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.

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Intro music:

➞ Artist: Ron Gelinas

➞ Track Title: Daybreak Chill Blend (original mix)

➞ Link to Track: https://youtu.be/d8Y2sKIgFWc

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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

Brian Sims

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