In this episode, we break down a fascinating new approach that helps AI models think more like humans. Researchers Zhiyuan Li, Hong Liu, Denny Zhou, and Tengyu Ma have discovered that by guiding AI to think step-by-step — a process they call "Chain-of-Thought" (CoT) — it can tackle much tougher tasks like solving puzzles, doing math, and making complex decisions. We’ll explain how this method works and why it could be a game-changer for AI. If you’re curious about how AI can learn to think better, this episode is for you!

Original Paper:
"Chain of Thought Empowers Transformers to Solve Inherently Serial Problems" by Zhiyuan Li, Hong Liu, Denny Zhou, and Tengyu Ma.
Link: https://arxiv.org/abs/2402.12875v3

In the premiere episode of AI Odyssey, we tackle one of the most pressing challenges in artificial intelligence: how can we make large language models smarter and more reliable? Join us as we explore the groundbreaking paper "Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make Your LLMs Use External Data More Wisely", authored by Siyun Zhao, Yuqing Yang, Zilong Wang, Zhiyuan He, Luna K. Qiu, and Lili Qiu from Microsoft Research Asia. This episode, generated with Google's NotebookLM, uncovers how integrating external data can turn powerful AI into true domain experts, minimize hallucinations, and push the limits of what LLMs can achieve. Whether you're curious about the future of AI or a seasoned expert, this episode offers deep insights and practical takeaways.

Don't miss out—tune in for a journey into the evolving intelligence of machines!
Link to the paper: https://arxiv.org/abs/2409.14924v1