Sergey Levine: Robotics and Machine Learning | Lex Fridman Podcast
Lex Fridman Lex Fridman
3.85M subscribers
95,773 views
0

 Published On Jul 14, 2020

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 signing up with these sponsors:
- ExpressVPN at 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

EPISODE LINKS:
Sergey's Twitter:   / svlevine  
Sergey's Website: http://rail.eecs.berkeley.edu/
Sergey's Papers: https://scholar.google.com/citations?...

PODCAST INFO:
Podcast website:
https://lexfridman.com/podcast
Apple Podcasts:
https://apple.co/2lwqZIr
Spotify:
https://spoti.fi/2nEwCF8
RSS:
https://lexfridman.com/feed/podcast/
Full episodes playlist:
   • Lex Fridman Podcast  
Clips playlist:
   • Lex Fridman Podcast Clips  

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

CONNECT:
- Subscribe to this YouTube channel
- Twitter:   / lexfridman  
- LinkedIn:   / lexfridman  
- Facebook:   / lexfridmanpage  
- Instagram:   / lexfridman  
- Medium:   / lexfridman  
- Support on Patreon:   / lexfridman  

show more

Share/Embed