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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\\u2019s AlphaGo, or its more recent descendant MuZero, need far more experience than humans do to master a given game.
\\nSo when someone develops an algorithm that can reach human-level performance at anything as fast as a human can, it\\u2019s a big deal. And that\\u2019s 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\\u2019s a tremendous breakthrough in sample-efficiency, and a major milestone in the development of more general and flexible AI systems.
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\\nIntro music:
\\n\\u279e Artist: Ron Gelinas
\\n\\u279e Track Title: Daybreak Chill Blend (original mix)
\\n\\u279e Link to Track: https://youtu.be/d8Y2sKIgFWc
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\\nChapters:
\\n- 0:00 Intro
\\n- 1:50 Yang\\u2019s background
\\n- 6:00 MuZero\\u2019s activity
\\n- 13:25 MuZero to EfficiantZero
\\n- 19:00 Sample efficiency comparison
\\n- 23:40 Leveraging algorithmic tweaks
\\n- 27:10 Importance of evolution to human brains and AI systems
\\n- 35:10 Human-level sample efficiency
\\n- 38:28 Existential risk from AI in China
\\n- 47:30 Evolution and language
\\n- 49:40 Wrap-up
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