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.

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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