Jitendra Malik on Computer Vision

Jitendra Malik on Computer Vision

Lex Fridman interviews Jitendra Malik, a professor at Berkeley and one of the seminal figures in the field of computer vision, the kind before the deep learning revolution, and the kind after.

He has been cited over 180,000 times and has mentored many world-class researchers in computer science. This conversation is part of the Artificial Intelligence podcast.

Content index:

  • 0:00 – Introduction
  • 3:17 – Computer vision is hard
  • 10:05 – Tesla Autopilot
  • 21:20 – Human brain vs computers
  • 23:14 – The general problem of computer vision
  • 29:09 – Images vs video in computer vision
  • 37:47 – Benchmarks in computer vision
  • 40:06 – Active learning
  • 45:34 – From pixels to semantics
  • 52:47 – Semantic segmentation
  • 57:05 – The three R’s of computer vision
  • 1:02:52 – End-to-end learning in computer vision
  • 1:04:24 – 6 lessons we can learn from children
  • 1:08:36 – Vision and language
  • 1:12:30 – Turing test
  • 1:16:17 – Open problems in computer vision
  • 1:24:49 – AGI
  • 1:35:47 – Pick the right problem

Brian Kernighan on UNIX, C, AWK, AMPL, and Go Programming

Brian Kernighan on UNIX, C, AWK, AMPL, and Go Programming

Lex Fridman interviews Brian Kernighan in the latest episode of his podcast.

Brian Kernighan is a professor of computer science at Princeton University. He co-authored the C Programming Language with Dennis Ritchie (creator of C) and has written a lot of books on programming, computers, and life including the Practice of Programming, the Go Programming Language, his latest UNIX: A History and a Memoir. He co-created AWK, the text processing language used by Linux folks like myself. He co-designed AMPL, an algebraic modeling language for large-scale optimization. This conversation is part of the Artificial Intelligence podcast.

Outline:

  • 0:00 – Introduction
  • 4:24 – UNIX early days
  • 22:09 – Unix philosophy
  • 31:54 – Is programming art or science?
  • 35:18 – AWK
  • 42:03 – Programming setup
  • 46:39 – History of programming languages
  • 52:48 – C programming language
  • 58:44 – Go language
  • 1:01:57 – Learning new programming languages
  • 1:04:57 – Javascript
  • 1:08:16 – Variety of programming languages
  • 1:10:30 – AMPL
  • 1:18:01 – Graph theory
  • 1:22:20 – AI in 1964
  • 1:27:50 – Future of AI
  • 1:29:47 – Moore’s law
  • 1:32:54 – Computers in our world
  • 1:40:37 – Life

Sergey Levine on Robotics and Machine Learning

Sergey Levine on Robotics and Machine Learning

Lex Fridman interviews Sergey Levine in episode 108 of his podcast.

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. This conversation is part of the Artificial Intelligence podcast.

Episode outline:

  • 0:00 – Introduction
  • 3:05 – 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

Peter Singer on Suffering in Humans, Animals, and AI

Peter Singer on Suffering in Humans, Animals, and AI

Lex Fridman interviews Peter Singer in this enlightening episode of his podcast.

Peter Singer is a professor of bioethics at Princeton, best known for his 1975 book Animal Liberation, that makes an ethical case against eating meat. He has written brilliantly from an ethical perspective on extreme poverty, euthanasia, human genetic selection, sports doping, the sale of kidneys, and happiness including in his books Ethics in the Real World and The Life You Can Save. He was a key popularizer of the effective altruism movement and is generally considered one of the most influential philosophers in the world. This conversation is part of the Artificial Intelligence podcast.

Content index:

  • 0:00 – Introduction
  • 5:25 – World War II
  • 9:53 – Suffering
  • 16:06 – Is everyone capable of evil?
  • 21:52 – Can robots suffer?
  • 37:22 – Animal liberation
  • 40:31 – Question for AI about suffering
  • 43:32 – Neuralink
  • 45:11 – Control problem of AI
  • 51:08 – Utilitarianism
  • 59:43 – Helping people in poverty
  • 1:05:15 – Mortality
  • AI
  • 0
  • Closed
  • 58 sec read

Matt Botvinick on Neuroscience, Psychology, and AI at DeepMind

Matt Botvinick on Neuroscience, Psychology, and AI at DeepMind

Lex Fridman interviews Matt Botvinick in this latest episode of his podcast.

Matt Botvinick is the Director of Neuroscience Research at DeepMind. He is a brilliant cross-disciplinary mind navigating effortlessly between cognitive psychology, computational neuroscience, and artificial intelligence. This conversation is part of the Artificial Intelligence podcast.

Content outline:

  • 0:00 – Introduction
  • 3:29 – How much of the brain do we understand?
  • 14:26 – Psychology
  • 22:53 – The paradox of the human brain
  • 32:23 – Cognition is a function of the environment
  • 39:34 – Prefrontal cortex
  • 53:27 – Information processing in the brain
  • 1:00:11 – Meta-reinforcement learning
  • 1:15:18 – Dopamine
  • 1:19:01 – Neuroscience and AI research
  • 1:23:37 – Human side of AI
  • 1:39:56 – Dopamine and reinforcement learning
  • 1:53:07 – Can we create an AI that a human can love?

Lex Fridman Interviews Ben Goertzel About Artificial General Intelligence

Lex Fridman Interviews Ben Goertzel About Artificial General Intelligence

Lex Fridman interviews Ben Goertzel in episode 103 of his AI podcast.

Ben Goertzel is one of the most interesting minds in the artificial intelligence community. He is the founder of SingularityNET, designer of OpenCog AI framework, formerly a director of research at the Machine Intelligence Research Institute, Chief Scientist of Hanson Robotics, the company that created the Sophia Robot. He has been a central figure in the AGI community for many years, including in the Conference on Artificial General Intelligence. This conversation is part of the Artificial Intelligence podcast.

Show outline:

  • 0:00 – Introduction
  • 3:20 – Books that inspired you
  • 6:38 – Are there intelligent beings all around us?
  • 13:13 – Dostoevsky
  • 15:56 – Russian roots
  • 20:19 – When did you fall in love with AI?
  • 31:30 – Are humans good or evil?
  • 42:04 – Colonizing mars
  • 46:53 – Origin of the term AGI
  • 55:56 – AGI community
  • 1:12:36 – How to build AGI?
  • 1:36:47 – OpenCog
  • 2:25:32 – SingularityNET
  • 2:49:33 – Sophia
  • 3:16:02 – Coronavirus
  • 3:24:14 – Decentralized mechanisms of power
  • 3:40:16 – Life and death
  • 3:42:44 – Would you live forever?
  • 3:50:26 – Meaning of life
  • 3:58:03 – Hat
  • 3:58:46 – Question for AGI

Joscha Bach on Artificial Consciousness and the Nature of Reality

Joscha Bach on Artificial Consciousness and the Nature of Reality

Lex Fridman interviews Joscha Bach.

Joscha Bach is the VP of Research at the AI Foundation, previously doing research at MIT and Harvard. Joscha work explores the workings of the human mind, intelligence, consciousness, life on Earth, and the possibly-simulated fabric of our universe. This conversation is part of the Artificial Intelligence podcast.

Content index:

  • 0:00 – Introduction
  • 3:14 – Reverse engineering Joscha Bach
  • 10:38 – Nature of truth
  • 18:47 – Original thinking
  • 23:14 – Sentience vs intelligence
  • 31:45 – Mind vs Reality
  • 46:51 – Hard problem of consciousness
  • 51:09 – Connection between the mind and the universe
  • 56:29 – What is consciousness
  • 1:02:32 – Language and concepts
  • 1:09:02 – Meta-learning
  • 1:16:35 – Spirit
  • 1:18:10 – Our civilization may not exist for long
  • 1:37:48 – Twitter and social media
  • 1:44:52 – What systems of government might work well?
  • 1:47:12 – The way out of self-destruction with AI
  • 1:55:18 – AI simulating humans to understand its own nature
  • 2:04:32 – Reinforcement learning
  • 2:09:12 – Commonsense reasoning
  • 2:15:47 – Would AGI need to have a body?
  • 2:22:34 – Neuralink
  • 2:27:01 – Reasoning at the scale of neurons and societies
  • 2:37:16 – Role of emotion
  • 2:48:03 – Happiness is a cookie that your brain bakes for itself

Lex Friedman Interviews his Father, Alexander Fridman a Plasma Physicist

Lex Friedman Interviews his Father, Alexander Fridman a Plasma Physicist

Lex Fridman

Alexander Fridman is a professor at Drexel University and the director of the Nyheim Plasma Institute. He is one of the top plasma physicists and plasma chemists in the world. And most importantly to me, he is my dad. This conversation is part of the Artificial Intelligence podcast.

Content index:

  • 0:00 – Introduction
  • 6:57 – Einstein and beautiful ideas in physics
  • 10:03 – Simple, powerful ideas
  • 12:57 – Falling in love with science
  • 19:45 – Gagarin and space race
  • 21:29 – Poetry
  • 29:31 – Early school days
  • 38:33 – Soviet education
  • 43:31 – A stressful experience
  • 51:49 – Childhood – memories of mom and dad
  • 54:26 – Losing dad
  • 1:02:30 – World War II
  • 1:12:21 – Soviet Union
  • 1:13:50 – Moscow Institute of Physics and Technology
  • 1:24:18 – Theoretical physics
  • 1:26:29 – Kurchatov Institute of Atomic Energy
  • 1:37:34 – Meeting mom
  • 1:45:16 – Becoming a father
  • 2:06:28 – Chernobyl nuclear disaster
  • 2:17:35 – What is plasma?
  • 2:26:54 – Hot plasma and cold plasma
  • 2:31:24 – Mysteries in plasma
  • 2:36:38 – Plasma physics and plasma chemistry
  • 2:39:42 – Plasma medicine
  • 2:45:12 – Nobel prizes in plasma
  • 2:50:17 – Cold fusion
  • 2:54:16 – Journey to America
  • 2:59:43 – Nyheim Plasma Institute
  • 3:12:10 – Artificial intelligence
  • 3:21:43 – Mortality
  • 3:27:59 – Meaning of life
  • 3:35:32 – Toast

Kate Darling on Social Robotics

Kate Darling on Social Robotics

Lex Fridman interviews Kate Darling in this episode of the AI Show.

Kate Darling is a researcher at MIT, interested in social robotics, robot ethics, and generally how technology intersects with society. She explores the emotional connection between human beings and life-like machines, which for me, is one of the most exciting topics in all of artificial intelligence. This conversation is part of the Artificial Intelligence podcast.

Time index:

  • 0:00 – Introduction
  • 3:31 – Robot ethics
  • 4:36 – Universal Basic Income
  • 6:31 – Mistreating robots
  • 17:17 – Robots teaching us about ourselves
  • 20:27 – Intimate connection with robots
  • 24:29 – Trolley problem and making difficult moral decisions
  • 31:59 – Anthropomorphism
  • 38:09 – Favorite robot
  • 41:19 – Sophia
  • 42:46 – Designing robots for human connection
    47:01 – Why is it so hard to build a personal robotics company?
    50:03 – Is it possible to fall in love with a robot?
    56:39 – Robots displaying consciousness and mortality
    58:33 – Manipulation of emotion by companies
    1:04:40 – Intellectual property
    1:09:23 – Lessons for robotics from parenthood
    1:10:41 – Hope for future of robotics

Ilya Sutskever on Deep Learning

Ilya Sutskever on Deep Learning

Lex Fridman interviews Ilya Sutskever. co-founder of OpenAI.

Ilya Sutskever is the co-founder of OpenAI, is one of the most cited computer scientist in history with over 165,000 citations, and to me, is one of the most brilliant and insightful minds ever in the field of deep learning. There are very few people in this world who I would rather talk to and brainstorm with about deep learning, intelligence, and life than Ilya, on and off the mic.

This conversation is part of the Artificial Intelligence podcast.

Time index:

  • 0:00 – Introduction
  • 2:23 – AlexNet paper and the ImageNet moment
  • 8:33 – Cost functions
  • 13:39 – Recurrent neural networks
  • 16:19 – Key ideas that led to success of deep learning
  • 19:57 – What’s harder to solve: language or vision?
  • 29:35 – We’re massively underestimating deep learning
  • 36:04 – Deep double descent
  • 41:20 – Backpropagation
  • 42:42 – Can neural networks be made to reason?
  • 50:35 – Long-term memory
  • 56:37 – Language models
  • 1:00:35 – GPT-2
  • 1:07:14 – Active learning
  • 1:08:52 – Staged release of AI systems
  • 1:13:41 – How to build AGI?
  • 1:25:00 – Question to AGI
  • 1:32:07 – Meaning of life