have rolled out with a major bug, is available for Twitter Blue subscribers, but what’s the point given that Twitter is a short-form content platform?
A team of scientists discovered what could be a new mineral in the ‘fossilized remains’ of a lightning strike, showing some striking similarities to minerals found so far only in meteorites.
#1-Ranked Industry Analyst Patrick Moorhead dives in as Google noted a recent dramatic increase in ML predictions and ML evaluations (different evaluation metrics to understand a machine learning model’s performance)—perhaps a precursor for more companies succeeding with models in production.
App developers must consider who will use their app when in development to ensure they are creating safe spaces for kids and that their data is not being tracked or shared.
OpenAI, the firm behind chatbot sensation ChatGPT, said on Tuesday that it would offer up to $20,000 to users reporting vulnerabilities in its artificial intelligence systems.
Alibaba Group Holding Ltd on Tuesday unveiled Tongyi Qianwen, an AI large language model similar to GPT that it plans to integrate into all of the company’s business applications in the near future.
SpaceX released a new promotional video on Monday with some absolutely stunning animated imagery. The video imagines what it may look like if the company’s Starship rocket makes it to Mars one day. And it looks incredible.
Asia Times: Do Japanese manufacturers use ChatGPT? ChatGPT: It is possible that some Japanese manufacturers use ChatGPT or other similar language models for various applications…
Nationwide Children’s Hospital researchers utilized a machine- learning tool with an EHR-integrated risk index algorithm to alert providers of early pediatric deterioration.
The demand for AI and machine learning talent has increased by 75% over the last few years, creating abundant job opportunities. Various careers in AI require specialization in specific sets of skills and responsibilities. The top in-demand AI careers include Machine Learning Engineer, Data Scientist, AI
Top Tech Trends in April 2023: More AI/ML Trends in April 2023
Will artificial intelligence and machine learning technologies save the world or send it into chaos? Only time will tell. However, as these technologies continues to improve, it definitely seems like …
FRIDAY, April 14, 2023 (HealthDay News) — Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates, according to ….
Women in Data Science (WiDS) Blacksburg – which is free and open to all genders – is one of an estimated 200 regional WiDS events worldwide designed to feature outstanding women doing outstanding women …
Science X network: Science X is a network of high quality websites with most complete and comprehensive daily coverage of the full sweep of science, technology, and medicine news
I am giving a talk on Optimal Transport and Information Geometry at the SIAM Conference on Mathematics of Data Science (MDS22). The talk is intended to be an introduction which doesn’t assume any background on either subject, although I did assume some familiarity with probability.
If you are a data scientist and looking for making some extra income, then here are the top 10 ways to earn passive income as a data scientist in 2023.
Users should know the signs of malware on Android devices to ensure that endpoints stay secure. Learn how to detect and remove malware on Android phones.
Company Asus has announced its latest Android-powered gaming smartphone. I’ve spent time with the ROG Phone 7 Ultimate to find out just how much gaming it delivers.
For those who hang on to phones for longer periods of time or who decided not to break the bank and buy a $1,000 phone, a lack of storage can be a problem. Specifically, running out of space as…
When ChatGPT was launched, it was a great chatbot that captured users’ attention, but the introduction of plug-ins has changed the game in technology. If users start using plug-ins instead of apps, Apple (NASDAQ: AAPL) and Alphabet (NASDAQ: GOOG) (NASDAQ: GOOGL) will feel the hit
NEW YORK, N.Y., April 17, 2023 (SEND2PRESS NEWSWIRE) — It is true that many Android users are switching over to iPhones but are worried about the troublesome process of transferring…
Welcome to our latest blog post, where we delve into the most exciting AI trends that have emerged in April 2023. As artificial intelligence continues to transform industries and reshape the world, staying informed about the latest advancements is crucial for professionals, enthusiasts, and curious minds alike. In this post, we’ll explore groundbreaking research, innovative applications, and emerging technologies that are pushing the boundaries of AI. From the recent merger of Google Brain and DeepMind to the latest developments in generative AI, we’ll provide you with a comprehensive update on the AI landscape in April 2023. So, grab a cup of your favorite beverage and join us on this fascinating journey through the cutting-edge world of artificial intelligence!
Researchers have figured out a way to predict bacteria’s environmental pH preferences from a quick look at their genomes, using machine learning. Led by experts at the University of Colorado Boulder
Whether it’s bad data or bad users, AI created with machine learning can end up making serious mistakes.
1. Google Image Search Result Mishaps
Google Search has made navigating the web a whole lot easier. The engine’s algorithm takes a variety of things into consideration when churning up results. But the algorithm also learns from user traffic, which can cause problems for search result quality.
2. Microsoft Bot Tay Turned Into a Nazi
AI-powered chatbots are extremely popular, especially those powered by large language models like ChatGPT. ChatGPT has several problems, but its creators have also learned from the mistakes of other companies.
3. AI Facial Recognition Problems
Facial recognition AI often makes headlines for all the wrong reasons, such as stories about facial recognition and privacy concerns. But this AI has a problematic history when attempting to recognize people of color.
4. Deepfakes Used for Hoaxes
While people have long used Photoshop to create hoax images, machine learning takes this to a new level. Deepfakes use deep learning AI to create fake images and videos. Software like FaceApp allows you to face-swap subjects from one video into another.
5. Employees Say Amazon AI Decided Hiring Men Is Better
In October 2018, Reuters reported that Amazon had to scrap a job-recruitment tool after the software’s AI decided that male candidates were preferential.
Employees who wished to remain anonymous came forward to tell Reuters about their work on the project. Developers wanted the AI to identify the best candidates for a job based on their CVs. However, people involved in the project soon noticed that the AI penalized female candidates. They explained that the AI used CVs from the past decade, most of which were from men, as its training dataset.
6. Jailbroken Chatbots
While newer chatbots have limitations in place to prevent them from giving answers that go against their terms of service, users are finding ways to jailbreak the tools to provide banned content.
In 2023, a Forcepoint security researcher Aaron Mulgrew was able to create zero-day malware using ChatGPT prompts.
7. Self-Driving Car Crashes
Enthusiasm for autonomous vehicles has been dampened from its initial hype stage due to mistakes made by self-driving AI. In 2022, The Washington Post reported that in roughly a year, 392 crashes involving advanced driver-assistance systems were reported to the US National Highway Traffic Safety Administration.
Artificial intelligence is revolutionizing healthcare by enabling personalized medicine, improving diagnostics, predicting patient outcomes, and optimizing treatment plans. AI-driven tools, such as machine learning algorithms and natural language processing, are enhancing medical research and clinical practice, paving the way for more efficient and targeted patient care.
Exciting challenges for exploring generative AI include developing improved image, audio, and text synthesis, creating realistic virtual environments, enhancing AI-generated artwork and design, exploring AI-driven storytelling and gaming, and advancing the field of AI-generated music. These challenges push the boundaries of generative AI and inspire new applications in various industries
The ChatGPT Assistant feature in Utopia Messenger offers users an advanced conversational AI experience for improved communication and productivity. By integrating ChatGPT, users can benefit from real-time language translation, enhanced text analysis, context-aware responses, and personalized recommendations, making the messenger platform more versatile and user-friendly.
Nature – Machine-generated data sets have the potential to improve privacy and representation in artificial intelligence, if researchers can find the right balance between accuracy and fakery.
The new hot trend in platform advertising is to put the machines in charge of everything. It’s true of Google, true of Facebook and, even more so as of today,…
Back in 2016, four years before a pandemic saw the world grind to a halt, the United Nations Environment Programme (UNEP) was sounding the alarm on zoonotic diseases, identifying them as a key emerging issue of global concern.
According to recent Google Trends data, ChatGPT, the AI language model developed by OpenAI, has seen a significant rise in popularity. It has outpaced competitors like Bing and Bard, being 8.3 times more popular than Bing and a staggering 33 times more popular than Bard. ChatGPT’s growth shows no signs of slowing down, as more users adopt it for various applications, including content generation, customer support, and even entertainment.
Artificial intelligence has the potential to learn and discover things beyond human understanding or that have not yet been discovered. Machine learning algorithms, in particular, can analyze large datasets, identify patterns, and make predictions that may not be apparent to human researchers. This capability can lead to new insights and discoveries in various fields, including medicine, physics, and climate science. However, AI’s success in making such discoveries relies heavily on the quality and quantity of data it is trained on, as well as the ability of researchers to interpret its findings.
Nature – Machine-generated data sets have the potential to improve privacy and representation in artificial intelligence, if researchers can find the right balance between accuracy and fakery.
Amazon Is The Latest Major Ad Platform Going All-In On Machine Learning Tech
The new hot trend in platform advertising is to put the machines in charge of everything. It’s true of Google, true of Facebook and, even more so as of today,…
Discover the 7 most popular tools and frameworks for developing AI applications, from TensorFlow and PyTorch to Keras and Caffe.
TensorFlow is an open-source platform developed by Google, which provides a comprehensive framework for building and deploying machine learning models across multiple platforms.
PyTorch is another popular open-source machine learning framework, widely used for developing AI applications such as image recognition, natural language processing and reinforcement learning.
Keras is an open-source neural network library that runs on top of TensorFlow or Theano. It is a user-friendly platform that allows developers to create and train deep learning models with just a few lines of code.
Caffe is a deep learning framework developed by Berkeley AI Research (BAIR) and community contributors. It is designed for fast training of convolutional neural networks and is commonly used for image and speech recognition.
CNTK is an open-source framework developed by Microsoft that provides a scalable and efficient platform for building deep learning models.
Theano is a popular Python library for numerical computation, specifically designed for building and optimizing deep neural networks.
Apache MXNet is a scalable and efficient open-source deep learning framework, which supports multiple programming languages, including Python, R and Scala. It is widely used for computer vision, NLP and speech recognition applications.
I could be the secret weapon in preventing the next global pandemic
Back in 2016, four years before a pandemic saw the world grind to a halt, the United Nations Environment Programme (UNEP) was sounding the alarm on zoonotic diseases, identifying them as a key emerging issue of global concern.
Attention AI Unraveled podcast listeners! Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence,” now available on Amazon! This engaging read answers your burning questions and provides valuable insights into the captivating world of AI. Don’t miss this opportunity to elevate your knowledge and stay ahead of the curve. Get your copy on Amazon today at https://amzn.to/40HXDEl
Popular culture plays an important role in shaping society’s perceptions and attitudes around gender roles. We are fed images and stories through television, film, music and social media that can both …
In a post-AI world, where an algorithm can draft marketing copy—or even pop songs and movie scripts—anything seems possible. Five Harvard Business School faculty members discuss how artificial
Artificial intelligence (AI) and machine learning (ML) are increasingly being used in the field of astrobiology to help in the search for life in space.
Discover how AI and ML can potentially change the software development industry, and how AI affects software development and minimizes developers’ workload Software development is a long, com…
Building on its commitment to make the UK a globally competitive player in artificial intelligence, the government has launched task force to support the safe and reliable development of AI.
Scientific Reports – Ensemble machine learning algorithm for predicting acute kidney injury in patients admitted to the neurointensive care unit following brain surgery
Research indicates that genetic predisposition supersedes age as a risk factor in adults above 65. According to a recent study, once individuals reach the age of 65, which is the threshold for the onset of Alzheimer’s disease, their genetic risk may play a larger role in determining if they will de …
A research team has confirmed evidence of a previously unknown planet outside of our solar system, and they used machine learning tools to detect it. A recent study by the team showed that machine learning can correctly determine if an exoplanet is present by looking in protoplanetary disks, the gas…
What are some practical applications of machine learning that can be used by a regular person on their phone? With advancements in machine learning and artificial intelligence (AI), it’s no surprise that these technologies have made their way onto our smartphones. For the regular person, machine learning has
Say the words “dream vacation” and everyone will picture something different. This brings a particular challenge to the modern travel marketer – especially in a world of personalization, when all travelers are looking for their own unique experiences. Fortunately, artificial intelligence (AI) provides a solution that allows travel marketers to draw upon a variety of sources when researching the best ways to connect with potential audiences.
By utilizing and combining data from user-generated content, transaction history and other online communications, AI and machine-learning (ML) solutions can help to give marketers a customer-centric approach, while successfully accounting for the vast diversity amongst their consumer base.
ETFs using machine learning and natural language processing to pick stocks do not consistently outperform indices . . . yet
AbridgIt – a browser extension that uses GPT to summarize any article you find on the web with a single click
AbridgIt is a browser extension that utilizes GPT technology to provide users with concise summaries of online articles in just a single click. This innovative tool can help users save time and digest information more efficiently. Read more…
AI MAY PUSH ARTISTS BACK TO PHYSICAL MEDIUM
As AI-generated art becomes increasingly prevalent, many traditional artists are returning to physical mediums. This shift reflects a desire to differentiate their work from AI-generated art and preserve the human touch in their creations. Read more…
Humane showcases its wearable AI assistant that could oust smartphones
Humane, a tech company, has revealed its wearable AI assistant, which could potentially replace smartphones in the future. The device is designed to streamline user experiences by incorporating advanced AI technology and a sleek, user-friendly interface. Read more…
Wouldn’t superintelligent sentient AI hide itself?
Some experts argue that if a superintelligent sentient AI were to exist, it might choose to remain hidden from humans. This perspective is based on the idea that a truly intelligent AI would recognize potential threats from humans and prioritize self-preservation. Read more…
Why would AI choose to kill everyone? This has never made sense to me…
The fear that AI would choose to eliminate humanity is often based on misconceptions and misunderstandings about the nature of artificial intelligence. Many experts emphasize the importance of responsible AI development and implementation to avoid potential harm. Read more…
👻 Snapchat Onboards 363 Million to AI
Snapchat has successfully integrated AI technology into its platform, providing new experiences and features for its 363 million users. By utilizing AI, Snapchat enhances user engagement with innovative filters, AR experiences, and improved content discovery. Read more…
How much of the noise around generative AI is true?
While generative AI has made significant advancements in recent years, it’s essential to approach the technology with a balanced perspective. Some hype surrounds its capabilities, but generative AI has also produced impressive results in various fields, such as art, music, and text generation. Read more…
Updated TextGen Ai WebUI Install! Run LLM Models in MINUTES!
TextGen Ai WebUI is an updated user interface that allows users to run large language models like GPT-4 in just a few minutes. This accessible platform enables users to take advantage of AI-powered text generation for various applications, such as content creation and natural language processing. Read more…
Opinion polling on predicted job loss from AI advances.
The Ariel Data Challenge 2023 launched on 14 April and invites AI and machine learning experts to help astronomers in understanding planets outside our solar system.
Mastering the art of deep learning: the top 10 indispensable requirements for novice practitioners, explained in our comprehensive guide to boost your skills and knowledge.
Google Brain and DeepMind merge under Alphabet’s umbrella
Two of the most prominent AI research organizations, Google Brain and DeepMind, have merged under Alphabet’s umbrella. This strategic move aims to consolidate resources, foster collaboration, and accelerate advancements in artificial intelligence. The joint team will continue to work on cutting-edge projects in AI, machine learning, and deep learning, pushing the boundaries of what’s possible in the field. Read more.
Bard now helps you code
Bard, an advanced AI language model, has recently been updated to assist developers in writing code. With natural language processing capabilities, Bard can understand and generate code snippets based on user input, making it an invaluable tool for software developers. This new feature streamlines the coding process, potentially reducing development time and improving code quality. Read more.
Google DeepMind: Bringing together two world-class AI teams
The merger of Google Brain and DeepMind represents a landmark moment in the field of artificial intelligence. By bringing together these two world-class teams, Alphabet aims to accelerate advancements in AI and machine learning by pooling resources and expertise. This collaboration is expected to result in groundbreaking research and innovations, shaping the future of technology. Read more.
Ask a Techspert: What is generative AI?
Generative AI is a subfield of artificial intelligence that focuses on creating new content, such as images, text, music, or even code. It employs machine learning algorithms, like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), to learn patterns in data and generate novel outputs. This technology has numerous applications, ranging from creating realistic images to generating human-like text for chatbots. Read more.
A Gentle Intro to Chaining LLMs, Agents, and utils via LangChain
This article provides an easy-to-understand introduction to LangChain, a framework designed for chaining Large Language Models (LLMs), agents, and utility functions. LangChain simplifies the process of combining these components, enabling developers to create complex AI applications more efficiently. The article covers the basics of LangChain and offers examples of its practical applications. Read more.
Survival Analysis: Leveraging Deep Learning for Time-to-Event Forecasting
Survival analysis is a statistical technique for predicting the time until a specific event occurs. This article delves into how deep learning can be applied to survival analysis, enhancing time-to-event forecasting capabilities. It covers the fundamental concepts, methodologies, and real-world applications of this powerful combination, such as predicting customer churn or equipment failure. Read more.
Train ImageNet without Hyperparameters with Automatic Gradient Descent
Automatic gradient descent is a novel technique that simplifies the training of neural networks on large datasets like ImageNet. By eliminating the need for hyperparameter tuning, this method can save researchers and practitioners significant time and effort. The article explores the underlying concepts, implementation, and potential benefits of adopting automatic gradient descent in machine learning projects. Read more.
Architecture of AI-Driven Security Operations with a Low False Positive Rate
AI-driven security operations are becoming increasingly important in the fight against cyber threats. This article presents an architecture designed to minimize false positive rates while maximizing detection accuracy. The proposed system combines advanced AI algorithms, data fusion techniques, and domain expertise to provide a robust and effective security solution. Read more.
Which GPT-like Engineering Strategies Work on System Logs?
GPT-like models have demonstrated remarkable capabilities in natural language processing tasks. This article examines the application of these engineering strategies to system logs, a critical source of information for IT administrators. It evaluates different approaches and provides insights into their effectiveness in processing and analyzing log data, thereby improving system monitoring and maintenance. Read more.
Top 10 Pre-Trained Models for Image Embedding every Data Scientist Should Know
Pre-trained models for image embedding can significantly improve the efficiency and accuracy of computer vision tasks. This article presents the top 10 pre-trained models that every data scientist should be familiar with, including popular choices like ResNet, Inception, and VGG. It provides an overview of each model’s strengths, weaknesses, and ideal use cases, helping data scientists choose the best model for their specific needs. Read more.
New research describes a machine-learning technique that could provide insight into the type of patients that would benefit the most from hypertension treatment.
Stroke can be tricky to diagnose as patients don’t always present with classic symptoms, and other conditions can mimic it. Researchers have used existing data to develop a machine-learning model that accurately predicts stroke and may make diagnosis easier.
Instead of just striving to eliminate biases in datasets, researchers should ask why biases are in datasets in the first place and what power and poli…
Among all greenhouse gases, carbon dioxide is the highest contributor to global warming. If we do not take action by 2100, according to the Intergovernmental Panel on Climate Change, the average…
GPT-4 is the latest Large Language Model that OpenAI has released. Its multimodal nature sets it apart from all the previously introduced LLMs. GPT’s transformer architecture is the technology behind the
A team of medical researchers, engineers and computer scientists affiliated with multiple institutions across the U.S. has found that machine learning technology can help doctors predict which patients …
With nearly 170,000 tech employees laid off in the previous three months, I thought this could be an interesting real-world workflow powered by AI to try for this week’s challenge.
First, use ChatGPT to learn what Hiring Managers and Recruiters are searching for in your role, and get a nice intro… Use this base prompt:
“Please act as an expert HR headhunter to help me with updating my CV. I’m [Main Role], and I’ve also been [Secondary Role], and my main expertise is [Expertise 1], [Expertise 2], and [Expertise 3]. Could you please provide me with a list of the skills that are most in demand for these roles, as well as what Hiring Managers and HR recruiters are looking for? Write these as an introduction to my CV.”
You can also use ChatGPT to find ways to describe your positions with “Now, for my CV, use the first list to describe my role as [Main Role], highlighting potential achievements that will capture recruiters’ attention“
Then, use MidJourney to make a trendy CV template that will stand out amid the crowd, here’s the base prompt:
“Resume professional Curriculum Vitae for a [Main Role] and [Secondary Role], with [Soft Skills], specialized in [Expertise 1] and [Expertise 2] –ar 2:3 –q 2 –v 5 –s 250“
See the results below… Of course, this is only a part of the content and design, then you have to make it real with your preferred design tool… for that I suggest you to check the following resources:
PowerPoint Ninjas course. How to create a CV (Spanish, but explained) – https://lnkd.in/dKbWYauF
Asia Times: Do Japanese manufacturers use ChatGPT? ChatGPT: It is possible that some Japanese manufacturers use ChatGPT or other similar language models for various applications…
In today’s business world, “data is king.” From customer insights to market trends, businesses must rely on data to make informed decisions and stay ahead of the competition. As the “mentor of the giants,” as Fortune magazine has dubbed me, I always emphasize to our clients how important it is to analyze…
Picking, sorting, and packaging are just some of the many warehouse operations that may be automated using robotic object-handling systems. It is not easy to construct robust and scalable robotic systems for use in handling objects in warehouses. Warehouses now handle millions of items that va…
Amazon has launched a new AI platform for businesses called Amazon Bedrock, aimed at providing customers of Amazon Web Service (AWS) with a suite of generative AI tools that can build chatbots, generate and summarize text, and classify images based on a
We recently expanded access to Bard, an early experiment that lets you collaborate with generative AI. Bard is powered by a large language model, which is a type of machine learning model that has become known for its ability to generate natural-sounding language. That’s why you often hear it described interchangeably as “generative AI.” As with any new technology, it’s normal for people to have lots of questions — like what exactly generative AI even is. Read more at https://blog.google/inside-google/googlers/ask-a-techspert/what-is-generative-ai/
The top 10 Deep learning algorithms to know in 2023: driving progress in the field of Artificial Intelligence. These algorithms are up-to-date mastering the future of AI.
It’s been five months since Microsoft-backed OpenAI released its generative large language model ChatGPT, followed by GPT-4 in March. | Tech giants and startups are off to the races to test out the potential for LLMs and generative AI tools in medicine and healthcare use
The Pentagon is hiring data scientists, technologists and engineers as part of its effort to incorporate artificial intelligence into the machinery used to wage war.
Large language models (LLMs) that can comprehend and produce language similar to that of humans have been made possible by recent developments in natural language processing.
A shared agenda for responsible AI progress
Generative AI experiments like Bard, tools like the PaLM and MakerSuite APIs and a growing number of services from across the AI ecosystem have sparked excitement about AI’s transformative potential — and concern about potential misuse.
It’s helpful to recall that AI is simultaneously revolutionary and something that’s been quietly helping us out for years.
LinkedIn’s vision is to create economic opportunity for every member of the global workforce. By blending human innovation with advanced technology, we’re dedicated to making this goal a reality. Over the past 15 years, our AI technology has played a vital role in connecting professionals worldwide with economic opportunities, such as promoting knowledge sharing, helping managers find the most relevant candidates, and assisting small businesses in reaching their target customers. Learn more ….
In the face of technological change, creativity is often held up as a uniquely human quality, less vulnerable to the forces of technological disruption and critical for the future. Today however, generative AI applications such as ChatGPT and Midjourney are threatening to upend this special status and…
The image of the supermassive black hole at the heart of the galaxy Messier 87 was boosted to high fidelity by a machine learning program trained on black hole models.
Nature – This review discusses generalist medical artificial intelligence, identifying potential applications and setting out specific technical capabilities and training datasets necessary to…
Photographs play a crucial role in our lives by preserving priceless memories and significant experiences we can cherish forever. They serve as a way to keep us connected to our past, families, and communities. They are a physical representation of our history. Yet, pictures can deteriorate, age, or sustain various ….
The AI industry is evolving and coming up with new and unique research and models daily. Whether we talk about healthcare, education, retail, marketing, or business, Artificial Intelligence and Machine Learning practices are beginning to shift how industries operate. Every organization is adopting AI to include
Type 2 diabetes is a chronic disease that affects millions of people around the world, leading to long-term health complications such as heart disease,
Latest AI Trends in April 2023: Top AI trends as of April 12th 2023
Stanford students have created RizzGPT, a tool that combines AI and augmented reality to aid individuals during challenging conversations.
The tool uses GPT-4 and Whisper to generate responses to questions asked during a conversation, which can be displayed through AR glasses with a monocle.
This technology can have applications for people with social anxiety, public speaking, job interviews, and more.
In recent years, artificial intelligence and machine learning have had a substantial impact on several businesses. These technologies have completely changed the way organizations run because they offer
Latest AI Trends in April 2023: Top AI trends as of April 11th 2023
Twitter recently open-sourced several components of their system for recommending tweets for a user’s Twitter timeline. The release includes the code for several of the services and jobs that run the algorithm, as well as code for training machine learning models for embedding and ranking tweets.
The outstanding generalization skills of Large Language Models (LLMs), such as in-context learning and chain-of-though ts reasoning, have been demonstrated. Researchers have been looking towards techniques for instruction-tuning LLMs to help them follow instructions in plain language and finish jobs in the…
The emergence of publicly accessible chatbots capable of engaging in humanlike conversations has brought AI into the public spotlight, with reactions ranging from amazement to apprehension due to concerns over biases and harmful behaviors. To address these issues, a Columbia University and…
Instead of having a website with 10 FAQs, companies will have a chatbot to ask any question.
Instead of having to go through 10s of documents to find some info, you’ll just ask the chatbot.
Imagine being able to ask “What are the sales of X product in the last 6 months” and getting the answer in seconds.
How long it takes to get this information now?
The way it works is called “retrieval augmented generation”.
Using Azure Cognitive Search, you can search through millions of documents or data points.
It retrieves a list of the top-ranked results, kind of like a search engine.
Azure OpenAI service uses its language modeling capabilities to understand the question and the context provided by the search engine, to generate an answer in natural language.
Something very important.
All this information remains private and external to the ChatGPT language model. The data is never used to train the model because all chat sessions live entirely within the company application.
This architecture is, in my opinion, one of the most powerful AI use cases for companies.
AI is revolutionizing space exploration, from autonomous spaceflight to planetary exploration and charting the cosmos. ML algorithms help astronauts and scientists navigate and study space, avoid hazards, and classify features of celestial bodies.
Toran Bruce Richards, founder of Significant Gravitas, along with a group of developers, explores what could be accomplished by combining LLMs with other high-powered information sources and tools. These systems can be built easily using today’s LLMs, prompting approaches, knowledge centers, and open-source
Access to basic healthcare is seen as a fundamental global right. However, in both developed and developing economies, we find the surge in chronic lifestyle ailments and rising populations overburdens
The world of artificial intelligence (AI) is continually evolving, with new advancements, applications, and ethical considerations emerging daily. A shared agenda for responsible AI progress has recently been proposed, highlighting the importance of collaboration and accountability in this rapidly developing field. Stakeholders from various sectors are joining forces to ensure that AI systems are developed and deployed ethically, transparently, and for the benefit of all.
A new AI-powered tool called Bard is making waves and inviting users to share their feedback.
This innovative platform aims to streamline and enhance the creative process for writers and content creators, with its developers eager to learn from user experiences to improve the system further.
Google AI is also making strides in helping users sleep better.
With six new AI-driven features, the tech giant aims to assist individuals in understanding and improving their sleep patterns, providing data-driven insights and recommendations to promote better rest and overall health.
In the realm of healthcare, researchers are actively exploring AI’s potential to transform diagnostics, treatment, and patient care.
The latest health AI research updates reveal promising developments in areas such as medical imaging, disease prediction, and drug discovery, paving the way for a more efficient and personalized healthcare system.
Google is pushing the boundaries of AI integration for developers and Google Workspace users.
The next generation of AI solutions promises to streamline workflows, enhance collaboration, and optimize productivity across a variety of industries and applications.
Meanwhile, four Black Founders Fund recipients are leveraging AI technology to build innovative solutions and drive positive change. Their groundbreaking work spans diverse fields, from education and healthcare to fashion and climate change, showcasing the transformative potential of AI in addressing pressing global challenges.
In the realm of quantum computing, researchers are making progress toward quantum error correction. This critical advancement will enable the creation of more stable and reliable quantum computers, which have the potential to revolutionize numerous industries by tackling problems currently unsolvable by classical computers.
AI is already making an impact on everyday life through devices such as the Google Pixel. Seven new AI-powered features are making the popular smartphone even more helpful and efficient, enhancing user experience through advanced capabilities like real-time translation, battery optimization, and camera improvements.
Finally, Google Maps is embracing AI to create a more immersive and sustainable user experience. New AI-driven features aim to provide more accurate and eco-friendly route suggestions, along with better real-time traffic updates, ultimately contributing to a more sustainable future for all.
Today’s latest trends in AI showcase the ongoing advancements and potential of this transformative technology. As we collectively strive for a better tomorrow, AI continues to play a crucial role in shaping a more efficient, sustainable, and equitable world.
The Machine Learning For Dummies App is the perfect way to learn about Machine Learning, AI and how to Elevate your Brain. With over 400+ Machine Learning Operations, Basic and Advanced ML questions and answers, the latest ML news, and a daily Quiz, the App is perfect for anyone who wants to learn more about this exciting field.
With operations on AWS, Azure, and GCP, the App is perfect for beginners and experts alike. And with its updated daily content, you’ll always be up-to-date on the latest in Machine Learning. So whether you’re a beginner or an expert, the Machine Learning For Dummies App is the perfect way to learn more about this fascinating field. Use this App to learn about Machine Learning and Elevate your Brain with Machine Learning Quiz, Cheat Sheets, Questions and Answers updated daily.
– 400+ Machine Learning Operation on AWS, Azure, GCP and Detailed Answers and References
– 100+ Machine Learning Basics Questions and Answers
– 100+ Machine Learning Advanced Questions and Answers – Scorecard
– Countdown timer – Machine Learning Cheat Sheets
– Machine Learning Interview Questions and Answers
– Machine Learning Latest News and Tweets
The App covers: Azure AI Fundamentals AI-900 Exam Prep: Azure AI 900, ML, Natural Language Processing, Modeling, Data Engineering, Computer Vision, Exploratory Data Analysis, ML implementation and Operations, S3, SageMaker, Kinesis, Lake Formation, Athena, Kibana, Redshift, Textract, EMR, Glue, GCP PROFESSIONAL Machine Learning Engineer, Framing ML problems, Architecting ML solutions, Designing data preparation and processing systems, Developing ML models, Monitoring, optimizing, and maintaining ML solutions, Automating and orchestrating ML pipelines, Quiz and Brain Teaser for AWS Machine Learning MLS-C01, Cloud Build, Kubeflow, TensorFlow, CSV, JSON, IMG, parquet or databases, Hadoop/Spark, Vertex AI Prediction, Describe Artificial Intelligence workloads and considerations, Describe fundamental principles of machine learning on Azure, Describe features of computer vision workloads on Azure, Describe features of Natural Language Processing (NLP) workloads on Azure , Describe features of conversational AI workloads on Azure, QnA Maker service, Language Understanding service (LUIS), Speech service, Translator Text service, Form Recognizer service, Face service, Custom Vision service, Computer Vision service, facial detection, facial recognition, and facial analysis solutions, optical character recognition solutions, object detection solutions, image classification solutions, azure Machine Learning designer, automated ML UI, conversational AI workloads, anomaly detection workloads, forecasting workloads identify features of anomaly detection work, NLP, Kafka, SQl, NoSQL, Python, DocumentDB, linear regression, logistic regression, Sampling, dataset, statistical interaction, selection bias, non-Gaussian distribution, bias-variance trade-off, Normal Distribution, correlation and covariance, Point Estimates and Confidence Interval, A/B Testing, p-value, statistical power of sensitivity, over-fitting and under-fitting, regularization, Law of Large Numbers, Confounding Variables, Survivorship Bias, univariate, bivariate and multivariate, Resampling, ROC curve, TF/IDF vectorization, Cluster Sampling, etc.
Important: To succeed with the real exam, do not memorize the answers in this app. It is very important that you understand why a question is right or wrong and the concepts behind it by carefully reading the reference documents in the answers.
Note and disclaimer: We are not affiliated with Microsoft or Azure or Google or Amazon. The questions are put together based on the certification study guide and materials available online. The questions in this app should help you pass the exam but it is not guaranteed. We are not responsible for any exam you did not pass.
Download the Machine Learning For Dummies App below:
Azure AI Fundamentals AI-900 Exam Preparation: Azure AI 900 is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure. This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.
Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.
This Azure AI Fundamentals AI-900 Exam Preparation App provides Basics and Advanced Machine Learning Quizzes and Practice Exams on Azure, Azure Machine Learning Job Interviews Questions and Answers, Machine Learning Cheat Sheets.
I trying to understand ACUs to standarize the compute unit across app service plan. , just correct if I'm wrong here: I understood ACU as just as compute power , so let us consider my ASP S1 is 100 ACU 1 core ... And if it hits 50% cpu percentage in a 5 min time period, does it mean that the asp consumes 50 ACUs out of 100 So similarly if I have an S2 plan which comes with 100 ACU two cores , so that means I have 200 ACUs So the same 50% in S1 will it be now 25% in S2 because now i know that it consumes 50 ACUs.. thus for a machine with 200 ACUs it's gonna come to 25% My question are basically: - can we multiply core and ACU to get the total ACU (compute power) -and me converting the cpu percentage to ACUs will it help me choosing the right ASP ? submitted by /u/kevin_noel [link] [comments]
👋🏻 Hi there! If I end up securing a Microsoft Azure Sponsorship subscription could I 'buy' a second Parallel Job (for Azure DevOps Pipelines) and it's charged to the subscription? Or is this totally separate? Or could there be a 'special consideration'? (Trying to see if we should go with Azure DevOps vs GitHub Actions. Please don't turn this into a debate about the pro's/con's of either one - this topic is just about pricing/billing). Thank you kindly. submitted by /u/PureKrome [link] [comments]
That was a HARD exam! The WAF, AppGW, and FW questions are what tripped me up. Got a 711 on it so I'm happy. I also have my AZ-500 and AZ-900. What's the 104 like? Lot of PS/CLI or a variety of things? submitted by /u/icebreaker374 [link] [comments]
I am finding those terms "Resource manager" and "Classic" as deployment models that I can use. As I understand for "Resource manager deployment model" I should use "Resource manager". What about "Classic deployment model"? Is this though "+" button from Azure main page or it is some old way that is not available any more or some other way? submitted by /u/Alex_df_300 [link] [comments]
For Azure and/or AWS which should I learn in order of priority? Like what is the most relied on language for scripting on the Cloud? submitted by /u/bsoliman2005 [link] [comments]
I hope I am allowed to ask this question here. Our company (approx. 250 employees) works entirely in Azure. The users request servers from the IT department, for example, to test their products/projects. The IT department then creates the desired resources with corresponding network resources, authorizations, etc. Since our work is very agile, we would like to establish a system that allows users to compile and create VMs (or other services such as SQL databases, AppServices, storage accounts, etc.) themselves. I think I have already found a corresponding software in the Marketplace to implement this quite easily with the help of a simple WebGUI. We don't want to give users additional rights in the portal itself, but would like to make the whole thing simple. It would be important that things like network, authorizations etc. are configured appropriately (according to a one-time specification) when the application is made. Unfortunately, I didn't remember the name of the service/offer at the time. Does anyone know of a corresponding Marketplace offer? We would hate to create everything ourselves by hand in Powershell scripts or similar if there was already something ready-made. submitted by /u/ThreeC82 [link] [comments]
.Net Standard user-defined functions for Azure Stream Analytics will be retired on 30 September 2024. After that date, it won’t be possible to use the feature. Please transition to JavaScript user-defined functions for Azure Stream Analytics.
I have a B2s dev VM (also tried a Standard DS1 v2) running in the South Central US region that shuts off nightly, and just a few days ago we started getting the "Allocation failed. We do not have sufficient capacity..." error due to lack of resources in the region. I've retried allocation at least 8 times over the past 2 days to no avail. What options do I have here? Since I can't allocate it I believe my only options are - Completely re-create the server in a new region Use the resource mover tool (which I'm skeptical that this will be an easy or successful process) Change the server size to something else and hope there's availability for that server size? We have this server configured and working just how we need it. So I'm understandably a bit bothered that this is an issue in our region. Lastly, how can I avoid this issue moving forward? Are there known regions that are much larger and therefore less likely to have these issues? submitted by /u/Boring-Count9382 [link] [comments]
Noob here assigned with this task at work. A client was a victim of ransomware on the server that was hosting ADFS (server inaccesible now). We would like to disconnect the federation in Entra Connect sync and move all auth to Entra ID. I've been beating my head against my keyboard fighting with Powershell and the various modules that were just recently deprecated (all scripts I've found online are using deprecated modules). I've fixed all permissions with the MgGraph module but I'm once again stuck. I've been following this guide https://community.spiceworks.com/t/disable-adfs-server-no-longer-in-environment/966478/2 but am now stuck getting an error on the last step while running $authSettings.Authentication = "Managed". I'd appreciate ANY help with this, I've been struggling for almost two days!! submitted by /u/dcrogers25 [link] [comments]
After a horrible testing experience, I passed DP-203 today! I chose to take the exam at a Person testing center. I have never had a problem taking my exams this way but today was a complete mess. There were many people taking exams, so there were people constantly entering and leaving the room. Then there was someone two computers away who needed to listen to a video for their exam. The problem was that their headset was not working. The proctor decided to unplug the head set and let the audio play through the speakers on the monitor. So, as I’m trying to focus on my questions, I am trying to block out that playing in the background. When I was about halfway through my test, the testing center started to have internet connection issues. Everyone’s exams were completely freezing and we were just looking at each other as the proctors ran around in a panic. Eventually the exams re-connected and continued, but there seemed to be lag throughout the rest of the exam. Towards the end of my exam, I felt like I was not performing very well with all of the problems. I thought I failed. When I clicked the finish exam button and the “Congratulations!” screen appeared I could not believe my eyes. I’m so glad that one is over. submitted by /u/Cleveland_Steve [link] [comments]
Trying my luck again. I am looking for an audio device to show up on Control Panel => Sound (like how we see it on a physical machine) I've tried Virtual Audio Cable (and other similar virtual software's). These are registering Audio devices in Device Manager, but I am not seeing them in Control Panel / Sound. I've tried both VMs and AVD, but no luck. Is it possible to achieve this? Any ideas anyone? Thank you submitted by /u/karuninchana-aakasam [link] [comments]
I have long term retention set on a 3TB azure sql database, it needs to be stored once a month going back 10 years. Now its too expensive. Can i take these long term retention points into archive tier or something else? The cost is insane, its like $70,000 / yr to back up 3tb. submitted by /u/heapsp [link] [comments]
I was just told about this from a coworker. You can request up to an extra 100% of the time allowed on the exam if you have certain diagnosis. I have adhd and all the paperwor that shows my diagnosis. I was wondering if anyone has ever applied for the exemption and what process was like. I applied and my app is at tier4, started at tier 1 on monday. Any insight is appreciated. Thanks yall. submitted by /u/Personal-Ad9152 [link] [comments]
So I have my CDN endpoint xxxx.azureedge.net that needs to be added as a DNS record to my GoDaddy domain as a CNAME type named WWW with data of xxxx.azureedge.net but I can't add it as there is already a CNAME named WWW. I don't experience this issue if it was a static website that wasn't based off of blob storage as I could just enter an A record. Would it be better to just simply forward the GoDaddy domain to the endpoint or to create a DNS zone in Azure and just update the name servers in GoDaddy or am I missing something obvious and haven't dug far enough? submitted by /u/d-weezy2284 [link] [comments]
Hi. I inherited an azure sql database. It's not currently being backed up. As of last friday we actually have users and so I can't just go in with a chainsaw and mess up tables and views any more. I'm thinking we need a testing database and a backup system. Preferably with the ability to synchronize tables between testing and live and the ability to move all the changes from the testing database to the live database. The testing will be mainly to support further development of features. The backup would be in case of disaster so we don't have to spend weeks/months repopulating the data. I've been googling it this morning and I'm getting conflicting advice. One site said to make a backpac and store in it in a blob on azure as a backup and then restore it to migrate between testing and live. Another site said to use azure's built in backup system. Any advice? Can you give me an idea of how to find out how expensive all this is? Our database is currently 1gb of data but soon it will stabilize around 50-75gb with slow growth after that. submitted by /u/jjhhw [link] [comments]
Read Aloud For Me – Multilingual – Speech Synthesizer – Read and Translate for me without tracking me – AI Dashboard
Unlock the power of AI with “Read Aloud For Me” – your ultimate AI Dashboard and Hub. Access all major AI tools in one seamless app, designed to elevate your productivity and streamline your digital experience. Available now on the web at readaloudforme.com and across all your favorite app stores: Apple, Google, and Microsoft. “Read Aloud For Me” brings the future of AI directly to your fingertips, merging convenience with innovation. Whether for work, education, or personal enhancement, our app is your gateway to the most advanced AI technologies. Download today and transform the way you interact with AI tools.
If you’re looking for a safe and secure way to have text read aloud to you in your chosen language, look no further than the Read Aloud For Me app. This app uses cutting-edge speech synthesis technology to translate text into speech, without tracking you or collecting your data. You can also use the app to translate text into your chosen language, making it a great tool for international communication. The Read Aloud For Me app is perfect for students, professionals, or anyone who wants to make their life a little easier. Download it today and start enjoying the benefits of hands-free text-to-speech translation.
Read Aloud For Me is an Application of Machine Learning, Natural Language Processing, Computer Vision to help everyone read text, pdf photos, documents, translate and synthesize speech in their preferred language securely and without being tracked.
Description: Read Aloud For Me in my chosen language
Read Aloud for Me is the perfect app for anyone who wants to hear their text, documents, or images read aloud without any ads or data tracking. This app uses secure speech synthesis to read your chosen language aloud, allowing you to easily follow along and understand what you’re reading. You can also use the built-in translator to translate text into your chosen language without worrying about your data being collected. Whether you’re trying to learn a new language or just want an easy way to read text aloud, Read Aloud for Me is the perfect solution! Read, Translate Text, Images, Photos, Documents to Speech in your chosen language leveraging Machine Learning, Natural Language Processing, Computer Vision – Synthesize Speech – Read Text for you in your preferred language, – read text for you in your preferred language, – Translate Text for you in your chosen language
This application helps the visually impaired to read text, documents, photos in their language of choice without being tracked and without their content being tracked.
Detailed Description:Read Aloud For Me Multilingual App including: – Speech Synthesizer, – Can Read Text From Photos/Images, – Can Translate Text and Documents in your chosen language
Read, Translate Text, Images, Photos, Documents to Speech in your chosen language leveraging Machine Learning, Natural Language Processing, Computer Vision
This App can: – Read Text for you in your preferred language, – read text for you in your preferred language, – Read Text from Photo or Images for you in your chosen language – Translate Text and Documents for you in your chosen language
This is an application which helps the visually impaired hear text. With the help of AI services such as Google AutoML, Amazon Textract, Amazon Comprehend, Amazon Translate and Amazon Polly.
Users enter text or upload a picture of a document, or anything with text, and within a few seconds hear that document in their chosen language.
Can read text, photos and documents in the following languages:
Afrikaans, Afrikaans, af
Albanian, Shqip, sq
Arabic,عربي, ar
Armenian, Հայերէն, hy
Azerbaijani, آذربایجان دیلی, az
Basque, Euskara, eu
Belarusian, Беларуская, be
Bulgarian, Български, bg
Catalan, Català, ca
Chinese (Simplified), 中文简体, zh-CN
Chinese (Traditional), 中文繁體, zh-TW
Croatian, Hrvatski, hr
Czech, Čeština, cs
Danish, Dansk, da
Dutch, Nederlands, nl
English, English, en
Estonian, Eesti keel, et
Filipino, Filipino, tl
Finnish, Suomi, fi
French, Français, fr
Galician, Galego, gl
Georgian, ქართული, ka
German, Deutsch, de
Greek, Ελληνικά, el
Haitian Creole, Kreyòl ayisyen, ht
Hebrew, עברית, iw
Hindi, हिन्दी, hi
Hungarian, Magyar, hu
Icelandic, Íslenska, is
Indonesian, Bahasa Indonesia,id
Irish, Gaeilge, ga
Italian, Italiano, it
Japanese, 日本語 , ja
Korean, 한국어, ko
Latvian, Latviešu, lv
Lithuanian, Lietuvių kalba, lt
Macedonian, Македонски, mk
Malay, Malay, ms
Maltese, Malti, mt
Norwegian, Norsk, no
Persian, فارسی, fa
Polish, Polski, pl
Portuguese, Português, pt
Romanian, Română, ro
Russian, Русский, ru
Serbian, Српски, sr
Slovak, Slovenčina, sk
Slovenian, Slovensko, sl
Spanish, Español, es
Swahili, Kiswahili, sw
Swedish, Svenska, sv
Thai, ไทย, th
Turkish, Türkçe, tr
Ukrainian, Українська, uk
Urdu, اردو, ur
Vietnamese, Tiếng Việt, vi
Welsh, Cymraeg, cy
Yiddish, ייִדיש, yi
Zulu, zu
Security: Open source, your data is not stored, you are not tracked
This Application Read aloud For You without tracking you.
This Application translate for you without tracking you
This application Read and analyze Photos for you without keeping your data
Secure Application of Machine Learning, Natural Language Processing, Computer Vision