Yang Gao's guest appearances

Yang Gao

Towards Data Science

Robot Talk

OwlTail

This week, Claire chatted to Yang Gao from the University of Surrey all  about space robotics and planetary exploration. Yang Gao is Professor of Space Autonomous Systems and Founding Head of the STAR LAB that specializes in robotic sensing, perception, visual guidance, navigation, and control (GNC) and biomimetic mechanisms for industrial applications in extreme environments. She brings over 20 years of research experience in developing robotics and autonomous systems, in which she has been the principal investigator of over 30 inter/nationally teamed projects and involved in real-world mission development.

This week, Claire chatted to Yang Gao from the University of Surrey all about space robotics and planetary exploration. <a href="https://www.surrey.ac.uk/people/yang-gao" rel="nofollow" target="_blank">Yang Gao</a> is Professor of Space Autonomous Systems and Founding Head of the STAR LAB that specializes in robotic sensing, perception, visual guidance, navigation, and control (GNC) and biomimetic mechanisms for industrial applications in extreme environments. She brings over 20 years of research experience in developing robotics and autonomous systems, in which she has been the principal investigator of over 30 inter/nationally teamed projects and involved in real-world mission development.

Episode Thirty-Seven: Yang Gao

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

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: <a href="https://youtu.be/d8Y2sKIgFWc" rel="nofollow" target="_blank">https://youtu.be/d8Y2sKIgFWc</a> --- 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