1. 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].

2. Generative AI short courses by DeepLearning.AI – Five short courses on generative AI including LangChain for LLM Application Development, How Diffusion Models Work and more. [Link].

3. LLM Bootcamp: A series of free lectures by The full Stack on building and deploying LLM apps [Link].

4. Building AI Products with OpenAI – a free course by CoRise in collaboration with OpenAI [Link].

5. Free Course by Activeloop on LangChain & Vector Databases in Production [Link].

6. Pinecone learning center – Lots of free guides as well as complete handbooks on LangChain, vector embeddings etc. by Pinecone [Link].

7. Build AI Apps with ChatGPT, Dall-E and GPT-4  – a free course on Scrimba [Link].

8. Gartner Experts Answer the Top Generative AI Questions for Your Enterprise – a report by Gartner [Link]

9. GPT best practices: A guide by OpenAI that shares strategies and tactics for getting better results from GPTs [Link].

10. OpenAI cookbook by OpenAI – Examples and guides for using the OpenAI API [Link].

11. Prompt injection explained, with video, slides, and a transcript from a webinar organized by LangChain [Link].

12. A detailed guide to Prompt Engineering by DAIR.AI [Link]

13. What Are Transformer Models and How Do They Work. A tutorial by Cohere AI [Link]

14. Learn Prompting: an open source course on prompt engineering[Link]

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.

https://news.berkeley.edu/2023/04/07/stuart-russell-calls-for-new-approach-for-ai-a-civilization-ending-technology/

Of this $14 quadrillion, it is estimated that the top five AI companies will earn the following wealth:

1. Google: $1.5 quadrillion

2. Amazon: $1.1 quadrillion

3. Apple: $2.5 quadrillion

4. Microsoft: $2.0 quadrillion

5. 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:

1. Google: 17-20%

2. Amazon: 13-15%

3. Microsoft: 18-22%

4. Apple: 20-25%

5. 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:

https://www.forbes.com/sites/jackkelly/2023/03/31/goldman-sachs-predicts-300-million-jobs-will-be-lost-or-degraded-by-artificial-intelligence/?sh=24a8a85e782b

Microsoft reports $20.1B quarterly profit as it promises to lead “the new AI platform shift”

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.

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

How to use Meta’s open-source ChatGPT competitor?

Image source: Meta

Summary: Meta has unveiled Llama 2, an open-source LLM that can be used commercially! Here are the key features and how could you use it

Key Points:

In partnership with Microsoft (Azure), and will soon be on AWS, Hugging Face and more. Qualcomm will work with Meta to put Llama 2 into devices starting in 2024. Pre-trained on 2 trillion tokens and has double the context length of LLama. The models are available with 7B, 13B and 70B parameters. (paper)

How to use it?

Vercel AI SDK Playground: You can have a side-by-side comparison between other models such as GPT and Claude Perplexity AI Chat: Chatbot interface similar to ChatGPT https://ai.meta.com/llama/

OpenAI doubles GPT-4 messages for ChatGPT Plus users

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.

Source: https://the-decoder.com/openai-doubles-gpt-4-messages-for-chatgpt-plus-users

AI Tutorial:

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.

Imitation Models and the Open-Source LLM Revolution

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.

https://cameronrwolfe.substack.com/p/imitation-models-and-the-open-source

Google AI’s SimPer unlocks 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.  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.

3 Machine Learning Stocks for Getting Rich in 2023

InvestorPlace – Stock Market News, Stock Advice & Trading Tips

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.

Top 10 career options in Generative AI

Here is the list of top 10 career option in Generative AI field.

Machine LEarning Engineer

Data Scientist

Computer Vision Engineer

Natural Language Processing Engineer

Robotics Engineer

Deep learning Engineer

Prompt Engineer

Data Engineer

A.I. will do to call center jobs what the tractor did to farm laborer jobs 100 years ago

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.

https://www.nytimes.com/2023/07/19/business/call-center-workers-battle-with-ai.html

New study confirms user rumors: Huge drop in quality of GPT generations from March to June 2023

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.”

• https://notes.aimodels.fyi/new-study-validates-user-rumors-of-degraded-chatgpt-performance/

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.

Source (TheRegister)

76% of Gen Z is concerned about losing their jobs to AI

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.

https://www.businessinsider.com/gen-z-workers-ai-boost-productivity-chatgpt-2023-7

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 hottest new job is “head of AI” and nobody knows what they do

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.

https://www.vox.com/technology/2023/7/19/23799255/head-of-ai-leadership-jobs

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.

Source (Vox)

Cerebras and G42 Unveil Condor Galaxy 1, a 4 exaFLOPS AI Supercomputer for Generative AI

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.

• Press release: 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

• Blog: https://www.cerebras.net/blog/introducing-condor-galaxy-1-a-4-exaflop-supercomputer-for-generative-ai/

Microsoft and OpenAI test synthetic data to train LLMs, as web data is “no longer good enough

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 a AI – Just type “Help” and instant access GPT-4 on any site + Complete Source Code

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.

My Product link – https://www.helperai.info/

I hope you like it because i’m not good in marketing and writing, if you good marketer please DM. i love to work with you.

Tech Giants Betting Big on AI’s Potential in Medicine and Drug Discovery

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/

https://ir.recursion.com/news-releases/news-release-details/recursion-announces-collaboration-and-50-million-investment

Google

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 $50M in 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.

LLaMA 2: How to access and use Meta’s versatile open-source chatbot right now

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 pitching an AI for writing news articles. Media orgs who saw it found it “unsettling.”

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.

Open AI releases “Custom Instructions” for ChatGPT here are 6 use cases

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.

https://openai.com/blog/custom-instructions-for-chatgpt

The main takeaway:

• 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.

1. “Expertise calibration: Sharing your level of expertise in a specific field to avoid unnecessary explanations.

2. Language learning: Seeking ongoing conversation practice with grammar correction.

3. Localization: Establishing an ongoing context as a lawyer governed by their specific country’s laws.

4. Novel writing: Using character sheets to help ChatGPT maintain a consistent understanding of story characters in ongoing interactions.

5. Response format: Instructing ChatGPT to consistently output code updates in a unified format.

6. 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.)

Source: (link)
Use cases: (link)

No-Code AI Applications for Healthcare and Other Traditional Industries – Examples

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

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/

LLaMA 2 is available for download right now here.

Here’s what’s important to know:

• 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.

Source (CNBC)

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

Here’s the paper on arXiv for those who are interested in reading it in-depth. I’ve teased out the main points for Reddit discussion below.

Why this matters:

• 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.

Source: Microsoft: Furthering our AI Ambitions

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.

Read the full article here.

Wix’s new AI tool creates entire websites

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.

MedPerf makes AI better for Healthcare

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. 

Source

LLMs benefiting robotics and beyond

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.

Source

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AI TUTORIAL: Use ChatGPT to learn new subjects

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.

1. AI eating software

2. Speaking of education…

3. 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.

Source

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What Else Is Happening in AI

HEIDI movie trailer, generated by AI! Isn’t this incredible? (Link)

AI Web TV showcases the latest automatic video and music synthesis advancements. (Link)

Infosys takes the AI world by signing a $2B deal! (Link)

AI helps Cops by deciding if you’re driving like a criminal.  (Link)

FedEx Dataworks employs analytics and AI to strengthen supply chains. (Link)

Runway secures $27M to make financial planning more accessible and intelligent. (Link)

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Trending  AI Tools

• Locofy AI: Designs to code 3-4x faster. Try Figma to React, React Native, HTML-CSS, Next.js, Gatsby in FREE BETA!
• SparkBrief: AI essay writer. Set word limit, tones, speakers, and objectives, and get generated essay. Powered by GPT and PaLM.
• AI Email Writer: Snov.io Email AI for better opens, clicks, replies, and bookings. Easy email creation without copywriting skills.
• Finito AI: ChatGPT assists with grammar, Q&A, writing, ideas, and translation. Select text, hit Control, and simplify tasks.
• AutoRFP AI: Automating RFP responses saves time. 🤖 AI Response Engine 🔎 AI Search 📚 Content Management 🎯 AI Reviewer.
• MailBunny AI: Email-based AI assistant summarizes messages and engages in conversations. No apps are needed. Just email!
• Booom AI: Elevate fun with AI trivia games. Generate & style personalized games for parties and team activities!
• QR Blend: World’s first AI QR code generator. Convert any link into an astonishing QR code.

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• 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

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)

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.”

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?

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)
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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.

For a quick demo: https://twitter.com/RayVenturaHQ/status/1678808052047151112
For those who want to dig deeper into the technical bits and understand how it works, the project is available on GitHub:
https://github.com/RayVentura/ShortGPT
Additionally, if you enjoy a hands-on experience, he has also made available a Colab Notebook:
https://colab.research.google.com/drive/1_2UKdpF6lqxCqWaAcZb3rwMVQqtbisdE?usp=sharing
I invite you all to go through the project, utilize it, and share your opinions. I’m genuinely eager to hear your thoughts and perspectives – it.

Ai Company has been fined by EU after failing to comply

UN Fears AI-Powered brain implants can spy on our thoughts

Why actors are on strike: Hollywood studios offered just 1 days’ pay for AI likeness, forever

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.

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.

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

ChatGPT will have a Real Time News with AP

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.

Meta AI released SOTA generative AI model for text and images 

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.

Why is everyone mad about New York’s AI hiring law?

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.

Achieving Enhanced AI Synchronization and Data Transfer through WebSocket Server Connections

Hey, fellow AI enthusiasts!

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.

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.

https://news.mit.edu/2023/learning-language-molecules-predict-properties-0707

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).

Copywriting ai tools

1. Copy.ai . Beginner friendly and good for short form content and social media marketing campaigns.

2. Article Forage. Good for SEO friendly articles.

3. Copymatic. Offers templates and creates content for various categories – social media, e-commerce copy, website, blog content etc.

4. Simplified. Can generate blogs and other copies in 2 minutes. Also offers video and picture editing.

5. Jasper AI. Writes content with a personalised touch.

More here

AIs may soon replace humans in behavioral experiments

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.

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.

Google slapped with a lawsuit for ‘secretly stealing’ data to train Bard

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.

Source (Mashable)

China mandates that AI must follow “core values of socialism”

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.

Source (The Verge)

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.

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.

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?

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.

I am a data scientist for Cleanlab.

Bard is available in new places and languages

• 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.

Source:

1. https://bard.google.com/updates

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.

Source: In-Memory Computing and Analog Chips for AI

And here are some other resources if you want to learn more about analog computing and how they work in depth:

What is In-Memory Computing? (from IBM)

In-Memory Computing (from Semiconductor Engineering)

In-Memory And Near-Memory Compute (from Semiconductor Engineering)

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.

https://en.wikipedia.org/wiki/Computing_Machinery_and_Intelligence

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

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?

China’s open source large language model to rival OpenAI

China’s open source large language model to rival OpenAI

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.

AI sees through Russian attempt to hide ships from Ukraine using ‘deceptive camouflage’

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.

AI applied to SAR (synthetic aperture radar) satellite imagery can help unmask these deceptively camouflaged warships. See https://www.navalnews.com/naval-news/2023/07/new-technology-sees-through-russian-attempt-to-hide-ships-from-ukraine/

ChatGPT: A bullshit tool for bullshit jobs and a cure for the professional illness of our times

ChatGPT is a bullshit generator.

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.

I wrote a deeper dive for The Algorithmic Bridge

Google’s AI-powered notes app is now called NotebookLM, and it’s launching today

Relevant Links:

https://blog.google/technology/ai/notebooklm-google-ai/

https://www.theverge.com/2023/7/12/23792382/google-notebooklm-tailwind-ai-notes

From the Verge:

JUL 12, 2023 at 12:00 PM EDT

• 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.

Man loses 26 pounds with ChatGPT-generated running plan

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. 😀

Source.

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:

1. It will consist of 22,000 H100 NVIDIA GPUs.

2. 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.

1. Anthropic – $1.5 billion

2. Cohere – $445 million

3. Adept – $415 million

4. Runway – $237 million

1. Netflix’s AI revolutionizes filming!

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.

2. Hospitals begin testing Google’s medical AI

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.

3. US Military uses AI to make decisions

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.

4. Wildfire AI detection startup raises $17M

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.

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…

Sources: https://www.01net.com/actualites/leurope-pourrait-lancer-son-propre-chatgpt-dici-a-2028-mais-ce-sera-beaucoup-trop-tard.html

https://www.gamingdeputy.com/europe-could-launch-its-own-chatgpt-by-2028-but-it-will-be-far-too-late/