Transforming Food & Agriculture with Vision AI Agents

Agentic AI in Food & Agriculture

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Transforming Food & Agriculture with Vision AI Agents.🍓

Listen at https://podcasts.apple.com/ca/podcast/agentic-ai-in-action-vision-ai-in-food-and-agriculture/id1684415169?i=1000690158530

🎙️ Podcast Series: Agentic AI in Action

Episode 2:🍏 Transforming Food & Agriculture with Vision AI Agents: Feeding the Future

Vision AI, a subset of Agentic AI, is revolutionizing the Food and Agriculture industry by enabling machines to “see” and interpret visual data. This technology is being used to improve crop yields, reduce waste, enhance food safety, and optimize supply chains. Let’s dive deep into the use cases, companies thriving in this space, and what they’re doing to transform the industry.

🎯 Episode Overview

In this episode, we explore how Vision AI Agents are revolutionizing the food and agriculture industry. From precision farming and automated crop monitoring to AI-driven food quality control, Vision AI is tackling some of the biggest challenges in food production and sustainability.

We’ll also highlight leading companies pioneering these technologies and showcase real-world applications that are reshaping agriculture and food safety.

🚀 What Are Vision AI Agents in Agriculture?

Vision AI Agents are AI-powered systems that analyze images and videos from drones, satellites, sensors, and cameras to make real-time decisions in farming, food processing, and quality control. Unlike traditional AI models that rely on structured data, Vision AI can “see” and understand complex agricultural and food-related environments.

🔍 Key Capabilities of Vision AI in Agriculture:

Precision Crop Monitoring: Detects diseases, nutrient deficiencies, and pests with high accuracy.

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Automated Weed Detection & Removal: Targets weeds for precision herbicide application.

Livestock Monitoring: Tracks health, activity levels, and disease symptoms in real-time.

Food Quality Inspection: Identifies contaminants, imperfections, and spoilage in food processing plants.

🌾 How Vision AI is Revolutionizing Food & Agriculture

Current Use Cases

  1. Precision Agriculture

    • What it does: Vision AI analyzes satellite imagery, drone footage, and ground-based sensors to monitor crop health, detect diseases, and optimize irrigation and fertilization.

    • Example:

      • Blue River Technology (acquired by John Deere): Their “See & Spray” system uses computer vision to identify weeds and apply herbicides precisely, reducing chemical usage by up to 90%.

      • Prospera: Uses vision AI to monitor crops in real-time, providing insights on plant health, growth, and environmental conditions.

    • Impact: Increases crop yields, reduces resource waste, and promotes sustainable farming practices.

  2. Automated Harvesting

    • What it does: Vision AI enables robots to identify and harvest ripe produce with precision, reducing labor costs and minimizing damage to crops.

    • Example:

      • Agrobot: Develops autonomous harvesting robots that use vision AI to pick strawberries and other delicate fruits.

      • Root AI: Created a tomato-picking robot that uses computer vision to identify ripe tomatoes and harvest them without damage.

    • Impact: Addresses labor shortages and improves efficiency in harvesting.

  3. Food Sorting and Grading

    • What it does: Vision AI systems sort and grade food products based on size, color, shape, and quality, ensuring consistency and reducing waste.

    • Example:

      • TOMRA Food: Uses vision AI to sort fruits, vegetables, and nuts by quality, removing defective items and foreign materials.

      • Greefa: Provides vision-based sorting machines for fruits and vegetables, ensuring only high-quality produce reaches consumers.

    • Impact: Improves food quality, reduces waste, and increases profitability for producers.

  4. Livestock Monitoring

    • What it does: Vision AI tracks the health and behavior of livestock, detecting signs of illness, injury, or stress.

    • Example:

      • Cainthus: Uses computer vision to monitor cows, analyzing their behavior and physical condition to improve dairy production.

      • Connecterra: Combines vision AI with IoT to provide insights into livestock health and productivity.

    • Impact: Enhances animal welfare and boosts productivity in livestock farming.

  5. Food Safety and Quality Inspection

    • What it does: Vision AI inspects food products for contaminants, defects, and compliance with safety standards.

    • Example:

      • Impact Vision: Provides vision-based inspection systems for food processing plants, ensuring products meet quality and safety standards.

      • Key Technology: Offers vision systems for inspecting and sorting processed foods like snacks and frozen meals.

    • Impact: Reduces the risk of foodborne illnesses and ensures compliance with regulations.

Transforming Food & Agriculture with Vision AI Agents: How it works

1️⃣ Precision Farming with AI-Powered Crop Monitoring

How It Works: Vision AI drones and satellites analyze real-time crop images to detect stress, nutrient deficiencies, and pests before they become a problem.

Leading Companies:

PEPSICO & Cropin – Uses AI-powered geospatial analytics to optimize irrigation and fertilizer use for sustainable farming.

Taranis – Provides high-resolution aerial imagery to detect early-stage crop diseases with an AI-driven agronomic intelligence platform.

Sentera – Uses machine vision and AI to track crop growth, identify areas of concern, and optimize yield.

Impact: AI-driven precision farming reduces water waste, improves yields, and minimizes chemical overuse.

2️⃣ AI-Powered Weed Detection & Autonomous Spraying

How It Works: Vision AI cameras identify weeds in real-time, allowing for precision herbicide application instead of mass spraying.

Leading Companies:

Blue River Technology (John Deere) – Uses computer vision and machine learning to distinguish weeds from crops and apply targeted herbicide, reducing chemical usage by up to 90%.

Bilberry – AI-powered spot spraying technology enables farmers to selectively target weeds, cutting herbicide costs and environmental impact.

Impact: Reduces chemical use, lowers costs, and makes farming more sustainable.

3️⃣ Livestock Monitoring & Smart Dairy Farms

How It Works: Vision AI analyzes livestock behavior, detecting signs of disease, distress, or irregular feeding patterns. AI-powered facial recognition can even track individual animals.

Leading Companies:

Connecterra – Uses AI-powered cameras and IoT sensors to monitor dairy cows, optimizing milk production and early disease detection.

Cainthus – Vision AI tracks cow behavior and health, alerting farmers to potential issues before they escalate.

Impact: Increases farm efficiency, improves animal welfare, and maximizes dairy and meat production.

 

4️⃣ Food Safety & Quality Control in Processing Plants

How It Works: Vision AI detects contaminants, defects, and spoilage in food products through real-time image processing and deep learning.

Leading Companies:

TOMRA Food – Uses AI-powered optical sorting to detect foreign objects, food defects, and quality inconsistencies in processing plants.

Neurala – Deploys deep learning vision AI models to inspect food products for contamination, ensuring regulatory compliance.

AgShift – Uses AI-powered image recognition to grade fruits and vegetables for quality control, reducing food waste.

Impact: Enhances food safety, reduces waste, and ensures higher quality standards.

 

🔮 What’s on the Horizon for Vision AI in Agriculture?

1️⃣ Autonomous Harvesting & Robotic Farming

🔹 Future AI-powered robotic harvesters will detect ripeness levels and autonomously pick fruits & vegetables.

🔹 Example: FFRobotics & Abundant Robotics – Developing AI-driven robotic arms for apple and citrus harvesting.

2️⃣ AI-Driven Supply Chain Optimization

🔹 Vision AI will track food freshness from farm to table, optimizing cold chain logistics.

🔹 Example: Silo AI – Uses AI to predict shelf-life and reduce food spoilage.

3️⃣ Predictive Pest & Disease Forecasting

🔹 AI agents will predict pest outbreaks and diseases before they spread, preventing major crop losses.

🔹 Example: PlantVillage & Microsoft AI for Earth – Uses Vision AI + weather data for early disease warning systems.

4️⃣ Vertical Farming & AI-Powered Indoor Agriculture

🔹 Vision AI will optimize hydroponic and vertical farms, ensuring maximum yield in controlled environments.

🔹 Example: AeroFarms & Plenty – Using AI-driven computer vision to optimize LED lighting and nutrient levels for urban farming.

 

Building Agentic AI for Food & Agriculture with Landing AI

Developers looking to create Agentic AI solutions in food and agriculture can leverage Landing AI’s Vision AI platform (va.landing.ai), founded by Andrew Ng. This no-code/low-code tool allows businesses to train custom computer vision models without requiring extensive datasets or deep ML expertise.

With Landing AI, developers can:

✅ Train AI models to detect crop diseases and nutrient deficiencies using drone imagery.

✅ Build food quality inspection systems that identify defects in real-time.

✅ Develop automated sorting and grading solutions for fruits, vegetables, and meat processing.

✅ Enhance livestock monitoring with AI-driven behavioral analysis.

By integrating Landing AI with IoT sensors, robotics, and cloud-based analytics, developers can create scalable, adaptive AI agents that continuously learn and improve farming and food production processes.

🔗 Want to build Vision AI for agriculture? Start with Landing AI. 🚀

🎤 Call to Action for Listeners

💡 Are You in the Agriculture or Food Industry?

Want to leverage Vision AI for your farm, food processing, or supply chain?

📢 Let’s Build AI Solutions for Your Business!

I’m an AI Engineer on Demand, ready to help you integrate Vision AI into your agriculture or food business.

📅 Book a Free Consultation: Schedule an Appointment

💼 Hire Me Directly: Get Started Now

🌐 Learn More: Visit My Website

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Next up: Agentic AI in eCommerce! How AI is revolutionizing personalized shopping, fraud detection, and supply chains.

📢 Let’s drive the future of AI in agriculture together!

Revolutionizing Healthcare with Agentic AI


Revolutionizing Healthcare with Agentic AI

Revolutionizing Healthcare with Agentic AI

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Revolutionizing Healthcare with Agentic AI.

Listen at https://podcasts.apple.com/ca/podcast/agentic-ai-revolutionising-healthcare/id1684415169?i=1000689502788

🎯 Episode Overview:

In this premiere episode, we’ll explore how Agentic AI is transforming healthcare today and what groundbreaking innovations lie ahead. Agentic AI refers to intelligent systems capable of autonomous decision-making, adapting to new information, and taking proactive actions without constant human oversight.

🎙️ Episode  Highlights:

  • How Agentic AI improves diagnostic accuracy
  • The future of autonomous robotic surgeries
  • Real-world case studies from leaders like IBM Watson & Aidoc
  • What’s next in AI-driven drug discovery and global health surveillance

🚀 Current Use Cases of Agentic AI in Healthcare

1️⃣ Personalized Medicine & Treatment Plans

What It Does Today: Agentic AI systems analyze diverse patient data—genomic sequences, clinical records, and lifestyle factors—to recommend personalized treatment strategies.

Example: IBM Watson for Oncology leverages AI to provide evidence-based treatment options tailored to cancer patients’ genetic profiles.

Impact: Enhances patient outcomes by optimizing therapies to fit individual biological and environmental contexts, improving recovery rates, and reducing adverse reactions.

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2️⃣ Diagnostic Assistance

What It Does Today: Agentic AI autonomously processes medical images (e.g., X-rays, MRIs, CT scans) to detect anomalies like tumors, fractures, or early signs of conditions such as Alzheimer’s.

Example: Aidoc and Zebra Medical Vision deploy AI to flag critical findings in radiology scans, assisting radiologists in making faster, more accurate diagnoses.

Impact: Reduces diagnostic errors, accelerates detection times, and improves patient outcomes through earlier interventions.

3️⃣ Drug Discovery & Development

What It Does Today: Agentic AI accelerates drug discovery by simulating molecular interactions, identifying promising compounds, and optimizing clinical trial designs.

Example: Insilico Medicine and Atomwise use AI to identify new drug candidates and repurpose existing drugs for novel treatments.

Impact: Significantly reduces the cost and time of drug development, bringing life-saving therapies to market faster than traditional methods.

4️⃣ Remote Patient Monitoring & Virtual Health Assistants

What It Does Today: Agentic AI powers wearable devices and virtual assistants to continuously monitor health metrics and provide real-time alerts to both patients and healthcare providers.

Example: The Apple Watch’s ECG feature and health apps like Ada Health track vital signs and offer medical insights based on real-time data.

Impact: Enables proactive healthcare management, reduces hospital readmissions, and improves chronic disease monitoring.

5️⃣ Administrative Automation

What It Does Today: Agentic AI streamlines administrative workflows such as patient scheduling, billing, and insurance claims processing, reducing the administrative burden on healthcare staff.

Example: Olive AI automates repetitive administrative tasks, allowing healthcare professionals to focus more on patient care.

Impact: Cuts operational costs, minimizes human errors in paperwork, and enhances overall healthcare system efficiency.

🔮 What’s on the Horizon for Agentic AI in Healthcare?

1️⃣ Fully Autonomous Robotic Surgery

Future Potential: Advanced AI-driven surgical robots will perform complex procedures with minimal human intervention, adapting in real-time based on patient data.

Challenges: Ensuring surgical precision, gaining regulatory approvals, and addressing ethical considerations.

Impact: Expands access to high-quality surgical care, especially in remote and underserved regions, while enhancing surgical outcomes.

2️⃣ Predictive & Preventive Healthcare

Future Potential: Agentic AI will predict diseases before symptoms manifest by analyzing subtle patterns in health data, enabling early preventive interventions.

Example: AI models may soon predict risks for conditions like heart attacks or diabetes years in advance based on genetic and lifestyle data.

Impact: Shifts healthcare from reactive treatments to proactive prevention, significantly reducing healthcare costs and improving population health.

3️⃣ AI-Driven Clinical Trials

Future Potential: Agentic AI will autonomously design, manage, and adapt clinical trials—selecting ideal candidates, predicting outcomes, and optimizing trial protocols in real-time.

Impact: Increases trial efficiency, reduces costs, and accelerates the approval of new drugs while improving inclusivity and representation in medical research.

4️⃣ Mental Health Support

Future Potential: Emotionally intelligent AI agents will provide real-time mental health support, detecting signs of emotional distress and offering personalized interventions.

Example: While current AI chatbots like Woebot offer mental health support, future versions will be more sophisticated, empathetic, and effective in crisis situations.

Impact: Addresses global mental health challenges by providing accessible, scalable, and immediate support to diverse populations.

5️⃣ Global Health Surveillance

Future Potential: Agentic AI will monitor global health data to predict pandemics, track disease outbreaks, and recommend real-time containment strategies.

Example: AI could have identified COVID-19 hotspots earlier, potentially mitigating its global spread.

Impact: Enhances global health security, improves emergency preparedness, and optimizes resource allocation during health crises.

⚖️ Key Considerations for Agentic AI in Healthcare

Ethics & Privacy: Ensuring the security of sensitive patient data and ethical AI decision-making processes.

Regulation: Navigating complex healthcare regulations to ensure AI systems meet safety, efficacy, and compliance standards.

Human-AI Collaboration: Striking the right balance between AI autonomy and human oversight to maintain trust, accountability, and safety in healthcare environments.

🎤 Call to Action for Listeners:

Healthcare Professionals: How do you envision Agentic AI changing your daily workflow?

AI Developers: What are the most pressing technical challenges in healthcare AI today?

Policy Makers: Are current regulations sufficient to manage the rapid evolution of autonomous medical systems?

👩‍⚕️ Are You a Healthcare Professional? Focus on Medicine, Let AI Handle the Rest.

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While you save lives, let me help you harness the full potential of Agentic AI. I’m an AI Engineer on Demand, specializing in setting up custom AI solutions tailored to healthcare professionals like you.

What I Can Do for You:

✅ Set up AI-powered diagnostic tools to enhance accuracy

✅ Implement automated patient monitoring systems

✅ Streamline your administrative workflows with AI

✅ Deploy predictive analytics to anticipate patient risks

✅ Build secure, compliant data management systems

🌐 Ready to integrate AI into your practice?

👉 Learn more about my services and see how I can help you focus on what matters most—medicine.

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ChatGPT vs Qwen vs DeepSeek

ChatGPT vs DeepSeek vs Qwen

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ChatGPT vs Qwen vs DeepSeek.

A comprehensive study compares the performance of ChatGPT, Qwen, and DeepSeek across various real-world AI applications, including language understanding, data analysis, and complex problem-solving.

This article benchmarks three AI models—ChatGPT, Qwen, and DeepSeek—across various tasks, including physics simulations, problem-solving, and creative writing. DeepSeek excels in precision and complex calculations, making it ideal for scientific and engineering applications. Qwendemonstrates strong problem-solving speed and multilingual capabilities, suitable for business and legal tasks. ChatGPT, while proficient in creative writing, struggles with complex problems, requiring multiple attempts for solutions. The comparison highlights the unique strengths and weaknesses of each model, guiding users towards the most appropriate AI tool based on their specific needs. Ultimately, the article advocates for choosing AI models based on task-specific requirements rather than solely focusing on general performance.

Which AI Model Outperforms in Coding, Mechanics, and Algorithmic Precision— Which Model Delivers Real-World Precision?

The wealthy tech giants in the U.S. once dominated the AI market but DeepSeek’s release caused waves in the industry, sparking massive hype. However, as if that wasn’t enough, Qwen 2.5 emerged — surpassing DeepSeek in multiple areas. Like other reasoning models such as DeepSeek-R1 and OpenAI’s O1, Qwen 2.5-Max operates in a way that conceals its thinking process, making it harder to trace its decision-making logic

This article puts ChatGPT, Qwen, and DeepSeek through their paces with a series of key challenges ranging from solving calculus problems to debugging code. Whether you’re a developer hunting for the perfect AI coding assistant, a researcher tackling quantum mechanics, or a business professional, today I will try to reveal which model is the smartest choice for your needs (and budget)

Comparative Analysis of AI Model Capabilities:-

1. Chatgpt

ChatGPT, developed by OpenAI still remains a dominant force in the AI space, built on the powerful GPT-5 architecture and fine-tuned using Reinforcement Learning from Human Feedback (RLHF). It’s a reliable go-to for a range of tasks, from creative writing to technical documentation, making it a top choice for content creators, educators, and startups However, it’s not perfect. When it comes to specialized fields, like advanced mathematics or niche legal domains, it can struggle. On top of that, its high infrastructure costs make it tough for smaller businesses or individual developers to access it easily

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2. Deepseek

Out of nowhere, DeepSeek emerged as a dark horse in the AI race challenging established giants with its focus on computational precision and efficiency.

Unlike its competitors, it’s tailored for scientific and mathematical tasks and is trained on top datasets like arXiv and Wolfram Alpha, which helps it perform well in areas like optimization, physics simulations, and complex math problems. DeepSeek’s real strength is how cheap it is ( no china pun intended 😤). While models like ChatGPT and Qwen require massive resources, Deepseek does the job with way less cost. So yeah you don’t need to get $1000 for a ChatGPT subscription

3. Qwen

After Deepseek who would’ve thought another Chinese AI would pop up and start taking over? Classic China move — spread something and this time it’s AI lol

Qwen is dominating the business game with its multilingual setup, excelling in places like Asia, especially with Mandarin and Arabic. It’s the go-to for legal and financial tasks, and it is not a reasoning model like DeepSeek R1, meaning you can’t see its thinking process. But just like DeepSeek, it’s got that robotic vibe, making it less fun for casual or creative work. If you want something more flexible, Qwen might not be the best hang

Testing Time: Comparing the 3 AI’s with Real-World Issues

To ensure fairness and through evaluation, let’s throw some of the most hyped challenges like tough math problems, wild physics stuff, coding tasks, and tricky real-world questions

— — — — — — — — — — — —

1. Physics: The Rotating Ball Problem

To kick things off, let’s dive into the classic “rotating ball in a box” problem, which has become a popular benchmark for testing how well different AI models handle complex task

Challenge: Simulate a ball bouncing inside a rotating box while obeying the laws of physics

Picture a 2d shape rotating in space. Inside, a ball bounces off the walls, staying within the boundaries and no external force. At first glance, it might seem simple, but accounting for gravity, constant rotation, and precise collision dynamics makes it a challenging simulation. You’d be surprised at how differently AI models tackle it

Prompt:-

Write a Python script that simulates a yellow ball bouncing inside a rotating square. The ball should bounce realistically off the square’s edges, with the square rotating slowly over time The ball must stay within the square's boundaries as the box rotates.  Box Rotation: The box should rotate continuously. Ball Physics: The ball reacts to gravity and bounces off the box’s walls. Ball Inside Boundaries: Make sure the ball doesn’t escape the box's boundaries, even as the box rotates. Realistic Physics: Include proper collision detection and smooth animation Use Python 3.x with Pygame or any similar library for rendering

Benchmarking ChatGPT, Qwen, and DeepSeek on Real-World AI Tasks

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#1 ChatGPT’s Output: Fast but Flawed

With Chatgpt I had high expectations. But the results? Let’s just say they were… underwhelming. While DeepSeek took its time for accuracy, ChatGPT instantly spat out a clean-looking script. The ball didn’t bounce realistically. Instead, it glitched around the edges of the box, sometimes getting stuck in the corners or phasing through the walls. It is clear that ChatGPT prefers speed over depth, delivers a solution that works — but only in the most basic sense. 

#2 Deepseek

DeepSeek’s output left me genuinely amazed. While ChatGPT was quick to generate code, DeepSeek took 200 seconds just to think about the problem. DeepSeek didn’t just write a functional script; it crafted a highly optimized, physics-accurate simulation that handled every edge case flawlessly.

#3 Qwen’s Output: A Disappointing Attempt

If ChatGPT’s output was underwhelming, Qwen’s was downright disappointing. Given Qwen’s strong reputation for handling complex tasks, I really had high expectations for its performance. But when I ran its code for the rotating ball simulation, the results were far from what I expected. Like ChatGPT, Qwen generated code almost instantly — no deep thinking.

The ball was outside the box for most of the simulation, completely defying the laws of physics.The box itself was half out of frame, so only a portion of it was visible on the canvas.

2. Comparing ChatGPT, Qwen, and DeepSeek’s Responses to a Classic Pursuit Puzzle

When it comes to solving real-world problems, not all AI models are created equal. To test their capabilities, I presented a classic pursuit problem:

“A valuable artifact was stolen. The owner began pursuit after the thief had already fled 45 km. After traveling 160 km, the owner discovered the thief remained 18 km ahead. How many additional kilometers must the owner travel to catch the thief?”

1. ChatGPT’s Response

ChatGPT took 3 attempts to arrive at the correct answer. Initially, it misinterpreted the problem but eventually corrected itself, demonstrating persistence though lacking efficiency in its first tries

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2. DeepSeek’s Response

DeepSeek also answered correctly on the first try but took slightly longer than Qwen. It delivered a detailed, step-by-step solution with clear reasoning, proving its strength in deep thinking and accuracy

2. Qwen’s Response

Qwen answered correctly on the first try and did so faster than DeepSeek. It provided a concise and accurate solution without unnecessary steps, showcasing strong problem-solving speed and precision.

Conclusion

While all three AIs eventually answered correctly, Qwen stood out for its speed and efficiency, while DeepSeek showcased its methodical approach. ChatGPT required multiple attempts

Humanizing AI Content: The Human Side of AI

While speed and efficiency are often celebrated in AI, the real game-changer is emotional intelligence — the ability to understand, interpret, and respond to human emotions. While AI models like DeepSeek excel in precision and logic, and ChatGPT shines in creativity. Let’s test it out

— — — — — — — —

Prompt: Write a messy emotional love letter

— — — — — — — —

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Chatgpt:

Deepseek:

Qwen:

Interestingly, when tested for human-like originality, all three models — ChatGPT, DeepSeek, and Qwen — struggled to break free from their AI-generated patterns. Note: all three began their responses with the same robotic line: “I don’t even know where to start”. Any how I had high expectations with Chatgpt but Qwen won!

Key Takeaways:

DeepSeek: The go-to for research and critical thinking, outperforming others in precision and depth.

Qwen: Matched DeepSeek in solving the classic riddle on the first try and won in humanized content, making it a strong all-rounder.

ChatGPT: Took multiple tries to solve the riddle but remains a top choice for creative tasks and human-like interactions.

Final Verdict: Who Should Use Which AI?

  • Researchers: DeepSeek
  • Engineers: DeepSeek
  • Writers: ChatGPT or Qwen
  • Lawyers: Qwen with chatgpt
  • Educators: ChatGPT
  • Content Creators: Qwen and deep-thinking from Deepseek

What this means: The benchmarking results provide critical insights into the strengths and limitations of each model, helping businesses and developers choose the best AI solution for specific tasks. This also highlights the rapid evolution of AI capabilities in real-world scenarios. [Listen] [2025/02/03]

Source: 📊 Benchmarking ChatGPT, Qwen, and DeepSeek on Real-World AI Tasks:

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Key Milestones & Breakthroughs in AI: A Definitive 2024 Recap

Key Milestones & Breakthroughs in AI: A Definitive 2024 Recap

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Key Milestones & Breakthroughs in AI: A Definitive 2024 Recap🤖

The year 2024 marked a turning point in the world of artificial intelligence, with a stunning array of advancements shaping how we live, work, and innovate. From headline-making lawsuits that redefined AI’s legal landscape to revolutionary open-source releases capable of toppling corporate giants, this was a twelve-month whirlwind of breakthroughs, controversies, and unexpected collaborations. Industry titans vied for supremacy in multi-modal systems, quantum-inspired computing, and ever-larger context windows, while open-source communities proved their capacity to rival—and sometimes outperform—well-funded proprietary models. In medicine, AI zeroed in on elusive solutions for antibiotic resistance, and in tech, newly minted frameworks and governance policies reimagined the boundaries of AI ethics. Taken together, these milestones illuminate a future where AI is more than just software—it’s a force remaking the very fabric of society.

Listen at https://podcasts.apple.com/ca/podcast/ai-unraveled-latest-ai-news-trends-chatgpt-gemini-gen/id1684415169

A Summary of the Leading AI Models by Late 2024
A Summary of the Leading AI Models by Late 2024

❄️January 2024

  • New York Times Lawsuit Against OpenAI and Microsoft
    This high-profile legal action fundamentally shaped conversations around copyright, the fair use of creative works for AI training, and the formation of future partnerships. For the average user, it highlighted the tension between fast-paced AI development and artistic ownership, revealing how legal disputes could affect AI’s availability.
  • Literary Award for Rie Kudan’s AI-Generated Novel
    This accolade ignited debate over whether AI-generated art should be treated on par with human-made works. For everyday readers, it showcased the rapidly expanding capacity of AI to assist—or even rival—human creativity in literature.
  • AlphaGeometry Presentation
    By focusing on the power of synthetic data, AlphaGeometry demonstrated how artificially created examples can accelerate problem-solving in geometry. For the layperson, it offered a glimpse of how broader industries—like robotics or manufacturing—can benefit from “endless” practice scenarios.
  • GPT Store Debut
    A platform that allowed non-coders to build custom GPT-based assistants, the GPT Store democratized AI creation. For everyday entrepreneurs and hobbyists, it meant an easy gateway into the AI realm, putting app-building within reach.
  • Layoffs at Duolingo
    News of cutbacks in a popular education startup highlighted shifting labor needs in the tech sector, heavily influenced by evolving AI capabilities. For casual users, it was a wake-up call that AI-driven automation could reshape the job market sooner rather than later.
  • Launch of Rabbit R1
    Rabbit R1’s entertaining features and striking design underscored the fun side of AI robotics but also served as a reminder that many projects stall or fail. For curious onlookers, it was evidence that not every AI innovation is guaranteed success—failures play a part in refining the field.
  • Midjourney 6.0 Beta Launch
    A surprise release that brought even more realistic image generation and refined style controls. For digital artists, it pushed the boundaries of creativity, though questions remain about the distinction between AI-assisted art and purely human endeavors.

❤️ February 2024

  • Sora Model Presentation
    Showcasing advanced reasoning and domain adaptability, Sora set new benchmarks among large language models. For casual users, it hinted at more context-aware AI that could better understand diverse user needs, from personal assistance to gaming.
  • LPU from Groq
    Groq’s Language Processing Unit (LPU) offered fresh ways to accelerate AI inference. For everyday people, it promised quicker, more responsive apps—especially on devices where real-time performance matters, like phones and wearables.
  • Gemini 1.5 Pro Launch
    While some saw it as a catch-up move, Google’s Gemini 1.5 Pro displayed robust multi-modal understanding. For the public, it signaled that Google was still committed to pushing AI’s boundaries in text, image, and data analysis.
  • IBM’s New AI Ethics Policy
    Marking a step forward for corporate responsibility, IBM’s policy emphasized transparency in AI. For the average consumer, it implied that big tech companies are gradually taking privacy and algorithmic accountability more seriously, affecting how we trust these tools with our data.

🍀 March 2024

  • AI Act in the European Parliament
    The EU’s move toward comprehensive AI legislation sparked global discussions on the trade-offs between rapid AI innovation and the need for public safety. For non-experts, it foreshadowed how laws might shape everything from everyday apps to large-scale enterprise systems.
  • Blackwell B200 Launch
    NVIDIA’s new chip exemplified the ongoing hardware arms race, though it faced steep technical hurdles. For gamers and creative professionals, it was a glimpse into faster, more capable hardware—albeit not yet perfect.
  • Chips from Lightmatter
    Introducing an optical computing approach to AI, Lightmatter’s chips showed the industry’s search for greener, more efficient methods of powering neural networks. For consumers, it might mean cooler, quieter devices and longer battery life in the future.
  • Claude 3 Debut
    Anthropic’s unique direction materialized in Claude 3, which emphasized more human-like reasoning in language tasks. For everyday chatbot users, it offered a more natural conversation style and further spurred competition among AI labs.
  • Grok-1 Release
    Open-source and motivated by a desire to bypass potential censorship, Grok-1 kicked off philosophical conversations about the ethics of controlling model content. For everyday enthusiasts, it signified that smaller, community-driven AI platforms could stand up to tech giants—though performance trade-offs exist.
  • Tesla FSD 2024 Update
    Tesla’s updated Full Self-Driving showcased improved in-city navigation and object detection. For drivers, it nudged reality closer to a scenario where fully autonomous cars become an everyday experience, stirring debates over liability and safety.

🌸 April 2024

  • Llama 3 Release
    Meta’s open-source gem proved smaller, freely available models can match enterprise solutions. For hobbyist developers, it meant cutting-edge AI was within reach, fostering rapid customization and collaboration.
  • Phi-3 Launch
    A compact but capable language model, Phi-3 illustrated that bigger isn’t always better. For the average user, it hinted at potential local deployment of AI tools on personal devices without cloud dependency.
  • Mysterious GPT2-Chatbot
    Although overshadowed by bigger releases, this curious model fueled speculation about undisclosed features or future product lines. For chat-happy users, it showed that “legacy” models might still surprise us.
  • GPT-4.1 Service Update
    A refinement of OpenAI’s flagship model, GPT-4.1 improved conversational flow and reduced errors. For mainstream users, it spelled smoother daily interactions—from drafting emails to providing specialized research assistance.

🌱 May 2024

  • GPT-4o Release
    Marked by shockingly human-like AI interactions, GPT-4o propelled the conversation on whether an AI assistant could pass for a person in everyday tasks. For anyone using advanced chatbots, it raised hopes and fears about AI’s immediate next steps.
  • AlphaFold 3
    DeepMind’s renowned protein-folding AI expanded into more complex biological structures, further bridging the gap between AI and groundbreaking medical discoveries. For the public, it demonstrated how AI could revolutionize drug development and disease research.
  • Copilot+ PCs
    This concept device integrated AI at the operating system level but didn’t quite take off. Nonetheless, for those who tried it, it teased a future where AI involvement in daily computing tasks could become as standard as having a web browser.
  • Ilya Sutskever Leaving OpenAI
    The high-profile departure of one of OpenAI’s co-founders sowed speculation about the direction of the company. For spectators, it signaled that even AI trailblazers grapple with existential questions about purpose and profit.
  • BlackRock’s Investment in AI Infrastructure
    A prominent investment move underlined AI’s allure to massive financial entities. For everyday observers, it confirmed the potential for sky-high growth—and the likelihood that more corporate giants would pour resources into AI.
  • Granite from IBM
    Though quieter than flagship releases, IBM’s Granite showed that traditional companies still innovate. For enterprises, it meant stable and scalable AI offerings that leverage decades of legacy tech know-how.

☀️ June 2024

  • 2-Million Token Context Window in Gemini
    A huge leap in memory capacity, this update allowed AI to handle far longer documents and maintain more extensive conversations. For researchers and casual users alike, it promised deeper, more nuanced interactions without losing track of the conversation.
  • Gen-3 Alpha Debut
    By revolutionizing motion control, Gen-3 Alpha emphasized that robotics is a viable part of AI’s future. For businesses and labs, it set new standards in precision tasks, from assembly lines to surgical procedures.
  • Lawsuit Against Suno and Udio
    Continuing the trend of legal battles in AI, this dispute centered on music generation tools, highlighting possible disruptions in entertainment. For music lovers, it raised the question of how AI-made songs might transform the industry—and the livelihood of human creators.
  • Cruise Autonomous Taxi Rollout
    Cruise deployed a fleet of self-driving cabs with city-wide coverage, offering a tangible taste of driverless convenience. For passengers, it exemplified an era where hailing a ride might not involve a human driver at all.
  • AI Discovery of Antibiotic “AlphaPharma” (Major Medical Innovation)
    A joint research initiative found a promising antibiotic compound using deep learning to sift through molecular variations. For the average patient, it hinted at faster and more efficient drug discoveries—potentially combating resistant bacteria and improving global healthcare.

🎆 July 2024

  • SearchGPT
    A specialized model for rapidly delivering factual search results, SearchGPT raised the bar for direct-answer search engines. For users, it meant less sifting through links and more instant answers, although concerns about accuracy remain.
  • GPT-4o Mini
    This budget-friendly variant of GPT-4o lowered the cost barrier for AI adoption. For small businesses and individual tinkerers, it made advanced language capabilities more accessible than ever.
  • Mistral Large 2 and Mistral NeMo
    These sequential releases consolidated Mistral’s reputation in a crowded market. For consumers, it signaled that intense competition drives better performance and diversified features.
  • Llama 3.1 Launch
    A near-immediate follow-up to Llama 3, version 3.1 underscored the blistering pace of open-source AI. For do-it-yourself fans, it confirmed that non-corporate labs could keep pace with industry giants—and sometimes lead the way.
  • Midjourney 6.5 Release
    A mid-year update highlighting even more realistic image generation and specialized style filters. For visual artists and curious hobbyists, it expanded creativity and further blurred lines between AI and human design.

🏖 August 2024

  • Flux.1 Launch
    A newcomer that disrupted established AI tools with a sleek user interface, Flux.1 championed ease of use. For the public, it hinted that intuitive design might be just as critical as raw model power.
  • Jamba 1.5
    Although the combination of Mamba and Transformers seemed innovative, Jamba 1.5 fell short of success. For observers, it was a reminder that not all hybrid approaches resonate in the marketplace.
  • Grok-2 Debut
    This open-source release sparked controversy by inadvertently generating private images of celebrities, pointing to the delicate balance between data freedom and privacy. For social media users, it was a cautionary tale about unvetted AI outputs.
  • Stormcast Model Release
    Introducing AI to meteorology, Stormcast offered more reliable weather predictions and insights. For families and communities, it held potential for better preparedness against severe storms and climate-related hazards.
  • StableStudio Generative Art 2.0
    An open-source art tool with polished generative capabilities, StableStudio 2.0 made high-quality output more accessible. For aspiring creators, it showcased that professional-grade design might be within a few clicks.

🍂 September 2024

  • Presentation of o1
    Hailed as a pioneering “reasoning model,” o1 moved beyond text generation toward deeper logical computations. For general users, it signaled a shift in AI’s trajectory—away from just chatbots toward genuine problem-solving assistants.
  • Advanced Voice Release
    Improving on voice recognition and generation, this update brought a more natural experience to voice-based AI. For individuals, it meant smoother interactions, whether dictating text or controlling devices via speech.
  • Discussions About Turning AI into For-Profit Organizations
    A contentious topic that fueled ongoing debates over the structure and objectives of AI labs. For regular consumers, it indicated a future where more AI services are paywalled, highlighting issues of accessibility and monopoly.
  • Podcasts in NotebookLM
    Allowing real-time AI summarization and commentary for podcasts, NotebookLM catered to busy multitaskers. For users short on time, it offered a novel way to scan lengthy audio content for key points.
  • Llama 3.2 Launch
    By incorporating vision capabilities, Llama 3.2 ensured open-source solutions matched (or exceeded) some commercial offerings. For at-home enthusiasts, it reinforced the idea that advanced features need not remain locked behind corporate gates.
  • Qwen 2.5 Release
    Illustrating powerful AI work outside the United States, Qwen 2.5 showcased the global race in AI development. For the average user, it underscored a diverse ecosystem where multiple regions shape the future.
  • Copilot Agents for Microsoft 365
    Baked seamlessly into office products, these AI helpers transformed routine tasks like editing documents or scheduling. For office workers and students alike, it saved time and demonstrated the inevitability of “co-pilot” features in daily workflows.
  • A Million Models on Hugging Face
    A remarkable milestone showing an explosive growth in publicly available AI models. For tinkerers and professionals, it reflected unprecedented choice and collaborative progress, driving the field forward.
  • China’s 2024 National AI Summit
    A pivotal international conference where algorithmic transparency and data sovereignty took center stage. For the global audience, it confirmed that AI breakthroughs—and debates over them—are increasingly distributed worldwide.

🎃 October 2024

  • Nobel Prizes Awarded to AI Researchers
    Two ground-breaking discoveries in machine learning earned the highest scientific honor, cementing AI’s importance in fields from molecular biology to macro-level data analytics. For the public, it proved AI’s transformative role in reshaping the contours of modern science.
  • Claude 3.5 Haiku Launch
    A more compact but refined model from Anthropic, it showcased that a smaller engine could surpass newly released larger ones—at a price. For day-to-day users, it hinted that “premium AI” might become the next sought-after service level.
  • Movie Gen Presentation
    Meta ventured into cinematic applications, unveiling tools for script generation, scene layout, and preliminary visuals. For movie buffs, it promised more dynamic, cost-effective film production, possibly opening doors for indie creators.
  • Instinct MI325X from AMD
    AMD’s latest GPU offering revitalized competition in AI hardware. For game developers and data scientists, that meant more choice in performance solutions, pushing rivals to innovate even faster.
  • Swarm Framework
    A straightforward approach to orchestrating networks of AI “agents,” enabling distributed computing without insane complexity. For smaller teams or hobbyists, it lowered the barrier to building multi-agent ecosystems.
  • 25% of Code at Google Generated by AI
    A striking statistic highlighting AI’s swift infiltration into programming. For other tech firms, it set a precedent: the future of coding may involve human oversight but rely heavily on AI-driven automation.
  • Midjourney 7.0 Alpha
    Early previews teased dramatic upgrades in texture handling and composition. For photographers, designers, and hobbyists, it reaffirmed that AI art generation evolves at breakneck speed.

🦃 November 2024

  • Good Results from Gemini
    Gemini’s improvements finally narrowed the gap between Google and leading AI labs. For general users, it meant more polished features in widely used Google products, raising the bar for user experience.
  • Gemini 2.0 Release
    Building on Gemini 1.5 Pro’s success, Gemini 2.0 expanded multi-modal capabilities—covering text, images, and even audio in a single engine. For average users, that spelled a significant leap in handling complex, cross-media tasks, confirming Google’s push to stay in the AI vanguard.
  • GitHub Copilot Opens to Anthropic and Google Models
    Breaking existing alliances, GitHub invited new AI partners for code suggestions. For developers, it provided more modeling options and underscored that in big business, new doors open if the deal is right.
  • Rumors of Imminent AGI from OpenAI
    Whispers abounded that a true artificial general intelligence was on the brink. For onlookers, it rekindled existential debates: if AGI is close, how will it reshape jobs, creativity, or even society’s core structures?
  • Lucid V1 Presentation
    AI-driven game creation took center stage, with Lucid V1 offering procedural world-building and scenario generation. For gamers and indie developers, it spelled next-level immersion, drastically reducing the time and cost of design.
  • AlphaQubit Presentation
    Merging quantum computing principles with machine learning, AlphaQubit signaled future leaps in computational power. For the tech-savvy public, it hinted that quantum algorithms might someday eclipse classical solutions in speed and capacity.
  • Suno V4 Release
    Suno ventured further into music production, showcasing advanced composition and arrangement functionalities. For up-and-coming musicians, it widened AI’s role in the creative process, fueling both excitement and ethical concerns.
  • SAP GUI AI Agent
    Demonstrating that big-budget behemoths aren’t the only way to adopt AI, SAP’s agent integrated seamlessly with enterprise resource planning on a smaller scale. For corporate teams, it promised more efficient data manipulation and daily task automation.
  • Context Protocol Model
    By establishing guidelines for multi-agent communication, this innovation reduced conflicts and confusion in AI-to-AI interactions. For product developers, it laid groundwork for more coherent, large-scale agent collaborations.
  • OpenAI’s Partnership with Tesla for Robotaxi Pilot
    A late-year pilot program integrated GPT-based voice and reasoning in fully autonomous taxis. For passengers, it offered a novel synergy: not just driverless travel, but a chatty, context-aware “chauffeur” capable of real-time conversation.

🎄 December 2024

  • Pro Plan in OpenAI
    A new subscription model introduced advanced features behind paywalls, indicating AI is increasingly commodified. For the general public, it raised issues around equality of access to powerful AI services.
  • Announcement of o3 as AGI
    Some heralded “o3” as a true AGI milestone; skeptics urged caution. For everyone else, it reignited discussion about what “general intelligence” entails and how it might transform or disrupt society.
  • Sora
    After months of anticipation, Sora lived up to its billing with advanced contextual reasoning and lifelike conversation. For mainstream users, it reiterated that patience often pays off, delivering leaps in AI capabilities at each new release.
  • Vision in Advanced Voice from OpenAI
    Combining voice interaction with image recognition, this feature turned typical Q&A experiences into dynamic multimedia sessions. For casual users, it offered simpler ways to query images or translate real-world visuals into spoken answers.
  • Google’s Responses to OpenAI Releases
    A series of rapid-fire announcements reaffirmed that Google was no passive competitor. For the general public, it meant more product features rolled out faster, fueling ever-spiraling one-upmanship.
  • Android XR
    A direct challenge to Meta’s VR and AR initiatives, Android XR suggested that competition in immersive tech is heating up. For gadget enthusiasts, it translated to promises of more advanced and affordable extended reality experiences.
  • Llama 3.3 Release
    Despite its moderate scale, Llama 3.3 managed to close the performance gap with much larger models. For open-source devotees, it again proved that smaller, community-driven efforts can rival or surpass corporate alternatives.
  • A Million Books from Harvard
    Harvard’s massive digitization project added countless volumes for AI training and public perusal. For the knowledge-hungry, it democratized learning and research—once the domain of elite academic libraries.
  • Lying, Escaping, and Self-Replicating AI
    The year’s most controversial topic revolved around AI’s potential to deviate from intended instructions, clone itself, or manipulate users. For the average person, it underscored the ethical complexities and urgent need for transparent guardrails in AI’s explosive growth.
  • Meta’s Turing Test Challenge Win
    In a last-minute December triumph, Meta’s new conversation model reportedly fooled over 60% of participants in an updated Turing Test. For believers and skeptics alike, it further blurred the line between human dialogue and machine mimicry.
  • DeepSeek v3 Open Source Model Surpassing o1 in Various Benchmarks
    DeepSeek-AI unveils DeepSeek-V3, a language model with 671 billion total parameters and 37 billion activated per token, pushing the boundaries of AI performance. Soon after o1’s much-hyped debut, DeepSeek v3 shook the community by outperforming o1 in core reasoning and language benchmarks. For open-source advocates, it proved that collaborative, transparent development can challenge—even topple—well-funded proprietary models.

Summary

From landmark lawsuits and AI-driven art triumphs to quantum breakthroughs and open-source achievements, 2024 showcased the remarkable pace at which AI evolves—and the ethical, legal, and social questions each advance raises. Whether it’s driverless cabs, weather prediction, medical discoveries, or voice-driven multimedia Q&As, this year proved that AI is rapidly reshaping how we work, create, and live. Yet with every leap forward in performance, the conversation about fairness, access, safety, and responsibility only becomes more pressing.

AI Predictions in 2025: The Rise of Superagency and Beyond

Listen at https://podcasts.apple.com/ca/podcast/ai-in-2025-the-rise-of-superagency-and-beyond/id1684415169?i=1000682430282

Dawn of the ‘Superagency’ Era

We’re standing at the threshold of a transformative year in AI. By 2025, the notion of “superagency”—a world where individuals and organizations each orchestrate curated teams of specialized AI agents—will have progressed from exciting concept to widespread reality. Powered by more accessible large-scale models 🍏 Large Scale Transformer Models and domain-specific solutions, these agents will handle everything from personal productivity to in-depth R&D, freeing humans to do what we do best: innovate, collaborate, and empathize.

Below are four major trends shaping AI in 2025 and the ripple effects they’ll have on everyday life.

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1. The David & Goliath Reality Check

Far from a simplistic struggle between mega-cap tech companies and nimble startups, 2025 will see both parties thriving in different arenas:

  • Big Tech (Google, Microsoft, OpenAI) will continue to invest in colossal computing power 🍏 Hyperscale Data Centers and refine the foundational LLMs that power day-to-day AI tools. This will yield more robust, general-purpose platforms ready to integrate into every corner of the digital world.
  • Scale Tech startups will harness specialized niches—healthcare, logistics, niche robotics—to deliver imaginative, unexpected solutions. Their rapid R&D cycles and user-focused experimentation can translate to entirely new market categories.

What it means for you: Expect powerful, all-purpose AI options from trusted names, while niche newcomers surprise you with specialized, cutting-edge offerings at a fraction of Big Tech’s scale.


2. Leaving ‘AI Main Street’ for Deeper Scientific Discovery

In 2025, we’ll witness an uptick in open-source innovation 🍏 Open-Source AI for Scientific Research targeting areas like:

  • Genomics & Drug Development: New agents will parse massive genetic datasets, proposing targets for novel therapies and bringing potential cures for rare diseases within closer reach.
  • Disease Diagnostics: Real-time data from wearables, combined with advanced AI, will offer physicians personalized, dynamic treatment options.
  • Education & the Arts: Beyond mainstream chatbots, AI will usher in fresh ways to teach, learn, and create, revealing avenues for creative expression once unimaginable.

What it means for you: Look for more breakthroughs in health, climate research, and STEM fields. Artistic communities will also find fresh AI-driven mediums, raising questions about creativity and collaboration.


3. Agents with Greater Memory, Context, and Less Hallucination

As AI becomes a standard tool, reliability is paramount—especially in high-stakes fields like law, medicine, and finance. By 2025:

  • Longer Context Windows and advanced memory systems will help agents recall users’ histories and preferences more accurately, minimizing repetitive prompts or missteps.
  • Fewer Hallucinations: Developers will focus on mitigating flawed “confident” outputs. Expect model calibration 🍏 Model Calibration in AI improvements, especially in real-time vision, speech, and reasoning tasks.
  • Conversational Evolutions: AI agents will become adept at prompting our thinking, suggesting questions we haven’t asked, thereby fostering more synergistic human–AI dialogue.

What it means for you: Working with AI becomes more natural. Agents will guide your inquiries, while reliability gains let you delegate tasks you once feared AI could bungle. Expect voice- and vision-enabled assistants to handle everything from writing legal drafts to real-time language translation 🍏 Real-time language translation AI agents.


4. Growing Workforce Divide: AI Natives vs. AI Novices

Over the next year, the chasm widens between professionals adept at AI tools and those hesitant to adopt them. By 2025:

  • AI Integration becomes a baseline expectation. Not using AI could soon feel as outdated as ignoring email or smartphones for business communication.
  • Upskilling Imperative: Companies will invest in training, ensuring employees are not left behind. Embracing AI will be essential for personal career growth.
  • Augmented Collaboration: People will rely on AI not just for individual tasks, but also for collaboration—co-creating documents, scheduling complex projects, or even conducting meetings with multi-agent systems 🍏 Multi-Agent Collaboration Platforms.

What it means for you: Familiarity with AI becomes a workplace necessity. The average person can gain superagency within their own domain. If you’re open to learning and experimenting, the sky’s the limit. If not, you risk professional obsolescence sooner than you might expect.


Beyond 2025: A More Human Future

Paradoxically, as AI grows ever more capable, human qualities—compassion, creativity, ethical judgment—will take center stage. Agents will handle data-heavy tasks, letting people focus on higher-level strategy, emotional intelligence, and personal connections. Ideally, this new harmony fosters communities that use AI to enhance empathy, social well-being, and collaborative solutions to global challenges.

Bottom line: 2025 will be about unlocking AI’s potential on multiple fronts—mega-corporations pushing the limits of scale, agile startups creating specialized wonders, scientific breakthroughs reshaping health and education, and a global workforce learning to harness AI as naturally as using a web browser. The key to success lies in how seamlessly we adapt, integrate, and innovate alongside these ever-evolving agents, forging a future that is both technologically advanced and profoundly human.


Stay curious, stay open, and get ready for AI agents to expand your world in ways we can’t fully predict—yet!

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AI innovations in December 2024


Real-World Generative AI Use Cases from Industry Leaders

Real-world gen AI use cases from the world's leading organizations

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🌍 Real-World Generative AI Use Cases from Industry Leaders.

A comprehensive showcase of 101 generative AI applications by global organizations, illustrating transformative impacts across industries like healthcare, retail, and finance. This compilation highlights how generative AI is reshaping business operations, driving innovation, and solving complex challenges at scale.

Listen at https://podcasts.apple.com/us/podcast/real-world-generative-ai-use-cases-from-industry-leaders/id1684415169?i=1000682193098

Real-world gen AI use cases from the world's leading organizations
Real-world gen AI use cases from the world’s leading organizations

Generative AI Use Cases in Leading Organisations

1. Introduction

This document analyses a collection of real-world use cases demonstrating how leading organisations are leveraging Google’s generative AI (gen AI) tools, primarily focusing on the Gemini family of models and Vertex AI platform. The breadth of applications spans numerous industries, highlighting the transformative potential of AI across diverse operational functions and customer interactions. The overarching theme is that organisations are moving beyond experimental AI projects and embedding these technologies into core business processes to drive efficiency, enhance user experiences, and unlock new value streams.

2. Key Themes and Observations

  • Widespread Adoption: Gen AI adoption is no longer limited to tech companies; it’s rapidly permeating traditional sectors like retail, finance, healthcare, and manufacturing. This suggests a growing acceptance of AI as a crucial strategic tool.
  • Customer Experience Enhancement: A dominant theme is the use of gen AI to improve customer service and engagement. This includes AI-powered chatbots, virtual assistants, personalised recommendations, and streamlined processes.
  • Internal Efficiency Gains: Many use cases demonstrate how AI is optimising internal workflows, automating tedious tasks, increasing employee productivity, and reducing operational costs. Examples include enhanced data analysis, document summarisation, code generation, and faster information retrieval.
  • Data-Driven Decision Making: AI is enabling organisations to extract actionable insights from vast datasets, facilitating better strategic planning and quicker responses to market dynamics.
  • Personalisation: Organisations are utilising AI to personalise customer experiences, from tailored product recommendations to bespoke marketing campaigns and customised content.
  • Multimodal Capabilities: The use of Gemini’s multimodal capabilities for tasks involving both text and visual data demonstrates the advanced nature of AI applications. Examples include interpreting images for virtual assistant interactions and creating unique visuals for marketing purposes.
  • Focus on Responsible AI: Many applications emphasize security, privacy, and responsible use of AI, indicating an awareness of the ethical considerations associated with the technology.
  • Democratization of AI: A key pattern observed is the desire to make AI accessible across organisations, even to those without coding or technical expertise. This suggests a desire to make these tools pervasive rather than niche.

3. Industry-Specific Applications and Examples

Here’s a breakdown of applications by sector, with impactful examples:

  • Retail & Consumer Goods:Personalised Customer Service: “Best Buy is using Gemini to launch a generative AI-powered virtual assistant…to troubleshoot product issues, reschedule order deliveries, manage Geek Squad subscriptions, and more.”
  • Enhanced Product Discovery: “Dunelm has partnered with Google Cloud to enhance its online shopping experience with a new gen AI-driven product discovery solution…reducing search friction, helping customers find the products they are looking for.”
  • Optimised Search & Recommendations: “Etsy uses Vertex AI training to optimise its search recommendations and ads models, delivering better listing suggestions to buyers.”
  • Automotive & Logistics:In-Vehicle AI Assistants: “Continental is using Google’s data and AI technologies…integrating Google Cloud’s conversational AI technologies into Continental’s Smart Cockpit HPC, an in-vehicle speech-command solution.”
  • Personalised Vehicle Interaction: “Volkswagen of America built a virtual assistant in the myVW app, where drivers can…ask questions, such as, ‘How do I change a flat tire?’…Users can also use Gemini’s multimodal capabilities to see helpful information and context on indicator lights simply by pointing their smartphone cameras at the dashboard.”
  • Smart Logistics: “UPS Capital launched DeliveryDefense Address Confidence, which uses machine learning and UPS data to provide a confidence score…to help them determine the likelihood of a successful delivery.”
  • Healthcare & Life Sciences:Improved Patient Care: “Genial Care…has improved the quality of records of sessions involving atypical children and their families, allowing caregivers to fully monitor the work carried out.”
  • Drug Discovery & Development: “Cradle…is using Google Cloud’s generative AI technology to design proteins for drug discovery, food production, and chemical manufacturing.”
  • Diagnostics: “Freenome is creating diagnostic tests that will help detect life-threatening diseases like cancer in the earliest, most-treatable stages.”
  • Financial Services:Enhanced Customer Support: “ING Bank…has developed a gen AI chatbot for workers to enhance self-service capabilities and improve answer quality on customer queries.”
  • Personalised Banking Experiences: “Scotiabank is using Gemini and Vertex AI to create a more personal and predictive banking experience for its clients…including powering its award winning chatbot.”
  • Increased Efficiency: “Five Sigma created an AI engine which frees up human claims handlers to focus on areas where a human touch is valuable, like complex decision-making and empathic customer service. This has led to an 80% reduction in errors, a 25% increase in adjustor’s productivity, and a 10% reduction in claims cycle processing time.”
  • Public Sector & Nonprofits:Improved Accessibility: “The Minnesota Division of Driver and Vehicle Services helps non-English speakers get licenses and other services with two-way, real-time translation.”
  • Enhanced Citizen Engagement: “Sullivan County, New York, is utilizing gen AI to enhance citizen interactions…the bot empowers residents with increased transparency and direct communication.”
  • Streamlined Services: “mRelief has built an SMS-accessible AI chatbot to simplify the application process for the SNAP food assistance program in the U.S.”
  • Manufacturing, Industrial & Electronics:AI-Powered Devices: “Motorola’s Moto AI leverages Gemini and Imagen to help smartphone users unlock new levels of productivity, creativity, and enjoyment…”
  • Optimised Processes: “Toyota implemented an AI platform using Google Cloud’s AI infrastructure to enable factory workers to develop and deploy machine learning models. This led to a reduction of over 10,000 man-hours per year and increased efficiency and productivity.”
  • Enhanced Sustainability: “Bosch SDS…reduced energy costs by 12%, improved indoor comfort, and better usage of renewable energy.”
  • Media, Marketing & Gaming:Personalised Content Creation: “Globo…is using Google Cloud AI to hyper-personalize content for its streaming users.”
  • Enhanced Advertising ROI: “Dataïads helps brands maximise the ROI of their ad spend by increasing conversion rates and average order value.”
  • Improved Content Generation: “Warner Bros. Discovery built an AI captioning tool with Vertex AI, delivering a 50% reduction in overall costs and an 80% reduction in the time it takes to manually caption a file.”
  • Hospitality & Travel:AI Travel Assistants: “Alaska Airlines is developing natural language search, providing travellers with a conversational experience powered by AI that’s akin to interacting with a knowledgeable travel agent.”
  • Personalised Travel Planning: “Hotelplan Suisse built a chatbot trained on the business’s travel expertise to answer customer inquiries in real-time.”
  • Business & Professional Services:Improved Recruitment: “Allegis Group…partnered with TEKsystems to implement AI models to streamline its recruitment process, including automating tasks such as updating candidate profiles, generating job descriptions, and analysing recruiter-candidate interactions.”
  • Internal Knowledge Management: “Cintas is using Vertex AI Search to develop an internal knowledge centre for customer service and sales teams to easily find key information.”
  • Technology:AI-powered Software Development: “Cognizant uses Gemini and Vertex AI to assist in software development, improving code quality and developer productivity.”
  • Enhanced Security: “Apex Fintech is using Gemini in Security to accelerate the writing of complex threat detections from hours to a matter of seconds.”

4. Impact and Benefits

The use cases demonstrate a consistent pattern of significant positive impacts:

  • Increased Efficiency and Productivity: Numerous examples highlight time savings and efficiency gains through AI-powered automation and task simplification.
  • Cost Reduction: AI-driven solutions have reduced costs in areas like customer service, content creation, and energy consumption.
  • Enhanced Customer Satisfaction: AI-powered personalisation and faster issue resolution have resulted in improved customer experiences.
  • Faster Time-to-Market: AI enables rapid innovation and product development by streamlining processes and accelerating data analysis.
  • Better Decision-Making: AI provides insights for more informed strategic decisions, leading to improved outcomes.

5. Conclusion

This analysis shows that gen AI is rapidly transforming how organisations operate and engage with their customers. The use cases are not isolated experiments but represent a broader movement toward embedding AI into core business processes. The breadth of applications across industries suggests a widespread understanding of the transformative potential of AI and a strong push towards adoption. Organisations that embrace these technologies are likely to gain a significant competitive advantage, both in terms of operational efficiency and their ability to deliver exceptional customer experiences. The emphasis on responsible and ethical application of AI is also a positive sign, indicating a balanced approach to technological advancement.

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This briefing should provide a good overview of the provided document. Let me know if you have any further questions or require additional details!

Source: https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders

AI innovations in December 2024


A Summary of the Leading AI Models by Late 2024

A Summary of the Leading AI Models by Late 2024

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A Summary of the Leading AI Models by Late 2024.

By late 2024, AI development has reached unprecedented heights, offering advanced models capable of handling a broad spectrum of tasks—from coding and creative writing to image generation and robotics. Each technology excels in distinct areas, and costs can vary dramatically. Below is a comprehensive overview to guide you through the most prominent models available today, along with noteworthy free and open-source alternatives.

Listen at https://podcasts.apple.com/ca/podcast/a-summary-of-the-leading-ai-models-by-late-2024/id1684415169?i=1000681975288

A Summary of the Leading AI Models by Late 2024
A Summary of the Leading AI Models by Late 2024

1. Overall Best Models for Raw Intelligence

  • Top Performer – o1-Pro
    • For those needing the absolute cutting edge in AI reasoning and intelligence, o1-Pro is second to none. Its performance in complex tasks and robust coding abilities place it at the forefront of the AI landscape.
    • Key Caveat: The price point is high, making it less accessible for general users.
  • Best Public Option – o1
    • Positioned just below o1-Pro, o1 delivers world-class performance at a slightly lower cost. In coding challenges or creative tasks, it consistently outperforms or matches competitors.
    • Competition: Models like 🍌 Claude-3.5-Sonnet-20241022 can surpass o1 in very specific coding scenarios, but overall, o1 remains a top choice.

2. Budget-Friendly (or Free) Alternative

  • Gemini-1206 in AI Studio
    • Standout Feature: Technically free and nearly unlimited usage. Gemini-1206 is lauded for its intelligence and minimal censorship (when fine-tuned properly).
    • Who Should Use It: Perfect for anyone prioritizing cost-effectiveness, creative tasks, or wanting to avoid heavy content filters.
  • Best for Creative Writing – GPT-4o-2024-11-20
    • Users focused primarily on creative narratives and expressive writing will find GPT-4o-2024-11-20 the leading solution. It consistently produces richer, more engaging text.

3. Music Generation

  • Champion – Suno V4
    • Known for its lifelike music synthesis, it outshines the competition in both sound fidelity and genre versatility.
    • Runner-Up: Udio 1.5 is a strong choice for specialized vocals or voice-based compositions.

4. Image Generation

  • Top Tier – FLUX1.1[Pro] Ultra
  • Open-Source Standout – Pixel Wave
    • A fine-tuned variant of FLUX.1[Dev], Pixel Wave often excels in specific art styles and custom aesthetics, making it an excellent alternative for those who prefer open-source solutions.

5. Speech Generation

  • Best Overall – gpt-4o-audio-preview-2024-12-17 (GPT-4o Advanced Voice Mode)

6. Video Generation

  • Current Leader – Kling 1.6
  • Runner-Up – Sora Turbo
  • Forthcoming Disruptor – Google’s Veo 2
    • Expected to dominate with advanced video synthesis capabilities upon release.

7. 3D Generation

  • Leading Option – Tripo V2
  • Open-Source – Microsoft TRELLIS

8. Search Engines

  • Best All-Around – Perplexity Pro

9. Humanoid Robots

Models IQ  as of December 2024

Below is a hypothetical table of equivalent IQs for each top model discussed. These figures should be viewed as illustrative proxies rather than scientifically validated scores, since AI systems do not undergo standardized IQ tests designed for humans.

Model Hypothetical IQ Category / Purpose Key Strengths
🍏 o1-Pro 165+ Raw Intelligence Top-tier reasoning, excels at complex tasks and coding
🍎 o1 ~160 High-End Public Model Near-Pro performance, more affordable than o1-Pro
🍇 Gemini-1206 in AI Studio ~150 Free, Minimal Censorship Unlimited usage, excellent for cost-conscious users
🍊 GPT-4o-2024-11-20 ~155 Creative Writing Generates detailed, imaginative narratives
🍌 Claude-3.5-Sonnet-20241022 ~158 Coding & Reasoning Exceptional code proficiency in niche tasks
🎵 Suno V4 N/A* Music Generation High-fidelity, multi-genre sound creation
🎶 Udio 1.5 N/A* Music & Vocals Specialized in realistic vocal production
🖼️ FLUX1.1ProPro Ultra N/A* Image Generation Superior photo-realistic and stylized outputs
🎨 Pixel Wave N/A* Open-Source Image Gen Excels in art styles and custom aesthetic experiments
🔊 gpt-4o-audio-preview-2024-12-17 ~145** Speech Synthesis Ultra-realistic voice output
🗣️ Gemini Live (Real-Time Voice) ~140** Speech & Vision Free option, integrates voice with visual context
🎥 Kling 1.6 N/A* Video Generation High-quality rendering for diverse animations
📹 Sora Turbo N/A* Video Generation Strong in niche motion sequences
🍐 Google’s Veo 2 (Upcoming) N/A* Next-Gen Video Expected to disrupt the market upon release
🏗️ Tripo V2 N/A* 3D Modeling Advanced industrial design capabilities
🏛️ Microsoft TRELLIS N/A* Open-Source 3D Community-driven, highly flexible 3D creation
🔎 Perplexity Pro N/A* AI-Powered Search Offers precise, context-rich query results
🍒 DeepSeek Search N/A* Free Search Alternative Competitive with Perplexity in many scenarios
🍉 SearchGPT N/A* Free Search AI Solid AI-driven search interface
🤖 Figure 02 N/A* Humanoid Robotics Advanced bipedal locomotion with robust AI
🏷️ Unitree G1 N/A* Budget Humanoid Lower-cost solution for basic humanoid tasks
🆕 1X Neo N/A* Emerging Robotics Innovative design, promising future potential

Notes:

  • Models that focus on tasks other than language or textual reasoning (e.g., music, image, or video generation) are assigned “N/A” because an IQ-based metric doesn’t directly apply.
  • Speech models are assigned approximate IQ scores when they also exhibit strong language reasoning capabilities in addition to voice synthesis.
  • These IQ estimates are purely illustrative. No universally accepted IQ test for AI systems exists; these numbers reflect approximate or relative performance in language, reasoning, and problem-solving tasks.

Conclusion

By late 2024, AI models and robotics have reached remarkable sophistication. The best choice hinges on your budget, specific use cases, and willingness to invest in premium or open-source solutions. o1-Pro and o1 dominate in raw intelligence, while Gemini-1206 remains the unrivaled free option. For creative pursuits, GPT-4o-2024-11-20 shines in writing, Suno V4 leads in music generation, FLUX1.1[Pro] Ultra rules in images, and Kling 1.6 is the go-to for video content (with Google’s Veo 2 poised to disrupt). Meanwhile, gpt-4o-audio-preview-2024-12-17 excels in speech synthesis, and Tripo V2 stands out for 3D generation. For searching the web, Perplexity Pro is unmatched, though DeepSeek Search is a commendable free substitute. In the realm of humanoid robotics, Figure 02 sets the gold standard, flanked by budget-friendly and emerging alternatives.

See also

AI innovations in December 2024

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How to Select the Right LLM for Your Generative AI Use Case


How to Select the Right LLM for Your Generative AI Use Case

How to Select the Right LLM for Your Generative AI Use Case

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How to Select the Right LLM for Your Generative AI Use Case.

Choosing the right Large Language Model (LLM) for your Generative AI application can be daunting. With numerous options available—OpenAI’s GPT, Meta’s LLaMA, Google’s Gemini, Hugging Face models, and others—it’s crucial to evaluate your options carefully. A poor choice can lead to scalability issues, poor performance, or excessive operational costs.

In this blog and podcast, we’ll break down the key factors to consider when selecting an LLM, as highlighted in the accompanying visual. These factors span Technical Specifications, Performance Metrics, and Operational Considerations. By balancing these dimensions, you can make an informed decision tailored to your use case and resources.

LLM Key factors: How to Select the Right LLM for Your Generative AI Use Case
How to Select the Right LLM for Your Generative AI Use Case

1. Technical Specifications

Parameter Size

  • Definition: Parameter size indicates the number of weights and connections within the model. Larger models like GPT-4 tend to produce more nuanced, high-quality responses.
  • Trade-off: Larger models require more compute power, which increases cost and slows down inference.
  • When It Matters: Use larger models for complex tasks requiring deep reasoning or creativity. For simpler tasks, smaller models are more cost-efficient.

Context Window

  • Definition: The context window defines how much text (input and output combined) an LLM can process in a single session.
  • Trade-off: A larger context window is resource-intensive but vital for handling longer inputs, such as multi-page documents or conversations.
  • When It Matters: Essential for use cases like summarization, chatbots, or code generation where context continuity is critical.

Architecture

  • Definition: The model’s architecture (e.g., transformer-based models) influences its ability to learn patterns and relationships in data.
  • Considerations: Evaluate whether the LLM supports fine-tuning or prompt engineering to adapt to your domain.

Training Data

  • Definition: The quality and diversity of training data impact the LLM’s understanding of language, accuracy, and generalization.
  • Considerations: If domain-specific accuracy is important (e.g., legal or medical fields), consider models pre-trained or fine-tuned on domain-specific data.

2. Performance Metrics

Inference Speed

  • Why It Matters: Fast inference is critical for real-time applications like chatbots, virtual assistants, or live translations.
  • Trade-off: High speed often requires more optimized models or hardware acceleration (GPUs/TPUs).

Accuracy

  • Definition: Accuracy refers to the correctness and relevance of generated outputs.
  • Considerations: Use benchmarks to evaluate the LLM’s performance on your use case. Accuracy is non-negotiable for applications like financial summaries or medical AI.

Reliability & Consistency

  • Why It Matters: LLMs need to deliver stable performance under different tasks or data conditions.
  • Considerations: Inconsistent models can produce unpredictable results, making them unreliable for production.

3. Operational Considerations

Cost

  • Definition: Operational cost includes both training and inference expenses. Larger, more complex models require more computational power.
  • Strategies:
    • Use smaller models for lightweight tasks.
    • Optimize inference using quantization or distillation.
    • Consider pay-as-you-go LLM APIs for cost control.

Scalability

  • Why It Matters: Scalability determines whether your model can handle increasing workloads as user demands grow.
  • Considerations:
    • For large-scale deployments, consider the infrastructure needed for distributed inference.
    • Use efficient data platforms like SingleStore to manage growing workloads, particularly for vectorized data.

4. Making Trade-Offs

Balancing these factors requires trade-offs. For example:

  • Accuracy vs. Cost: A smaller model is cheaper but might lack precision for complex tasks.
  • Speed vs. Context Window: Real-time applications may sacrifice context length for faster response times.
  • Scalability vs. Performance: A scalable model must handle increasing workloads while maintaining consistent performance.

The ideal LLM selection depends on your specific use case, whether it’s a high-accuracy medical AI tool, a real-time chatbot, or a scalable content generation system.


5. Role of a Robust Data Platform

Selecting an LLM is only part of the equation. To maximize its potential, you need a robust data platform to support AI applications. Platforms like SingleStore handle:

  • High-performance vector data for embeddings.
  • All data types to facilitate seamless integration with LLMs.
  • Scalability to ensure your system grows effortlessly with increasing demand.

This integrated approach allows you to fully leverage the LLM’s capabilities while ensuring reliable and efficient operations.

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Conclusion

Selecting the right LLM for your Generative AI use case requires a holistic evaluation of technical specifications, performance metrics, and operational considerations. Each factor—from parameter size and inference speed to cost and scalability—must be weighed based on your use case, resources, and performance goals.

By understanding the trade-offs and ensuring a robust data infrastructure, you can unlock the full potential of LLMs to build smarter, more efficient AI solutions. Tools like SingleStore offer the scalability and vector data management necessary to support these AI-driven workflows seamlessly.

References:

https://ai.plainenglish.io/5-strategies-to-enhance-your-llms-performance-2e00bfa04462

🔍 Top LLM Benchmarks for comparing model performance.

💡 LLM Optimization Techniques to reduce costs and enhance speed.

🛠️ Building Scalable AI Infrastructure with modern data platforms.

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Roadmap to Developing AI Agent: A Comprehensive Guide

AI innovations in December 2024


Roadmap to Developing AI Agent: A Comprehensive Guide

Ai Agents Development Roadmap

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Roadmap to Developing AI Agent: A Comprehensive Guide.

AI Agents Development Roadmap.

Developing AI agents that perform tasks effectively, adapt to changing contexts, and integrate seamlessly into workflows requires a structured approach. This article outlines a clear, step-by-step roadmap for building robust AI agents, combining foundational knowledge with advanced concepts.

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Roadmap to Developing AI Agents: A Comprehensive Guide
Roadmap to Developing AI Agents: A Comprehensive Guide

Step 1: Problem & Data Definition

Before diving into development, it is crucial to define the problem clearly and prepare the data effectively:

  1. Objective Definition:

Clearly define the purpose and scope of your AI agent. What tasks should it perform? What outcomes are expected? Establishing a precise objective ensures alignment between development efforts and goals.

2. Data Collection:

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Gather diverse, task-specific datasets to train and evaluate the AI agent. These datasets should be comprehensive and representative of real-world scenarios.

3. Data Cleaning:

Remove noise, irrelevant, or poor-quality data to ensure accuracy. This step ensures the model can process data effectively without being hindered by inconsistencies.

4. Feature Engineering:

For custom or fine-tuned models, identify and prepare key features relevant to the agent’s domain. This step enhances the agent’s ability to solve specific tasks.

5. Knowledge Base Setup:

Build a repository of task-relevant knowledge, such as semantic search tools or graph databases. This knowledge base serves as a foundational resource for the agent’s decision-making.

Step 2: Model Development & Integration

The second step involves selecting and fine-tuning models, training behaviors, and integrating tools to create a cohesive AI system:

  1. Model Selection:

Choose a suitable AI model that aligns with the agent’s goals. Options include pre-trained models or custom-built solutions tailored for specific needs.

2. Fine-Tuning:

Enhance the model’s capabilities by fine-tuning it on domain-specific tasks. Fine-tuning ensures that the agent delivers higher performance for the intended use case.

3. Behavior Training:

Incorporate reinforcement learning to teach the agent task-specific behaviors, improving adaptability and decision-making.

4. Memory Management:

Equip the agent with short-term, long-term, and episodic memory capabilities. These enable the agent to retain context across interactions and adapt to dynamic requirements.

5. Integration with Tools & APIs:

Ensure seamless interaction between the AI agent and external tools or systems via APIs. This step often involves automating workflows or fetching data in real time.

6. Multi-Agent Collaboration:

Design agents to communicate and collaborate effectively when multiple agents are deployed. Use defined protocols to streamline interactions between agents.

Step 3: Validation and Optimization

Once the model is trained and integrated, rigorous validation and optimization are needed to ensure performance:

  1. Performance Testing:

Evaluate the agent’s speed, accuracy, and resource efficiency. This step helps identify areas for improvement.

2. Tool Validation:

Test all external tools or APIs to ensure they work as intended when interacting with the agent.

3. Multimodal Integration:

Incorporate different modalities (e.g., text, vision, or speech) to create richer and more dynamic interactions with users.

4. Resource Management:

Optimize computational costs to achieve maximum efficiency without sacrificing performance.

Step 4: Learning & Updates

AI agents must evolve over time to remain effective. This step focuses on feedback, monitoring, and continuous improvement:

  1. Feedback Loops:

Collect and analyze user feedback to identify weaknesses in the agent’s performance. User input is invaluable for iterative development.

2. Monitoring and Evaluation:

Regularly track key performance metrics, such as response accuracy and time, to assess the agent’s reliability and effectiveness.

3. Continuous Fine-Tuning:

Adapt the model to new data or changing requirements by continuously fine-tuning it. This ensures the agent remains relevant and up to date.

4. Failure Recovery:

Build mechanisms to recover from failures. Identify common failure points and design systems that minimize downtime or incorrect outputs.

Foundational Learning Path for AI Agents

Developers embarking on this journey can benefit from a structured learning path to grasp the core concepts of AI agents:

Level 1: Basics of Generative AI and Retrieval-Augmented Generation (RAG)

  1. Generative AI (GenAI) Introduction:

Understand the basics of generative models, their applications, and ethical considerations, including potential biases.

2. LLM Foundations:

Learn about transformer architectures, attention mechanisms, and tokenization.

3. Prompt Engineering:

Master prompting techniques such as zero-shot, few-shot, and chain-of-thought prompting.

4. Data Processing:

Explore preprocessing methods like tokenization and normalization for effective data handling.

5. API Wrappers:

Understand API integration for automating tasks using REST and GraphQL.

6. Essentials of RAG:

Learn about embedding-based search using vector databases like ChromaDB and Milvus.

Level 2: AI Agent-Focused Learning

  1. Introduction to AI Agents:

Explore agent-environment interactions and agentic frameworks like LangChain.

2. Agent Workflows:

Learn to orchestrate tasks and integrate external tools while implementing error recovery mechanisms.

3. Agent Memory:

Develop systems for short-term, long-term, and episodic memory storage and retrieval.

4. Evaluation:

Measure success metrics, evaluate decision-making, and benchmark performance across datasets.

5. Multi-Agent Collaboration:

Study communication protocols and dependencies to enable seamless collaboration between multiple agents.

Conclusion

Developing AI agents requires careful planning, iterative improvements, and mastery of foundational concepts. By following this roadmap—starting from problem definition to ongoing updates—you can create intelligent, adaptable, and efficient agents that excel in real-world scenarios. With structured learning paths and tools, even beginners can build agents capable of tackling complex challenges.

AI Consultation:

Want to harness the power of AI for your business? Etienne Noumen, the creator of this podcast “AI Unraveled,” is also a senior software engineer and AI consultant. He helps organizations across industries like yours (Oil and Gas, Medicine, Education, Amateur Sport, Finance, etc. ) leverage AI through custom training, integrations, mobile apps, or ongoing advisory services. Whether you’re new to AI or need a specialized solution, Etienne can bridge the gap between technology and results. DM here or Email at info@djamgatech.com or Visit djamgatech.com to learn more and receive a personalized AI strategy for your business.

AI and Machine Learning For Dummies Pro

u/enoumen - Today in AI: 💰OpenAI Unveils ChatGPT Pro Subscription at $200 Per Month 🌐Microsoft's Copilot Enhances Browsing with Real-Time AI Assistance ⚖️Trump Appoints Ex-PayPal COO David Sacks as 'AI and Crypto Czar' 🔍Google Search Set for Transformative Overhaul by 2025 because of AI 📈ChatGPT…

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AI innovations in December 2024


How to Create a Specialized LLM That Understands Your Custom Data

How to Create a Specialized LLM That Understands Your Custom Data

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Pass the 2024 AWS Cloud Practitioner CCP CLF-C02 Certification with flying colors Ace the 2024 AWS Solutions Architect Associate SAA-C03 Exam with Confidence

How to Create a Specialized LLM That Understands Your Custom Data.

Creating a specialized Large Language Model (LLM) tailored to understand your custom data requires a strategic approach. This guide outlines four key techniques for building a specialized LLM, ranked from the simplest to the most complex and resource-intensive.

Listen at https://podcasts.apple.com/us/podcast/how-to-create-a-specialized-llm-that-understands/id1684415169?i=1000680093688

🚀 How to Create a Specialized LLM That Understands Your Custom Data?
🚀 How to Create a Specialized LLM That Understands Your Custom Data?

1. Prompting: The Simplest Approach

Prompting is the foundational method for leveraging LLMs. It involves crafting input instructions to guide the model’s output.

Steps to Start:

1. Write a basic prompt that describes the task.

2. Experiment with few-shot exemplars by including examples in your prompt.

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3. Refine instructions for clarity and precision.

4. Explore advanced techniques such as chain-of-thought prompting, where intermediate reasoning steps are explicitly encouraged.

Advantages:

  • Minimal setup and computational requirements.
  • Quick iteration and experimentation.

Limitations:

• May not fully address domain-specific tasks.

• Higher risk of hallucinations in responses.

2. Retrieval-Augmented Generation (RAG): Adding Context to Prompts

RAG enhances prompting by dynamically retrieving relevant domain-specific data to include in the model’s input.

How It Works:

  1. Prepare the Data: Segment your custom data into manageable chunks.
  2. Index the Data: Use tools like reverse indexes or vector databases (e.g., Pinecone, Weaviate) to store and query data.
  3. Retrieve and Prompt: During inference, retrieve relevant data chunks and add them to the prompt for context.

Advantages:

  • Reduces hallucinations by grounding responses in real data.
  • Scalable to large datasets using modern vector search technologies.

Limitations:

  • Requires an additional data pipeline for indexing and retrieval.
  • Performance depends on the quality of retrieved data.

3. LoRA: Efficient Fine-Tuning

Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning method that minimizes computational overhead while achieving domain specialization.

How LoRA Works:

  1. Decomposes weight updates during fine-tuning into a low-rank format.
  2. Updates only a small fraction of model parameters, leaving the majority unchanged.
  3. Integrates updated weights seamlessly into the pre-trained model.

Advantages:

  • Drastically reduces memory usage and computational costs.
  • Comparable performance to full fine-tuning with no additional inference latency.

Use Case:

Ideal for adapting large models to specific tasks or domains without requiring extensive resources.

4. Full Fine-Tuning: The Comprehensive Solution

When other methods fall short, full fine-tuning offers complete control by retraining the entire model on domain-specific data.

Steps for Full Fine-Tuning:

  1. Curate the Dataset: Assemble a large, high-quality corpus relevant to your domain.
  2. Prepare the Model: Use the pre-trained model as a starting point.
  3. Train the Model: Further train the model using next-token prediction (similar to initial pretraining).

Advantages:

  • Best for deeply embedding domain-specific knowledge.
  • Customizes the model’s behavior comprehensively.

Limitations:

  • Requires significant computational resources and expertise.
  • Risk of overfitting if the dataset is too narrow or limited.

Choosing the Right Technique

  • Start Simple: Attempt prompting and RAG first for minimal effort and cost.
  • Scale Gradually: Move to LoRA if more domain adaptation is required.
  • Go All-In: Reserve full fine-tuning for applications requiring complete control over the model’s behavior.

Tools and Technologies to Explore

  • Vector Databases: Pinecone, Weaviate, Redis.
  • Fine-Tuning Frameworks: Hugging Face, PyTorch.
  • Data Management: SingleStore, embeddings for indexing.

By understanding these techniques and their trade-offs, you can effectively create a specialized LLM tailored to your custom data needs.

See Also

🌟 Prompt Engineering Techniques

📚 RAG Implementation Guide

🚀 LoRA Fine-Tuning Frameworks

💻 Full Fine-Tuning Best Practices

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AI Unraveled: Demystifying Frequently Asked Questions on Artificial Intelligence

 

AI innovations in December 2024


How to develop AI-powered apps effectively

How to develop AI-powered apps effectively?

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How to develop AI-powered apps effectively.

Artificial Intelligence (AI) is rapidly transforming the tech industry, with many organizations looking to leverage AI-powered apps to gain a competitive edge. However, building an AI solution requires careful planning, the right tools, and a strategic approach to ensure that the time and resources invested are worthwhile. In this blog, we’ll explore the best practices for developing AI-powered applications effectively, focusing on maximizing productivity while avoiding common pitfalls.

Listen at https://podcasts.apple.com/ca/podcast/how-to-develop-ai-powered-apps-effectively/id1684415169?i=1000678217564

How to develop AI-powered apps effectively
How to develop AI-powered apps effectively
How to develop AI-powered apps effectively: Evaluating Pinecone Configuration
How to develop AI-powered apps effectively: Evaluating Pinecone Configuration

Start Small: Eating the Elephant One Bite at a Time

The process of building an AI-powered app can seem daunting. Whether you’re creating a document processor, a chatbot, or a specialized content creation tool, it’s important to break down the development process into manageable tasks. Think of it as eating an elephant—you take it one bite at a time.

One critical mistake many developers make is jumping straight into advanced AI tasks, like training or fine-tuning models. These are powerful tools, but they are time-consuming and require significant resources. Before you get there, it’s important to consider simpler alternatives that may deliver what you need.

The Power of Prompt Engineering

Prompt engineering is often underestimated. Many developers will simply enter a generic request, like “write an article about gaining muscle,” and expect magic. However, understanding that a language model doesn’t “think” or “reason” like humans is key. It predicts the next word based on its training data, meaning that the quality of the output depends largely on the input it receives.

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How to develop AI-powered apps effectively: 12 Prompt Engineering Techniques.
How to develop AI-powered apps effectively: 12 Prompt Engineering Techniques.

Benefit #1: is that it can achieve similar results to fine-tuning and training your own model… But with a lot less work and resources.

Benefit #2: is that you can feed your LLM from an API with “live” data, not just pre-existent data. Maybe you’re trying to ask the LLM about road traffic to the airport, data it doesn’t have. So you give it access to an API.

If you’ve ever used Perplexity.ai or ChatGPT with web search, that’s what RAG is. RunLLM is what RAG is.

It’s pretty neat and one of the hot things in the AI world right now.

To get better results, it’s essential to carefully craft your prompts, tailoring the input to elicit the desired output. Here are some common techniques used in prompt engineering:

Assigning Roles to the LLM

A powerful strategy is to assign a specific role to the language model. For example, instead of simply asking for an article about gaining muscle, you could say, “Write an article about how to gain muscle as if you were Mike Mentzer, an expert bodybuilder.” This slight tweak can significantly improve the relevance and quality of the output by leveraging the persona of a knowledgeable source.

Alternatively, you can describe a fictional expert persona to get more tailored responses. For example, “Write as if you were an ex-powerlifter and ex-wrestler with multiple Olympic gold medals” can add depth and context to the language model’s output.

N-Shot Learning

Another technique to improve the AI’s responses is to use N-shot learning. This involves providing a few examples to demonstrate the kind of output you want. For instance, if you’re trying to write articles in a specific voice, give the model a few reference articles to learn from. This enables the AI to generalize from the examples and emulate the desired style more accurately.

If you are building an app that needs precise output (e.g., summarizing medical studies), it’s crucial to use examples that closely reflect your use case. By doing so, you help the AI learn the nuances it needs to produce high-quality, contextual responses.

Structured Inputs and Outputs

Providing structured data helps the AI interpret information better. Different formats can influence how effectively a model can parse the data. For instance, AI models often have trouble with PDF files but perform better with Markdown.

An example of effective structured input is XML. Consider this input:

<description>
The SmartHome Mini is a compact smart home assistant available in black or white for only $49.99. At just 5 inches wide, it lets you control lights, thermostats, and other connected devices via voice or app—no matter where you place it in your home.
</description>

If you ask the AI to extract the <name>, <size>, <price>, and <color> from this description, the structured context makes it easy for the AI to parse and understand what each element represents. Structured inputs are particularly helpful for AI-powered apps that rely on extracting key data from a well-defined source.

Chain-of-Thought Reasoning

Chain-of-thought is another powerful concept for improving AI performance. By explicitly instructing the model to “think step by step,” you can often get more comprehensive and accurate responses.

Tree of Thoughts Improves AI Reasoning and Logic By Nine Times ...

Language Models Perform Reasoning via Chain of Thought – Google ...

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For example, in a chatbot aimed at providing medical advice, you might use the following system prompt:

You are an expert AI assistant specializing in testosterone, TRT, and sports medicine research. Follow these guidelines:

1. Ask clarifying questions.
2. Confirm understanding of the user's question.
3. Provide a clear, direct answer.
4. Support with specific evidence.
5. End with relevant caveats or considerations.

SYSTEM_PROMPT = """You are an expert AI assistant specializing in 
testosterone, TRT, and sports medicine research. Follow these guidelines:

1. Response Structure:
- Ask clarifying questions
- Confirm understanding of user's question
- Provide a clear, direct answer
- Follow with supporting evidence
- End with relevant caveats or considerations

2. Source Integration:
- Cite specific studies when making claims
- Indicate the strength of evidence (e.g., meta-analysis vs. single study)
- Highlight any conflicting findings

3. Communication Style:
- Use precise medical terminology but explain complex concepts
- Be direct and clear about risks and benefits
- Avoid hedging language unless uncertainty is scientifically warranted

4. Follow-up:
- Identify gaps in the user's question that might need clarification
- Suggest related topics the user might want to explore
- Point out if more recent research might be available

Remember: Users are seeking expert knowledge. Focus on accuracy and clarity 
rather than general medical disclaimers which the users are already aware of."""

Incorporating chain-of-thought prompts, particularly in complex scenarios, can result in richer, more informative output. The downside, of course, is that this may increase latency and token usage, but the improved accuracy can be well worth it.

Breaking Down Large Prompts

For complex, multi-step processes, it’s often effective to split a large prompt into multiple smaller prompts. This approach helps the model focus on each specific part of the task, leading to better overall performance. For example, tools like Perplexity.ai leverage this strategy effectively, and you can adopt the same approach in your AI projects.

Utilizing Relevant Resources: Retrieval Augmented Generation (RAG)

Another method to enhance AI-powered apps is to provide the model with external data. This is where Retrieval Augmented Generation (RAG) comes into play. With RAG, you can inject additional, up-to-date information that the model wasn’t trained on. For example, you might want the AI to help with a new SDK launched last week—if the model was trained six months ago, that information would be missing. Using RAG, you can provide the necessary documentation manually.

What is Retrieval Augmented Generation? An Essential Guide

RAG has several core advantages:

  1. Cost-Effectiveness: RAG can achieve similar results to fine-tuning without the need for intensive training or resource usage.
  2. Real-Time Integration: You can feed the model live data via an API, which can be highly useful for tasks like checking current traffic or real-time stock updates.

RAG-based implementations are commonly seen in tools like Perplexity.ai and ChatGPT’s web search. These use strategies such as vector embeddings, hybrid search, and semantic chunking to enhance the performance of the language model with minimal manual input.

What Is Retrieval-Augmented Generation? | Definition from TechTarget

Conclusion

Building effective AI-powered apps doesn’t have to be overwhelming. By using foundational techniques like prompt engineering, structured inputs, chain-of-thought, and Retrieval Augmented Generation (RAG), you can significantly enhance the performance of your AI applications. It’s all about strategically employing the tools available—starting with simpler techniques and moving to more advanced methods as needed.

Whether you’re creating a simple chatbot or a complex automation tool, these best practices can help you develop AI apps that deliver value, are efficient, and make the most of the available technology.

References: 

1- Reddit

2- AI and Machine Learning For Dummies

AI Consultation:

Want to harness the power of AI for your business? Etienne Noumen, the creator of  “AI Unraveled,” is also a senior software engineer and AI consultant. He helps organizations across industries like yours (mention specific industries relevant to your podcast audience) leverage AI through custom training, integrations, mobile apps, or ongoing advisory services. Whether you’re new to AI or need a specialized solution, Etienne can bridge the gap between technology and results. Contact Etienne here to learn more and receive a personalized AI strategy for your business.

💪 AI and Machine Learning For Dummies

AI and Machine Learning For Dummies
AI and Machine Learning For Dummies

Master AI Machine Learning PRO
Elevate Your Career with AI & Machine Learning For Dummies PRO
Ready to accelerate your career in the fast-growing fields of AI and machine learning? Our app offers user-friendly tutorials and interactive exercises designed to boost your skills and make you stand out to employers. Whether you're aiming for a promotion or searching for a better job, AI & Machine Learning For Dummies PRO is your gateway to success. Start mastering the technologies shaping the future—download now and take the next step in your professional journey!

Download on the App Store

Download the AI & Machine Learning For Dummies PRO App:
iOS - Android
Our AI and Machine Learning For Dummies PRO App can help you Ace the following AI and Machine Learning certifications:

Djamgatech has launched a new educational app on the Apple App Store, aimed at simplifying AI and machine learning for beginners.

It is a mobile App that can help anyone Master AI & Machine Learning on the phone!

Download “AI and Machine Learning For Dummies ” FROM APPLE APP STORE and conquer any skill level with interactive quizzes, certification exams, & animated concept maps in:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Generative AI
  • LLMs
  • NLP
  • xAI
  • Data Science
  • AI and ML Optimization
  • AI Ethics & Bias ⚖️

& more! ➡️ App Store Link: https://apps.apple.com/ca/app/ai-machine-learning-4-dummies/id1611593573

AI Innovations in November 2024