Historically, AI systems have been slow learners. For example, a computer vision model often needs to see tens of thousands of hand-written digits before it can tell a 1 apart from a 3. Even game-playing AIs like DeepMind’s AlphaGo, or its more recent descendant MuZero, need far more experience than humans do to master a given game. So when someone develops an algorithm that can reach human-level performance at anything as fast as a human can, it’s a big deal. And that’s exactly why I asked Yang Gao to join me on this episode of the podcast. Yang is an AI researcher with affiliations at Berkeley and Tsinghua University, who recently co-authored a paper introducing EfficientZero: a reinforcement learning system that learned to play Atari games at the human-level after just two hours of in-game experience. It’s a tremendous breakthrough in sample-efficiency, and a major milestone in the development of more general and flexible AI systems. ---  Intro music: ➞ Artist: Ron Gelinas ➞ Track Title: Daybreak Chill Blend (original mix) ➞ Link to Track: https://youtu.be/d8Y2sKIgFWc --- Chapters:  - 0:00 Intro - 1:50 Yang’s background - 6:00 MuZero’s activity - 13:25 MuZero to EfficiantZero - 19:00 Sample efficiency comparison - 23:40 Leveraging algorithmic tweaks - 27:10 Importance of evolution to human brains and AI systems - 35:10 Human-level sample efficiency - 38:28 Existential risk from AI in China - 47:30 Evolution and language - 49:40 Wrap-up