#483 – Julia Shaw: Criminal Psychology of Murder, Serial Killers, Memory & Sex

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. Support this podcast by supporting our sponsors: - Sun Basket, use code LEX: https://sunbasket.com/lex - PowerDot, use code LEX: https://powerdot.com/lex 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, Medium, 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

29 Juli 20202h 1min

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. Support this podcast by supporting our sponsors: - Eight Sleep: https://eightsleep.com/lex - Cash App – use code "LexPodcast" and download: - Cash App (App Store): https://apple.co/2sPrUHe - Cash App (Google Play): https://bit.ly/2MlvP5w 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, Medium, 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

26 Juli 20202h 8min

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. Support this podcast by supporting our sponsors: - BetterHelp: http://betterhelp.com/lex - ExpressVPN: https://www.expressvpn.com/lexpod 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, Medium, 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

21 Juli 20201h 42min

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. Support this podcast by supporting our sponsors: - Eight Sleep: https://eightsleep.com/lex - Raycon: http://buyraycon.com/lex 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, Medium, 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

18 Juli 20201h 43min

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. Support this podcast by supporting these sponsors: - ExpressVPN: https://www.expressvpn.com/lexpod - Cash App – use code "LexPodcast" and download: - Cash App (App Store): https://apple.co/2sPrUHe - Cash App (Google Play): https://bit.ly/2MlvP5w 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, Medium, 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

14 Juli 20201h 37min

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: - MasterClass: https://masterclass.com/lex - Cash App – use code "LexPodcast" and download: - Cash App (App Store): https://apple.co/2sPrUHe - Cash App (Google Play): https://bit.ly/2MlvP5w 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, Medium, 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

8 Juli 20201h 9min

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. Support this podcast by supporting these sponsors: - The Jordan Harbinger Show: https://www.jordanharbinger.com/lex - Magic Spoon: https://magicspoon.com/lex and use code LEX at checkout 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, Medium, 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?

3 Juli 20202h 1min

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: - MasterClass: https://masterclass.com/lex - Cash App – use code "LexPodcast" and download: - Cash App (App Store): https://apple.co/2sPrUHe - Cash App (Google Play): https://bit.ly/2MlvP5w This conversation is part of the Artificial Intelligence podcast. 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, Medium, 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?

30 Juni 20201h 2min