PODCAST · Lex Fridman Podcast

2020 年 7 月节目

8 期 · 8 期带分节

  • 07-31#113 – Manolis Kellis: Human Genome and Evolutionary Dynamics
    1. 00:00Introduction
    2. 03:54Human genome
    3. 17:47Sources of knowledge
    4. 29:15Free will
    5. 33:26Simulation
    6. 35:17Biological and computing
    7. 50:10Genome-wide evolutionary signatures
    8. 56:54Evolution of COVID-19
    9. 1:02:59Are viruses intelligent?
    10. 1:12:08Humans vs viruses
    11. 1:19:39Engineered pandemics
    12. 1:23:23Immune system
    13. 1:33:22Placebo effect
    14. 1:35:39Human genome source code
    15. 1:44:40Mutation
    16. 1:51:46Deep learning
    17. 1:58:08Neuralink
    18. 2:07:07Language
    19. 2:15:19Meaning of life
  • 07-29#112 – Ian Hutchinson: Nuclear Fusion, Plasma Physics, and Religion
    1. 00:00Introduction
    2. 05:32Nuclear physics and plasma physics
    3. 08:00Fusion energy
    4. 35:22Nuclear weapons
    5. 42:06Existential risks
    6. 50:29Personal journey in religion
    7. 56:27What is God like?
    8. 1:01:34Scientism
    9. 1:04:21Atheism
    10. 1:06:39Not knowing
    11. 1:09:57Faith
    12. 1:13:46The value of loyalty and love
    13. 1:23:26Why is there suffering in the world
    14. 1:35:08AGI
    15. 1:40:27Consciousness
    16. 1:48:14Simulation
    17. 1:52:20Adam and Eve
    18. 1:54:57Meaning of life
  • 07-26#111 – Richard Karp: Algorithms and Computational Complexity
    1. 00:00Introduction
    2. 03:50Geometry
    3. 09:46Visualizing an algorithm
    4. 13:00A beautiful algorithm
    5. 18:06Don Knuth and geeks
    6. 22:06Early days of computers
    7. 25:53Turing Test
    8. 30:05Consciousness
    9. 33:22Combinatorial algorithms
    10. 37:42Edmonds-Karp algorithm
    11. 40:22Algorithmic complexity
    12. 50:25P=NP
    13. 54:25NP-Complete problems
    14. 1:10:29Proving P=NP
    15. 1:12:57Stable marriage problem
    16. 1:20:32Randomized algorithms
    17. 1:33:23Can a hard problem be easy in practice?
    18. 1:43:57Open problems in theoretical computer science
    19. 1:46:21A strange idea in complexity theory
    20. 1:50:49Machine learning
    21. 1:56:26Bioinformatics
    22. 2:00:37Memory of Richard’s father
  • 07-21#110 – Jitendra Malik: Computer Vision
    1. 00:00Introduction
    2. 03:17Computer vision is hard
    3. 10:05Tesla Autopilot
    4. 21:20Human brain vs computers
    5. 23:14The general problem of computer vision
    6. 29:09Images vs video in computer vision
    7. 37:47Benchmarks in computer vision
    8. 40:06Active learning
    9. 45:34From pixels to semantics
    10. 52:47Semantic segmentation
    11. 57:05The three R’s of computer vision
    12. 1:02:52End-to-end learning in computer vision
    13. 1:04:246 lessons we can learn from children
    14. 1:08:36Vision and language
    15. 1:12:30Turing test
    16. 1:16:17Open problems in computer vision
    17. 1:24:49AGI
    18. 1:35:47Pick the right problem
  • 07-18#109 – Brian Kernighan: UNIX, C, AWK, AMPL, and Go Programming
    1. 00:00Introduction
    2. 04:24UNIX early days
    3. 22:09Unix philosophy
    4. 31:54Is programming art or science?
    5. 35:18AWK
    6. 42:03Programming setup
    7. 46:39History of programming languages
    8. 52:48C programming language
    9. 58:44Go language
    10. 1:01:57Learning new programming languages
    11. 1:04:57Javascript
    12. 1:08:16Variety of programming languages
    13. 1:10:30AMPL
    14. 1:18:01Graph theory
    15. 1:22:20AI in 1964
    16. 1:27:50Future of AI
    17. 1:29:47Moore’s law
    18. 1:32:54Computers in our world
    19. 1:40:37Life
  • 07-14#108 – Sergey Levine: Robotics and Machine Learning
    1. 00:00Introduction
    2. 03:05State-of-the-art robots vs humans
    3. 16:13Robotics may help us understand intelligence
    4. 22:49End-to-end learning in robotics
    5. 27:01Canonical problem in robotics
    6. 31:44Commonsense reasoning in robotics
    7. 34:41Can we solve robotics through learning?
    8. 44:55What is reinforcement learning?
    9. 1:06:36Tesla Autopilot
    10. 1:08:15Simulation in reinforcement learning
    11. 1:13:46Can we learn gravity from data?
    12. 1:16:03Self-play
    13. 1:17:39Reward functions
    14. 1:27:01Bitter lesson by Rich Sutton
    15. 1:32:13Advice for students interesting in AI
    16. 1:33:55Meaning of life
  • 07-08#107 – Peter Singer: Suffering in Humans, Animals, and AI
    1. 00:00Introduction
    2. 05:25World War II
    3. 09:53Suffering
    4. 16:06Is everyone capable of evil?
    5. 21:52Can robots suffer?
    6. 37:22Animal liberation
    7. 40:31Question for AI about suffering
    8. 43:32Neuralink
    9. 45:11Control problem of AI
    10. 51:08Utilitarianism
    11. 59:43Helping people in poverty
    12. 1:05:15Mortality
  • 07-03#106 – Matt Botvinick: Neuroscience, Psychology, and AI at DeepMind
    1. 00:00Introduction
    2. 03:29How much of the brain do we understand?
    3. 14:26Psychology
    4. 22:53The paradox of the human brain
    5. 32:23Cognition is a function of the environment
    6. 39:34Prefrontal cortex
    7. 53:27Information processing in the brain
    8. 1:00:11Meta-reinforcement learning
    9. 1:15:18Dopamine
    10. 1:19:01Neuroscience and AI research
    11. 1:23:37Human side of AI
    12. 1:39:56Dopamine and reinforcement learning
    13. 1:53:07Can we create an AI that a human can love?