Lex Fridman Podcast Archiv

Sara Seager is a planetary scientist at MIT, known for her work on the search for exoplanets.

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Episode links:

Sara’s Twitter: https://twitter.com/profsaraseager

Sara’s Website: https://www.saraseager.com/

The Smallest Lights in the Universe (book): https://amzn.to/3g3LfHA

If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

05:32 – Falling in love with the stars

09:55 – Are we alone in the universe?

15:27 – Seager equation for number of habitable planets

27:48 – Exoplanets

34:44 – Earth-like exoplanets

40:43 – Intelligent life

52:34 – Number of planets per star

55:09 – Space exploration

57:36 – Traveling to Proxima Centauri

1:00:52 – Starshade

1:07:34 – Using the sun as a gravitational lens

1:09:44 – Starshot

1:12:45 – Rogue planets

1:15:44 – The Smallest Lights in the Universe

1:30:15 – Book recommendations

1:37:48 – Advice for a young person

1:39:29 – Meaning of life

Dileep George is a researcher at the intersection of neuroscience and artificial intelligence, co-founder of Vicarious, formerly co-founder of Numenta. From the early work on Hierarchical temporal memory to Recursive Cortical Networks to today, Dileep’s always sought to engineer intelligence that is closely inspired by the human brain.

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

0:00 – Introduction

4:50 – Building a model of the brain

17:11 – Visual cortex

27:50 – Probabilistic graphical models

31:35 – Encoding information in the brain

36:56 – Recursive Cortical Network

51:09 – Solving CAPTCHAs algorithmically

1:06:48 – Hype around brain-inspired AI

1:18:21 – How does the brain learn?

1:21:32 – Perception and cognition

1:25:43 – Open problems in brain-inspired AI

1:30:33 – GPT-3

1:40:41 – Memory

1:45:08 – Neuralink

1:51:32 – Consciousness

1:57:59 – Book recommendations

2:06:49 – Meaning of life

Russ Tedrake is a roboticist and professor at MIT and vice president of robotics research at TRI. He works on control of robots in interesting, complicated, underactuated, stochastic, difficult to model situations.

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

04:29 – Passive dynamic walking

09:40 – Animal movement

13:34 – Control vs Dynamics

15:49 – Bipedal walking

20:56 – Running barefoot

33:01 – Think rigorously with machine learning

44:05 – DARPA Robotics Challenge

1:07:14 – When will a robot become UFC champion

1:18:32 – Black Mirror Robot Dog

1:34:01 – Robot control

1:47:00 – Simulating robots

2:00:33 – Home robotics

2:03:40 – Soft robotics

2:07:25 – Underactuated robotics

2:20:42 – Touch

2:28:55 – Book recommendations

2:40:08 – Advice to young people

2:44:20 – Meaning of life

Manolis Kellis is a professor at MIT and head of the MIT Computational Biology Group. He is interested in understanding the human genome from a computational, evolutionary, biological, and other cross-disciplinary perspectives.

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

03:54 – Human genome

17:47 – Sources of knowledge

29:15 – Free will

33:26 – Simulation

35:17 – Biological and computing

50:10 – Genome-wide evolutionary signatures

56:54 – Evolution of COVID-19

1:02:59 – Are viruses intelligent?

1:12:08 – Humans vs viruses

1:19:39 – Engineered pandemics

1:23:23 – Immune system

1:33:22 – Placebo effect

1:35:39 – Human genome source code

1:44:40 – Mutation

1:51:46 – Deep learning

1:58:08 – Neuralink

2:07:07 – Language

2:15:19 – Meaning of life

Ian Hutchinson is a nuclear engineer and plasma physicist at MIT. He has made a number of important contributions in plasma physics including the magnetic confinement of plasmas seeking to enable fusion reactions, which is the energy source of the stars, to be used for practical energy production. Current nuclear reactors are based on fission as we discuss. Ian has also written on the philosophy of science and the relationship between science and religion.

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

05:32 – Nuclear physics and plasma physics

08:00 – Fusion energy

35:22 – Nuclear weapons

42:06 – Existential risks

50:29 – Personal journey in religion

56:27 – What is God like?

1:01:34 – Scientism

1:04:21 – Atheism

1:06:39 – Not knowing

1:09:57 – Faith

1:13:46 – The value of loyalty and love

1:23:26 – Why is there suffering in the world

1:35:08 – AGI

1:40:27 – Consciousness

1:48:14 – Simulation

1:52:20 – Adam and Eve

1:54:57 – Meaning of life

Richard Karp is a professor at Berkeley and one of the most important figures in the history of theoretical computer science. In 1985, he received the Turing Award for his research in the theory of algorithms, including the development of the Edmonds–Karp algorithm for solving the maximum flow problem on networks, Hopcroft–Karp algorithm for finding maximum cardinality matchings in bipartite graphs, and his landmark paper in complexity theory called “Reducibility Among Combinatorial Problems”, in which he proved 21 problems to be NP-complete. This paper was probably the most important catalyst in the explosion of interest in the study of NP-completeness and the P vs NP problem.

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

03:50 – Geometry

09:46 – Visualizing an algorithm

13:00 – A beautiful algorithm

18:06 – Don Knuth and geeks

22:06 – Early days of computers

25:53 – Turing Test

30:05 – Consciousness

33:22 – Combinatorial algorithms

37:42 – Edmonds-Karp algorithm

40:22 – Algorithmic complexity

50:25 – P=NP

54:25 – NP-Complete problems

1:10:29 – Proving P=NP

1:12:57 – Stable marriage problem

1:20:32 – Randomized algorithms

1:33:23 – Can a hard problem be easy in practice?

1:43:57 – Open problems in theoretical computer science

1:46:21 – A strange idea in complexity theory

1:50:49 – Machine learning

1:56:26 – Bioinformatics

2:00:37 – Memory of Richard’s father

Jitendra Malik is 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.

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

03:17 – 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 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.

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

04:24 – 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 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.

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

03:05 – 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 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.

Support this podcast by supporting these sponsors:

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

05:25 – World War II

09:53 – 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

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.

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

03:29 – 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?

Robert Langer is a professor at MIT and one of the most cited researchers in history, specializing in biotechnology fields of drug delivery systems and tissue engineering. He has bridged theory and practice by being a key member and driving force in launching many successful biotech companies out of MIT.

Support this podcast by supporting these sponsors:

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

03:07 – Magic and science

05:34 – Memorable rejection

08:35 – How to come up with big ideas in science

13:27 – How to make a new drug

22:38 – Drug delivery

28:22 – Tissue engineering

35:22 – Beautiful idea in bioengineering

38:16 – Patenting process

42:21 – What does it take to build a successful startup?

46:18 – Mentoring students

50:54 – Funding

58:08 – Cookies

59:41 – What are you most proud of?

David Patterson is a Turing award winner and professor of computer science at Berkeley. He is known for pioneering contributions to RISC processor architecture used by 99% of new chips today and for co-creating RAID storage. The impact that these two lines of research and development have had on our world is immeasurable. He is also one of the great educators of computer science in the world. His book with John Hennessy “Computer Architecture: A Quantitative Approach” is how I first learned about and was humbled by the inner workings of machines at the lowest level.

Support this podcast by supporting these sponsors:

– Jordan Harbinger Show: https://jordanharbinger.com/lex/

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

03:28 – How have computers changed?

04:22 – What’s inside a computer?

10:02 – Layers of abstraction

13:05 – RISC vs CISC computer architectures

28:18 – Designing a good instruction set is an art

31:46 – Measures of performance

36:02 – RISC instruction set

39:39 – RISC-V open standard instruction set architecture

51:12 – Why do ARM implementations vary?

52:57 – Simple is beautiful in instruction set design

58:09 – How machine learning changed computers

1:08:18 – Machine learning benchmarks

1:16:30 – Quantum computing

1:19:41 – Moore’s law

1:28:22 – RAID data storage

1:36:53 – Teaching

1:40:59 – Wrestling

1:45:26 – Meaning of life

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 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.

Support this podcast by supporting these sponsors:

– Jordan Harbinger Show: https://jordanharbinger.com/lex/

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If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:

00:00 – Introduction

03:20 – Books that inspired you

06:38 – 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