Sergey Levine on Robotics and Machine Learning

Lex Fridman interviews Sergey Levine in episode 108 of his podcast.

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. This conversation is part of the Artificial Intelligence podcast.

Episode outline:

  • 0:00 – Introduction
  • 3:05 – 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

Frank

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