Lex Fridman interviews Daphne Koller, a professor of computer science at Stanford University, a co-founder of Coursera with Andrew Ng and Founder and CEO of insitro, a company at the intersection of machine learning and biomedicine.

This conversation is part of the Artificial Intelligence podcast.

Time index:

  • 0:00 – Introduction
  • 2:22 – Will we one day cure all disease?
  • 6:31 – Longevity
  • 10:16 – Role of machine learning in treating diseases
  • 13:05 – A personal journey to medicine
  • 16:25 – Insitro and disease-in-a-dish models
  • 33:25 – What diseases can be helped with disease-in-a-dish approaches?
  • 36:43 – Coursera and education
  • 49:04 – Advice to people interested in AI
  • 50:52 – Beautiful idea in deep learning
  • 55:10 – Uncertainty in AI
  • 58:29 – AGI and AI safety
  • 1:06:52 – Are most people good?
  • 1:09:04 – Meaning of life

Lex Fridman interviews David Silver for the Artificial Intelligence podcast..

David Silver leads the reinforcement learning research group at DeepMind and was lead researcher on AlphaGo, AlphaZero and co-lead on AlphaStar, and MuZero and lot of important work in reinforcement learning.

Time Index:

  • 0:00 – Introduction
  • 4:09 – First program
  • 11:11 – AlphaGo
  • 21:42 – Rule of the game of Go
  • 25:37 – Reinforcement learning: personal journey
  • 30:15 – What is reinforcement learning?
  • 43:51 – AlphaGo (continued)
  • 53:40 – Supervised learning and self play in AlphaGo
  • 1:06:12 – Lee Sedol retirement from Go play
  • 1:08:57 – Garry Kasparov
  • 1:14:10 – Alpha Zero and self play
  • 1:31:29 – Creativity in AlphaZero
  • 1:35:21 – AlphaZero applications
  • 1:37:59 – Reward functions
  • 1:40:51 – Meaning of life

MIT Introduction to Deep Learning 6.S191: Lecture 6 with Ava Soleimany.

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Lecture Outline

  • 0:00 – Introduction
  • 0:58 – Course logistics
  • 3:59 – Upcoming guest lectures
  • 5:35 – Deep learning and expressivity of NNs
  • 10:02 – Generalization of deep models
  • 14:14 – Adversarial attacks
  • 17:00 – Limitations summary
  • 18:18 – Structure in deep learning
  • 22:53 – Uncertainty & bayesian deep learning
  • 28:09 – Deep evidential regression
  • 33:08 – AutoML
  • 36:43 – Conclusion