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#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
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By Lex Fridman
4.7
1197811,978 ratings
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
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