# 106. Yang Gao - Sample-efficient AI

Page: https://stenobird.com/podcast/towards-data-science/106-yang-gao-sample-efficient-ai
Text version: https://stenobird.com/podcast/towards-data-science/106-yang-gao-sample-efficient-ai.md
Podcast: [Towards Data Science](https://stenobird.com/podcast/towards-data-science)
Published: 2021-12-08T15:26:47+00:00
Episode link: https://podcasters.spotify.com/pod/show/towardsdatascience/episodes/106--Yang-Gao---Sample-efficient-AI-e1baruu
Audio file: https://anchor.fm/s/36b4844/podcast/play/44445086/https%3A%2F%2Fd3ctxlq1ktw2nl.cloudfront.net%2Fstaging%2F2021-11-6%2F1b5c67a0-db93-7622-8ec3-a52fc4bd5392.m4a
Processing state: failed
JSON: https://stenobird.com/v1/public/podcasts/towards-data-science/episodes/106-yang-gao-sample-efficient-ai
Duration seconds: 2993

## Resource

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

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