#108 – Sergey Levine: Robotics and Machine Learning
July 14, 2020
1 hr 37 min
108
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105 of 498
Episode 108 · July 14, 2020
#108 – Sergey Levine: Robotics and Machine Learning
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#108 – Sergey Levine: Robotics and Machine Learning
Summary
<p>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.</p>
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<p>Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.</p>
<p>OUTLINE:<br />
00:00 – Introduction<br />
03:05 – State-of-the-art robots vs humans<br />
16:13 – Robotics may help us understand intelligence<br />
22:49 – End-to-end learning in robotics<br />
27:01 – Canonical problem in robotics<br />
31:44 – Commonsense reasoning in robotics<br />
34:41 – Can we solve robotics through learning?<br />
44:55 – What is reinforcement learning?<br />
1:06:36 – Tesla Autopilot<br />
1:08:15 – Simulation in reinforcement learning<br />
1:13:46 – Can we learn gravity from data?<br />
1:16:03 – Self-play<br />
1:17:39 – Reward functions<br />
1:27:01 – Bitter lesson by Rich Sutton<br />
1:32:13 – Advice for students interesting in AI<br />