Y Combinator

The Key Thing Human Brains Have That AI Is Trying To Learn

In this episode of Decoded, Ankit and Francois walk through the motivation and math behind world models. They cover a number of areas including why sample efficiency is one of the biggest unsolved problems in AI, how deterministic differentiable control and Newtonian physics represent a "perfect world model," and why the action space explosion makes chess tractable but robotics nearly intractable. Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs Transcript: https://ycrootaccess.substack.com/p/world-models-an-intuitive-introduction Chapters: 00:00 — Intro 01:45 — What would perfect efficiency look like? 05:10 — World models in the human brain 09:20 — Control theory & the drone example 14:30 — When physics breaks down 17:45 — Chess, Go & the action space problem 24:10 — Why AlphaGo can't scale 28:00 — Monte Carlo tree search explained 34:00 — Self-Driving: state space is infinite 40:30 — Model-Free vs. Model-Based RL 44:00 — Why robotics is the hardest case 48:20 — World models that actually work 54:10 — JEPA & latent space tricks 59:00 — Open problems remaining 01:04:30 — Does this pass the squint test? 01:08:00 — Outro