#108 – Sergey Levine: Robotics and Machine Learning

Sergey Levine is a professor at Berkeley and a world-class researcher in deep learning, reinforcement learning, robotics, and computer vision, including the development of algorithms for end-to-end training of neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, and deep RL algorithms. Support this podcast by supporting these sponsors: - ExpressVPN: }https://www.expressvpn.com/lexpod - Cash App – use code "LexPodcast" and download: - Cash App (App Store): }https://apple.co/2sPrUHe - Cash App (Google Play): }https://bit.ly/2MlvP5w If you would like to get more information about this podcast go to }https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon. Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. OUTLINE: 00:00 - Introduction 03:05 - State-of-the-art robots vs humans 16:13 - Robotics may help us understand intelligence 22:49 - End-to-end learning in robotics 27:01 - Canonical problem in robotics 31:44 - Commonsense reasoning in robotics 34:41 - Can we solve robotics through learning? 44:55 - What is reinforcement learning? 1:06:36 - Tesla Autopilot 1:08:15 - Simulation in reinforcement learning 1:13:46 - Can we learn gravity from data? 1:16:03 - Self-play 1:17:39 - Reward functions 1:27:01 - Bitter lesson by Rich Sutton 1:32:13 - Advice for students interesting in AI 1:33:55 - Meaning of life