Anca Dragan is a professor at Berkeley, working on human-robot interaction -- algorithms that look beyond the robot's function in isolation, and generate robot behavior that accounts for interaction and coordination with human beings. Support this podcast by supporting the sponsors and using the special code: - Download Cash App on the App Store or Google Play & use code "LexPodcast"  EPISODE LINKS: Anca's Twitter: https://twitter.com/ancadianadragan Anca's Website: https://people.eecs.berkeley.edu/~anca/ 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 02:26 - Interest in robotics 05:32 - Computer science 07:32 - Favorite robot 13:25 - How difficult is human-robot interaction? 32:01 - HRI application domains 34:24 - Optimizing the beliefs of humans 45:59 - Difficulty of driving when humans are involved 1:05:02 - Semi-autonomous driving 1:10:39 - How do we specify good rewards? 1:17:30 - Leaked information from human behavior 1:21:59 - Three laws of robotics 1:26:31 - Book recommendation 1:29:02 - If a doctor gave you 5 years to live... 1:32:48 - Small act of kindness 1:34:31 - Meaning of life

Vitalik Buterin is co-creator of Ethereum and ether, which is a cryptocurrency that is currently the second-largest digital currency after bitcoin. Ethereum has a lot of interesting technical ideas that are defining the future of blockchain technology, and Vitalik is one of the most brilliant people innovating this space today. Support this podcast by supporting the sponsors with a special code: - Get ExpressVPN at https://www.expressvpn.com/lexpod - Sign up to MasterClass at https://masterclass.com/lex EPISODE LINKS: Vitalik blog: https://vitalik.ca Ethereum whitepaper: http://bit.ly/3cVDTpj Casper FFG (paper): http://bit.ly/2U6j7dJ Quadratic funding (paper): http://bit.ly/3aUZ8Wd Bitcoin whitepaper: https://bitcoin.org/bitcoin.pdf Mastering Ethereum (book): https://amzn.to/2xEjWmE 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 04:43 - Satoshi Nakamoto 08:40 - Anonymity 11:31 - Open source project leadership 13:04 - What is money? 30:02 - Blockchain and cryptocurrency basics 46:51 - Ethereum 59:23 - Proof of work 1:02:12 - Ethereum 2.0 1:13:09 - Beautiful ideas in Ethereum 1:16:59 - Future of cryptocurrency 1:22:06 - Cryptocurrency resources and people to follow 1:24:28 - Role of governments 1:27:27 - Meeting Putin 1:29:41 - Large number of cryptocurrencies 1:32:49 - Mortality

Lee Smolin is a theoretical physicist, co-inventor of loop quantum gravity, and a contributor of many interesting ideas to cosmology, quantum field theory, the foundations of quantum mechanics, theoretical biology, and the philosophy of science. He is the author of several books including one that critiques the state of physics and string theory called The Trouble with Physics, and his latest book, Einstein's Unfinished Revolution: The Search for What Lies Beyond the Quantum. EPISODE LINKS: Books mentioned: - Einstein's Unfinished Revolution by Lee Smolin: https://amzn.to/2TsF5c3 - The Trouble With Physics by Lee Smolin: https://amzn.to/2v1FMzy - Against Method by Paul Feyerabend: https://amzn.to/2VOPXCD 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  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 03:03 - What is real? 05:03 - Scientific method and scientific progress 24:57 - Eric Weinstein and radical ideas in science 29:32 - Quantum mechanics and general relativity 47:24 - Sean Carroll and many-worlds interpretation of quantum mechanics 55:33 - Principles in science 57:24 - String theory

Ann Druyan is the writer, producer, director, and one of the most important and impactful communicators of science in our time. She co-wrote the 1980 science documentary series Cosmos hosted by Carl Sagan, whom she married in 1981, and her love for whom, with the help of NASA, was recorded as brain waves on a golden record along with other things our civilization has to offer and launched into space on the Voyager 1 and Voyager 2 spacecraft that are now, 42 years later, still active, reaching out farther into deep space than any human-made object ever has. This was a profound and beautiful decision she made as a Creative Director of NASA's Voyager Interstellar Message Project. In 2014, she went on to create the second season of Cosmos, called Cosmos: A Spacetime Odyssey, and in 2020, the new third season called Cosmos: Possible Worlds, which is being released this upcoming Monday, March 9. It is hosted, once again, by the fun and brilliant Neil deGrasse Tyson. EPISODE LINKS: Cosmos Twitter: https://twitter.com/COSMOSonTV Cosmos Website: https://fox.tv/CosmosOnTV 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  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 03:24 - Role of science in society 07:04 - Love and science 09:07 - Skepticism in science 14:15 - Voyager, Carl Sagan, and the Golden Record 36:41 - Cosmos 53:22 - Existential threats 1:00:36 - Origin of life 1:04:22 - Mortality

Alex Garland is a writer and director of many imaginative and philosophical films from the dreamlike exploration of human self-destruction in the movie Annihilation to the deep questions of consciousness and intelligence raised in the movie Ex Machina, which to me is one of the greatest movies on artificial intelligence ever made. I'm releasing this podcast to coincide with the release of his new series called Devs that will premiere this Thursday, March 5, on Hulu. EPISODE LINKS: Devs: https://hulu.tv/2x35HaH Annihilation: https://hulu.tv/3ai9Eqk Ex Machina: https://www.netflix.com/title/80023689 Alex IMDb: https://www.imdb.com/name/nm0307497/ Alex Wiki: https://en.wikipedia.org/wiki/Alex_Garland 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  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 03:42 - Are we living in a dream? 07:15 - Aliens 12:34 - Science fiction: imagination becoming reality 17:29 - Artificial intelligence 22:40 - The new "Devs" series and the veneer of virtue in Silicon Valley 31:50 - Ex Machina and 2001: A Space Odyssey 44:58 - Lone genius 49:34 - Drawing inpiration from Elon Musk 51:24 - Space travel 54:03 - Free will 57:35 - Devs and the poetry of science 1:06:38 - What will you be remembered for?

John Hopfield is professor at Princeton, whose life's work weaved beautifully through biology, chemistry, neuroscience, and physics. Most crucially, he saw the messy world of biology through the piercing eyes of a physicist. He is perhaps best known for his work on associate neural networks, now known as Hopfield networks that were one of the early ideas that catalyzed the development of the modern field of deep learning. EPISODE LINKS: Now What? article: http://bit.ly/3843LeU John wikipedia: https://en.wikipedia.org/wiki/John_Hopfield Books mentioned: - Einstein's Dreams: https://amzn.to/2PBa96X - Mind is Flat: https://amzn.to/2I3YB84 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  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 02:35 - Difference between biological and artificial neural networks 08:49 - Adaptation 13:45 - Physics view of the mind 23:03 - Hopfield networks and associative memory 35:22 - Boltzmann machines 37:29 - Learning 39:53 - Consciousness 48:45 - Attractor networks and dynamical systems 53:14 - How do we build intelligent systems? 57:11 - Deep thinking as the way to arrive at breakthroughs 59:12 - Brain-computer interfaces 1:06:10 - Mortality 1:08:12 - Meaning of life

Marcus Hutter is a senior research scientist at DeepMind and professor at Australian National University. Throughout his career of research, including with Jürgen Schmidhuber and Shane Legg, he has proposed a lot of interesting ideas in and around the field of artificial general intelligence, including the development of the AIXI model which is a mathematical approach to AGI that incorporates ideas of Kolmogorov complexity, Solomonoff induction, and reinforcement learning. EPISODE LINKS: Hutter Prize: http://prize.hutter1.net Marcus web: http://www.hutter1.net Books mentioned: - Universal AI: https://amzn.to/2waIAuw - AI: A Modern Approach: https://amzn.to/3camxnY - Reinforcement Learning: https://amzn.to/2PoANj9 - Theory of Knowledge: https://amzn.to/3a6Vp7x 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  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 03:32 - Universe as a computer 05:48 - Occam's razor 09:26 - Solomonoff induction 15:05 - Kolmogorov complexity 20:06 - Cellular automata 26:03 - What is intelligence? 35:26 - AIXI - Universal Artificial Intelligence 1:05:24 - Where do rewards come from? 1:12:14 - Reward function for human existence 1:13:32 - Bounded rationality 1:16:07 - Approximation in AIXI 1:18:01 - Godel machines 1:21:51 - Consciousness 1:27:15 - AGI community 1:32:36 - Book recommendations 1:36:07 - Two moments to relive (past and future)

Michael I. Jordan is a professor at Berkeley, and one of the most influential people in the history of machine learning, statistics, and artificial intelligence. He has been cited over 170,000 times and has mentored many of the world-class researchers defining the field of AI today, including Andrew Ng, Zoubin Ghahramani, Ben Taskar, and Yoshua Bengio. EPISODE LINKS: (Blog post) Artificial Intelligence—The Revolution Hasn’t Happened Yet 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  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 03:02 - How far are we in development of AI? 08:25 - Neuralink and brain-computer interfaces 14:49 - The term "artificial intelligence" 19:00 - Does science progress by ideas or personalities? 19:55 - Disagreement with Yann LeCun 23:53 - Recommender systems and distributed decision-making at scale 43:34 - Facebook, privacy, and trust 1:01:11 - Are human beings fundamentally good? 1:02:32 - Can a human life and society be modeled as an optimization problem? 1:04:27 - Is the world deterministic? 1:04:59 - Role of optimization in multi-agent systems 1:09:52 - Optimization of neural networks 1:16:08 - Beautiful idea in optimization: Nesterov acceleration 1:19:02 - What is statistics? 1:29:21 - What is intelligence? 1:37:01 - Advice for students 1:39:57 - Which language is more beautiful: English or French?

Andrew Ng is one of the most impactful educators, researchers, innovators, and leaders in artificial intelligence and technology space in general. He co-founded Coursera and Google Brain, launched deeplearning.ai, Landing.ai, and the AI fund, and was the Chief Scientist at Baidu. As a Stanford professor, and with Coursera and deeplearning.ai, he has helped educate and inspire millions of students including me. EPISODE LINKS: Andrew Twitter: https://twitter.com/AndrewYNg Andrew Facebook: https://www.facebook.com/andrew.ng.96 Andrew LinkedIn: https://www.linkedin.com/in/andrewyng/ deeplearning.ai: https://www.deeplearning.ai landing.ai: https://landing.ai AI Fund: https://aifund.ai/ AI for Everyone: https://www.coursera.org/learn/ai-for-everyone The Batch newsletter: https://www.deeplearning.ai/thebatch/ 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  This episode is also supported by the Techmeme Ride Home podcast. Get it on Apple Podcasts, on its website, or find it by searching "Ride Home" in your podcast app. 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 - First few steps in AI 05:05 - Early days of online education 16:07 - Teaching on a whiteboard 17:46 - Pieter Abbeel and early research at Stanford 23:17 - Early days of deep learning 32:55 - Quick preview: deeplearning.ai, landing.ai, and AI fund 33:23 - deeplearning.ai: how to get started in deep learning 45:55 - Unsupervised learning 49:40 - deeplearning.ai (continued) 56:12 - Career in deep learning 58:56 - Should you get a PhD? 1:03:28 - AI fund - building startups 1:11:14 - Landing.ai - growing AI efforts in established companies 1:20:44 - Artificial general intelligence

Scott Aaronson is a professor at UT Austin, director of its Quantum Information Center, and previously a professor at MIT. His research interests center around the capabilities and limits of quantum computers and computational complexity theory more generally. 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  This episode is also supported by the Techmeme Ride Home podcast. Get it on Apple Podcasts, on its website, or find it by searching "Ride Home" in your podcast app. Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 - Introduction 05:07 - Role of philosophy in science 29:27 - What is a quantum computer? 41:12 - Quantum decoherence (noise in quantum information) 49:22 - Quantum computer engineering challenges 51:00 - Moore's Law 56:33 - Quantum supremacy 1:12:18 - Using quantum computers to break cryptography 1:17:11 - Practical application of quantum computers 1:22:18 - Quantum machine learning, questionable claims, and cautious optimism 1:30:53 - Meaning of life

Vladimir Vapnik is the co-inventor of support vector machines, support vector clustering, VC theory, and many foundational ideas in statistical learning. He was born in the Soviet Union, worked at the Institute of Control Sciences in Moscow, then in the US, worked at AT&T;, NEC Labs, Facebook AI Research, and now is a professor at Columbia University. His work has been cited over 200,000 times. 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 - Introduction 02:55 - Alan Turing: science and engineering of intelligence 09:09 - What is a predicate? 14:22 - Plato's world of ideas and world of things 21:06 - Strong and weak convergence 28:37 - Deep learning and the essence of intelligence 50:36 - Symbolic AI and logic-based systems 54:31 - How hard is 2D image understanding? 1:00:23 - Data 1:06:39 - Language 1:14:54 - Beautiful idea in statistical theory of learning 1:19:28 - Intelligence and heuristics 1:22:23 - Reasoning 1:25:11 - Role of philosophy in learning theory 1:31:40 - Music (speaking in Russian) 1:35:08 - Mortality

Jim Keller is a legendary microprocessor engineer, having worked at AMD, Apple, Tesla, and now Intel. He's known for his work on the AMD K7, K8, K12 and Zen microarchitectures, Apple A4, A5 processors, and co-author of the specifications for the x86-64 instruction set and HyperTransport interconnect. 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 - Introduction 02:12 - Difference between a computer and a human brain 03:43 - Computer abstraction layers and parallelism 17:53 - If you run a program multiple times, do you always get the same answer? 20:43 - Building computers and teams of people 22:41 - Start from scratch every 5 years 30:05 - Moore's law is not dead 55:47 - Is superintelligence the next layer of abstraction? 1:00:02 - Is the universe a computer? 1:03:00 - Ray Kurzweil and exponential improvement in technology 1:04:33 - Elon Musk and Tesla Autopilot 1:20:51 - Lessons from working with Elon Musk 1:28:33 - Existential threats from AI 1:32:38 - Happiness and the meaning of life

David Chalmers is a philosopher and cognitive scientist specializing in philosophy of mind, philosophy of language, and consciousness. He is perhaps best known for formulating the hard problem of consciousness which could be stated as "why does the feeling which accompanies awareness of sensory information exist at all?" 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 - Introduction 02:23 - Nature of reality: Are we living in a simulation? 19:19 - Consciousness in virtual reality 27:46 - Music-color synesthesia 31:40 - What is consciousness? 51:25 - Consciousness and the meaning of life 57:33 - Philosophical zombies 1:01:38 - Creating the illusion of consciousness 1:07:03 - Conversation with a clone 1:11:35 - Free will 1:16:35 - Meta-problem of consciousness 1:18:40 - Is reality an illusion? 1:20:53 - Descartes' evil demon 1:23:20 - Does AGI need conscioussness? 1:33:47 - Exciting future 1:35:32 - Immortality

Cristos Goodrow is VP of Engineering at Google and head of Search and Discovery at YouTube (aka YouTube Algorithm). 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 - Introduction 03:26 - Life-long trajectory through YouTube 07:30 - Discovering new ideas on YouTube 13:33 - Managing healthy conversation 23:02 - YouTube Algorithm 38:00 - Analyzing the content of video itself 44:38 - Clickbait thumbnails and titles 47:50 - Feeling like I'm helping the YouTube algorithm get smarter 50:14 - Personalization 51:44 - What does success look like for the algorithm? 54:32 - Effect of YouTube on society 57:24 - Creators 59:33 - Burnout 1:03:27 - YouTube algorithm: heuristics, machine learning, human behavior 1:08:36 - How to make a viral video? 1:10:27 - Veritasium: Why Are 96,000,000 Black Balls on This Reservoir? 1:13:20 - Making clips from long-form podcasts 1:18:07 - Moment-by-moment signal of viewer interest 1:20:04 - Why is video understanding such a difficult AI problem? 1:21:54 - Self-supervised learning on video 1:25:44 - What does YouTube look like 10, 20, 30 years from now?

Paul Krugman is a Nobel Prize winner in economics, professor at CUNY, and columnist at the New York Times. His academic work centers around international economics, economic geography, liquidity traps, and currency crises. 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 - Introduction 03:44 - Utopia from an economics perspective 04:51 - Competition 06:33 - Well-informed citizen 07:52 - Disagreements in economics 09:57 - Metrics of outcomes 13:00 - Safety nets 15:54 - Invisible hand of the market 21:43 - Regulation of tech sector 22:48 - Automation 25:51 - Metric of productivity 30:35 - Interaction of the economy and politics 33:48 - Universal basic income 36:40 - Divisiveness of political discourse 42:53 - Economic theories 52:25 - Starting a system on Mars from scratch 55:11 - International trade 59:08 - Writing in a time of radicalization and Twitter mobs

Ayanna Howard is a roboticist and professor at Georgia Tech, director of Human-Automation Systems lab, with research interests in human-robot interaction, assistive robots in the home, therapy gaming apps, and remote robotic exploration of extreme environments. 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 - Introduction 02:09 - Favorite robot 05:05 - Autonomous vehicles 08:43 - Tesla Autopilot 20:03 - Ethical responsibility of safety-critical algorithms 28:11 - Bias in robotics 38:20 - AI in politics and law 40:35 - Solutions to bias in algorithms 47:44 - HAL 9000 49:57 - Memories from working at NASA 51:53 - SpotMini and Bionic Woman 54:27 - Future of robots in space 57:11 - Human-robot interaction 1:02:38 - Trust 1:09:26 - AI in education 1:15:06 - Andrew Yang, automation, and job loss 1:17:17 - Love, AI, and the movie Her 1:25:01 - Why do so many robotics companies fail? 1:32:22 - Fear of robots 1:34:17 - Existential threats of AI 1:35:57 - Matrix 1:37:37 - Hang out for a day with a robot

Daniel Kahneman is winner of the Nobel Prize in economics for his integration of economic science with the psychology of human behavior, judgment and decision-making. He is the author of the popular book "Thinking, Fast and Slow" that summarizes in an accessible way his research of several decades, often in collaboration with Amos Tversky, on cognitive biases, prospect theory, and happiness. The central thesis of this work is a dichotomy between two modes of thought: "System 1" is fast, instinctive and emotional; "System 2" is slower, more deliberative, and more logical. The book delineates cognitive biases associated with each type of thinking. 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 - Introduction 02:36 - Lessons about human behavior from WWII 08:19 - System 1 and system 2: thinking fast and slow 15:17 - Deep learning 30:01 - How hard is autonomous driving? 35:59 - Explainability in AI and humans 40:08 - Experiencing self and the remembering self 51:58 - Man's Search for Meaning by Viktor Frankl 54:46 - How much of human behavior can we study in the lab? 57:57 - Collaboration 1:01:09 - Replication crisis in psychology 1:09:28 - Disagreements and controversies in psychology 1:13:01 - Test for AGI 1:16:17 - Meaning of life

Grant Sanderson is a math educator and creator of 3Blue1Brown, a popular YouTube channel that uses programmatically-animated visualizations to explain concepts in linear algebra, calculus, and other fields of mathematics. 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 - Introduction 01:56 - What kind of math would aliens have? 03:48 - Euler's identity and the least favorite piece of notation 10:31 - Is math discovered or invented? 14:30 - Difference between physics and math 17:24 - Why is reality compressible into simple equations? 21:44 - Are we living in a simulation? 26:27 - Infinity and abstractions 35:48 - Most beautiful idea in mathematics 41:32 - Favorite video to create 45:04 - Video creation process 50:04 - Euler identity 51:47 - Mortality and meaning 55:16 - How do you know when a video is done? 56:18 - What is the best way to learn math for beginners? 59:17 - Happy moment

Stephen Kotkin is a professor of history at Princeton university and one of the great historians of our time, specializing in Russian and Soviet history. He has written many books on Stalin and the Soviet Union including the first 2 of a 3 volume work on Stalin, and he is currently working on volume 3. 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  Episode Links: Stalin (book, vol 1): https://amzn.to/2FjdLF2 Stalin (book, vol 2): https://amzn.to/2tqyjc3 Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 - Introduction 03:10 - Do all human beings crave power? 11:29 - Russian people and authoritarian power 15:06 - Putin and the Russian people 23:23 - Corruption in Russia 31:30 - Russia's future 41:07 - Individuals and institutions 44:42 - Stalin's rise to power 1:05:20 - What is the ideal political system? 1:21:10 - Questions for Putin 1:29:41 - Questions for Stalin 1:33:25 - Will there always be evil in the world?

Donald Knuth is one of the greatest and most impactful computer scientists and mathematicians ever. He is the recipient in 1974 of the Turing Award, considered the Nobel Prize of computing. He is the author of the multi-volume work, the magnum opus, The Art of Computer Programming. He made several key contributions to the rigorous analysis of the computational complexity of algorithms. He popularized asymptotic notation, that we all affectionately know as the big-O notation. He also created the TeX typesetting which most computer scientists, physicists, mathematicians, and scientists and engineers use to write technical papers and make them look beautiful. 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. This episode is presented by Cash App. Download it (App Store, Google Play), use code "LexPodcast".  Episode Links: The Art of Computer Programming (book set) Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. 00:00 - Introduction 03:45 - IBM 650 07:51 - Geeks 12:29 - Alan Turing 14:26 - My life is a convex combination of english and mathematics 24:00 - Japanese arrow puzzle example 25:42 - Neural networks and machine learning 27:59 - The Art of Computer Programming 36:49 - Combinatorics 39:16 - Writing process 42:10 - Are some days harder than others? 48:36 - What's the "Art" in the Art of Computer Programming 50:21 - Binary (boolean) decision diagram 55:06 - Big-O notation 58:02 - P=NP 1:10:05 - Artificial intelligence 1:13:26 - Ant colonies and human cognition 1:17:11 - God and the Bible 1:24:28 - Reflection on life 1:28:25 - Facing mortality 1:33:40 - TeX and beautiful typography 1:39:23 - How much of the world do we understand? 1:44:17 - Question for God