AIGuys Digest | Jan 2025

🌟 Welcome to the AIGuys Digest Newsletter, where we cover State-of-the-Art AI breakthroughs and all the major AI news🚀. Don’t forget to check my new book on AI, it covers a lot of AI optimizations and hands-on code:

Ultimate Neural Network Programming with Python

🔍 Inside this Issue:

• 🤖 Latest Breakthroughs: This month it’s all about DeepSeek, Agentic Framework, and RAG.
• 🌐 AI Monthly News: Discover how these stories revolutionize industries and impact everyday life.
• 📚 Editor’s Special: This covers the interesting talks, lectures, and articles we came across recently.

Let’s embark on this journey of discovery together! 🚀🤖🌟

Follow me on Twitter and LinkedIn at RealAIGuys and AIGuysEditor.

Latest Breakthroughs

Recently, post-training has emerged as an important component of the full training pipeline. It has been shown to enhance accuracy on reasoning tasks, align with social values, and adapt to user preferences, all while requiring relatively minimal computational resources against pre-training.

In the context of reasoning capabilities, OpenAI’s o1 series models were the first to introduce inference-time scaling by increasing the length of the Chain-of-Thought reasoning process. This approach has significantly improved in various reasoning tasks, such as mathematics, coding, and scientific reasoning.

Several previous works have explored various approaches, including process-based reward models, reinforcement learning, and search algorithms such as Monte Carlo Tree Search and Beam Search. However, none of these methods has achieved general reasoning performance comparable to OpenAI’s o1 series models. So, let’s see what Deepseek has cooked to challenge the leader in reasoning.

DeepSeek R1 Beating OpenAI In Reasoning

2025 is bringing a big change in AI — the rise of agent frameworks. While we’ve made great progress with Large Language Models (LLMs), just making them bigger and training them on more data isn’t giving us major improvements anymore. Even OpenAI’s O3 model shows that we’re hitting the limits of what we can achieve through pre-training alone.

So what’s next? The answer lies in reinforcement learning (RL) and agents — AI systems that can actively think through problems and work towards goals. Instead of just responding to prompts, these systems use RL to learn how to reason and make decisions.

Let’s look at the different frameworks that are making this possible and how they’re changing the way AI works.

Understanding Agentic Framework

Retrieval-augmented generation (RAG) has gained traction as a powerful approach for enhancing language models by integrating external knowledge sources. However, RAG introduces challenges such as retrieval latency, potential errors in document selection, and increased system complexity.

With the advent of large language models (LLMs) featuring significantly extended context windows, cache-augmented generation (CAG) bypasses real-time retrieval. It involves preloading all relevant resources into the LLM’s extended context and caching its runtime parameters, especially when the documents or knowledge for retrieval are limited and manageable.

Don’t Do RAG, It’s Time For CAG

AI Monthly News

DeepSeek’s AI Advancements

Chinese AI startup DeepSeek has released its AI Assistant, utilizing the V3 model, which has quickly become the highest-rated free app on the U.S. iOS App Store. Notably, DeepSeek achieved this with significantly fewer resources than its competitors, training its model with approximately 2,000 GPUs over 55 days at a cost of $5.58 million — about one-tenth of Meta’s recent AI expenditures. This efficiency has led to concerns about the U.S. maintaining its lead in AI development.

Wiki: Source

Financial Impact News: Source

Meta’s Continued Investment in AI

Despite DeepSeek’s rapid progress, Meta remains committed to substantial AI investments. The company plans to allocate hundreds of billions of dollars to AI initiatives, aiming to solidify its market position within the year. Meta’s strategy includes enhancing AI infrastructure, integrating AI into platforms like Facebook and Instagram, and improving ad targeting. CEO Mark Zuckerberg acknowledges DeepSeek’s advancements but emphasizes Meta’s focus on establishing an American standard in open-source AI.

News: Source

Predictions for AI’s Future

Yann LeCun, Meta’s chief AI scientist, predicts a significant AI revolution within the next five years. He emphasizes the need for breakthroughs that enable AI systems to understand and interact with the physical world, which is essential for developing domestic robots and fully autonomous vehicles. LeCun notes that while current AI excels at language manipulation, it lacks comprehension of the physical environment.

News: Source

Editor’s Special

• GRPO, the algorithm behind DeepSeek’s success: Click here
• Gaussian Splatting: Click here
• Understanding Artificial Super Intelligence: Click here

🤝 Join the Conversation: Your thoughts and insights are valuable to us. Share your perspectives, and let’s build a community where knowledge and ideas flow freely. Follow us on Twitter and LinkedIn at RealAIGuys and AIGuysEditor.

Thank you for being part of the AIGuys community. Together, we’re not just observing the AI revolution; we’re part of it. Until next time, keep pushing the boundaries of what’s possible. 🚀🌟

Your AIGuys Digest Team