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

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

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

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?

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?

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/ – 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 03:28 – 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/ – MasterClass: https://masterclass.com/lex 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 03:20 – 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

Steven Pressfield is a historian and author of War of Art, a book that had a big impact on my life and the life of millions of whose passion is to create in art, science, business, sport, and everywhere else. I highly recommend it and others of his books on this topic, including Turning Pro, Do the Work, Nobody Wants to Read Your Shit, and the Warrior Ethos. Also his books Gates of Fire about the Spartans and the battle at Thermopylae, The Lion’s Gate, Tides of War, and others are some of the best historical fiction novels ever written. Support this podcast by supporting these sponsors: – Jordan Harbinger Show: https://jordanharbinger.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 05:00 – Nature of war 11:43 – The struggle within 17:11 – Love and hate in a time of war 25:17 – Future of warfare 28:31 – Technology in war 30:10 – What it takes to kill a person 32:22 – Mortality 37:30 – The muse 46:09 – Editing 52:19 – Resistance 1:10:41 – Loneliness 1:12:24 – Is a warrior born or trained? 1:13:53 – Hard work and health 1:18:41 – Daily ritual

Karl Friston is one of the greatest neuroscientists in history, cited over 245,000 times, known for many influential ideas in brain imaging, neuroscience, and theoretical neurobiology, including the fascinating idea of the free-energy principle for action and perception. Support this podcast by signing up with these sponsors: – Cash App – use code “LexPodcast” and download: – Cash App (App Store): https://apple.co/2sPrUHe – Cash App (Google Play): https://bit.ly/2MlvP5w EPISODE LINKS: Karl’s Website: https://www.fil.ion.ucl.ac.uk/~karl/ Karl’s Wiki: https://en.wikipedia.org/wiki/Karl_J._Friston 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 01:50 – How much of the human brain do we understand? 05:53 – Most beautiful characteristic of the human brain 10:43 – Brain imaging 20:38 – Deep structure 21:23 – History of brain imaging 32:31 – Neuralink and brain-computer interfaces 43:05 – Free energy principle 1:24:29 – Meaning of life

Sertac Karaman is a professor at MIT, co-founder of the autonomous vehicle company Optimus Ride, and is one of top roboticists in the world, including robots that drive and robots that fly. Support this podcast by signing up with these sponsors: – Cash App – use code “LexPodcast” and download: – Cash App (App Store): https://apple.co/2sPrUHe – Cash App (Google Play): https://bit.ly/2MlvP5w EPISODE LINKS: Sertac’s Website: http://sertac.scripts.mit.edu/web/ Sertac’s Twitter: https://twitter.com/sertackaraman Optimus Ride: https://www.optimusride.com/ 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 01:44 – Autonomous flying vs autonomous driving 06:37 – Flying cars 10:27 – Role of simulation in robotics 17:35 – Game theory and robotics 24:30 – Autonomous vehicle company strategies 29:46 – Optimus Ride 47:08 – Waymo, Tesla, Optimus Ride timelines 53:22 – Achieving the impossible 53:50 – Iterative learning 58:39 – Is Lidar is a crutch? 1:03:21 – Fast autonomous flight 1:18:06 – Most beautiful idea in robotics

Dawn Song is a professor of computer science at UC Berkeley with research interests in security, most recently with a focus on the intersection between computer security and machine learning. Support this podcast by signing up with these sponsors: – Cash App – use code “LexPodcast” and download: – Cash App (App Store): https://apple.co/2sPrUHe – Cash App (Google Play): https://bit.ly/2MlvP5w EPISODE LINKS: Dawn’s Twitter: https://twitter.com/dawnsongtweets Dawn’s Website: https://people.eecs.berkeley.edu/~dawnsong/ Oasis Labs: https://www.oasislabs.com 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 01:53 – Will software always have security vulnerabilities? 09:06 – Human are the weakest link in security 16:50 – Adversarial machine learning 51:27 – Adversarial attacks on Tesla Autopilot and self-driving cars 57:33 – Privacy attacks 1:05:47 – Ownership of data 1:22:13 – Blockchain and cryptocurrency 1:32:13 – Program synthesis 1:44:57 – A journey from physics to computer science 1:56:03 – US and China 1:58:19 – Transformative moment 2:00:02 – Meaning of life

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. Support this podcast by signing up with these sponsors: – Cash App – use code “LexPodcast” and download: – Cash App (App Store): https://apple.co/2sPrUHe – Cash App (Google Play): https://bit.ly/2MlvP5w EPISODE LINKS: Ilya’s Twitter: https://twitter.com/ilyasut Ilya’s Website: https://www.cs.toronto.edu/~ilya/ 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 02:23 – AlexNet paper and the ImageNet moment 08:33 – 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