#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
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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