In this episode, we dive into AI researcher Noam Brown’s groundbreaking work on reasoning in AI and the development of the o1 model. Brown argues for the power of search and planning over traditional instant-action models, showcasing how these techniques have transformed AI’s performance in complex games like poker and Go. We explore how o1 leverages reinforcement learning to create high-quality chains of thought, solving complex problems across diverse fields like coding, science, and law. Brown’s insights present a bold vision for scaling inference compute and expanding AI’s potential beyond chatbots. Episode Highlights: AI in Games: Poker and Go: How search and planning led to superhuman AI performance in poker and Go. The Revolutionary o1 Model: Explore o1’s use of reinforcement learning to optimise chains of thought for complex reasoning. Performance Highlights: o1’s success in diverse domains, from AIME tests to coding and science. Implications for AI’s Future: The potential to reimagine AI’s role in scientific discovery and technological innovation. A Call to Action: Brown’s vision for prioritising long-term impact in AI research.   Source: YouTube