PODCAST · Lex Fridman Podcast

2020 年 2 月节目

7 期 · 7 期带分节

  • 02-29#76 – John Hopfield: Physics View of the Mind and Neurobiology
    1. 00:00Introduction
    2. 02:35Difference between biological and artificial neural networks
    3. 08:49Adaptation
    4. 13:45Physics view of the mind
    5. 23:03Hopfield networks and associative memory
    6. 35:22Boltzmann machines
    7. 37:29Learning
    8. 39:53Consciousness
    9. 48:45Attractor networks and dynamical systems
    10. 53:14How do we build intelligent systems?
    11. 57:11Deep thinking as the way to arrive at breakthroughs
    12. 59:12Brain-computer interfaces
    13. 1:06:10Mortality
    14. 1:08:12Meaning of life
  • 02-26#75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI
    1. 00:00Introduction
    2. 03:32Universe as a computer
    3. 05:48Occam’s razor
    4. 09:26Solomonoff induction
    5. 15:05Kolmogorov complexity
    6. 20:06Cellular automata
    7. 26:03What is intelligence?
    8. 35:26AIXI – Universal Artificial Intelligence
    9. 1:05:24Where do rewards come from?
    10. 1:12:14Reward function for human existence
    11. 1:13:32Bounded rationality
    12. 1:16:07Approximation in AIXI
    13. 1:18:01Godel machines
    14. 1:21:51Consciousness
    15. 1:27:15AGI community
    16. 1:32:36Book recommendations
    17. 1:36:07Two moments to relive (past and future)
  • 02-24#74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI
    1. 00:00Introduction
    2. 03:02How far are we in development of AI?
    3. 08:25Neuralink and brain-computer interfaces
    4. 14:49The term “artificial intelligence”
    5. 19:00Does science progress by ideas or personalities?
    6. 19:55Disagreement with Yann LeCun
    7. 23:53Recommender systems and distributed decision-making at scale
    8. 43:34Facebook, privacy, and trust
    9. 1:01:11Are human beings fundamentally good?
    10. 1:02:32Can a human life and society be modeled as an optimization problem?
    11. 1:04:27Is the world deterministic?
    12. 1:04:59Role of optimization in multi-agent systems
    13. 1:09:52Optimization of neural networks
    14. 1:16:08Beautiful idea in optimization: Nesterov acceleration
    15. 1:19:02What is statistics?
    16. 1:29:21What is intelligence?
    17. 1:37:01Advice for students
    18. 1:39:57Which language is more beautiful: English or French?
  • 02-20#73 – Andrew Ng: Deep Learning, Education, and Real-World AI
    1. 00:00Introduction
    2. 02:23First few steps in AI
    3. 05:05Early days of online education
    4. 16:07Teaching on a whiteboard
    5. 17:46Pieter Abbeel and early research at Stanford
    6. 23:17Early days of deep learning
    7. 32:55Quick preview: deeplearning.ai, landing.ai, and AI fund
    8. 33:23deeplearning.ai: how to get started in deep learning
    9. 45:55Unsupervised learning
    10. 49:40deeplearning.ai (continued)
    11. 56:12Career in deep learning
    12. 58:56Should you get a PhD?
    13. 1:03:28AI fund – building startups
    14. 1:11:14Landing.ai – growing AI efforts in established companies
    15. 1:20:44Artificial general intelligence
  • 02-17#72 – Scott Aaronson: Quantum Computing
    1. 00:00Introduction
    2. 05:07Role of philosophy in science
    3. 29:27What is a quantum computer?
    4. 41:12Quantum decoherence (noise in quantum information)
    5. 49:22Quantum computer engineering challenges
    6. 51:00Moore’s Law
    7. 56:33Quantum supremacy
    8. 1:12:18Using quantum computers to break cryptography
    9. 1:17:11Practical application of quantum computers
    10. 1:22:18Quantum machine learning, questionable claims, and cautious optimism
    11. 1:30:53Meaning of life
  • 02-14Vladimir Vapnik: Predicates, Invariants, and the Essence of Intelligence
    1. 00:00Introduction
    2. 02:55Alan Turing: science and engineering of intelligence
    3. 09:09What is a predicate?
    4. 14:22Plato’s world of ideas and world of things
    5. 21:06Strong and weak convergence
    6. 28:37Deep learning and the essence of intelligence
    7. 50:36Symbolic AI and logic-based systems
    8. 54:31How hard is 2D image understanding?
    9. 1:00:23Data
    10. 1:06:39Language
    11. 1:14:54Beautiful idea in statistical theory of learning
    12. 1:19:28Intelligence and heuristics
    13. 1:22:23Reasoning
    14. 1:25:11Role of philosophy in learning theory
    15. 1:31:40Music (speaking in Russian)
    16. 1:35:08Mortality
  • 02-05Jim Keller: Moore’s Law, Microprocessors, Abstractions, and First Principles
    1. 00:00Introduction
    2. 02:12Difference between a computer and a human brain
    3. 03:43Computer abstraction layers and parallelism
    4. 17:53If you run a program multiple times, do you always get the same answer?
    5. 20:43Building computers and teams of people
    6. 22:41Start from scratch every 5 years
    7. 30:05Moore’s law is not dead
    8. 55:47Is superintelligence the next layer of abstraction?
    9. 1:00:02Is the universe a computer?
    10. 1:03:00Ray Kurzweil and exponential improvement in technology
    11. 1:04:33Elon Musk and Tesla Autopilot
    12. 1:20:51Lessons from working with Elon Musk
    13. 1:28:33Existential threats from AI
    14. 1:32:38Happiness and the meaning of life