Noam Chomsky is one of the greatest minds of our time and is one of the most cited scholars in history. He is a linguist, philosopher, cognitive scientist, historian, social critic, and political activist. He has spent over 60 years at MIT and recently also joined the University of Arizona. 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 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 03:59 – Common language with an alience species 05:46 – Structure of language 07:18 – Roots of language in our brain 08:51 – Language and thought 09:44 – The limit of human cognition 16:48 – Neuralink 19:32 – Deepest property of language 22:13 – Limits of deep learning 28:01 – Good and evil 29:52 – Memorable experiences 33:29 – Mortality 34:23 – Meaning of life

Gilbert Strang is a professor of mathematics at MIT and perhaps one of the most famous and impactful teachers of math in the world. His MIT OpenCourseWare lectures on linear algebra have been viewed millions of 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 or support it on Patreon. This episode is presented by Cash App. Download it, use code LexPodcast. And it is supported by ZipRecruiter. Try it: http://ziprecruiter.com/lexpod 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 – Math rockstar 05:10 – MIT OpenCourseWare 07:29 – Four Fundamental Subspaces of Linear Algebra 13:11 – Linear Algebra vs Calculus 15:03 – Singular value decomposition 19:47 – Why people like math 23:38 – Teaching by example 25:04 – Andrew Yang 26:46 – Society for Industrial and Applied Mathematics 29:21 – Deep learning 37:28 – Theory vs application 38:54 – Open problems in mathematics 39:00 – Linear algebra as a subfield of mathematics 41:52 – Favorite matrix 46:19 – Advice for students on their journey through math 47:37 – Looking back

Dava Newman is the Apollo Program professor of AeroAstro at MIT and the former Deputy Administrator of NASA and has been a principal investigator on four spaceflight missions. Her research interests are in aerospace biomedical engineering, investigating human performance in varying gravity environments. She has developed a space activity suit, namely the BioSuit, which would provide pressure through compression directly on the skin via the suit’s textile weave, patterning, and materials rather than with pressurized gas. 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 or support it on Patreon. This episode is presented by Cash App. Download it, use code LexPodcast. You get $10 and $10 is donated to FIRST, one of my favorite nonprofit organizations that inspires young minds through robotics and STEM education. 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 03:11 – Circumnavigating the globe by boat 05:11 – Exploration 07:17 – Life on Mars 11:07 – Intelligent life in the universe 12:25 – Advanced propulsion technology 13:32 – The Moon and NASA’s Artemis program 19:17 – SpaceX 21:45 – Science on a CubeSat 23:45 – Reusable rockets 25:23 – Spacesuit of the future 32:01 – AI in Space 35:31 – Interplanetary species 36:57 – Future of space exploration

Michael Kearns is a professor at University of Pennsylvania and a co-author of the new book Ethical Algorithm that is the focus of much of our conversation, including algorithmic fairness, bias, privacy, and ethics in general. But, that is just one of many fields that Michael is a world-class researcher in, some of which we touch on quickly including learning theory or theoretical foundations of machine learning, game theory, algorithmic trading, quantitative finance, computational social science, and more. 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 or support it on Patreon. This episode is sponsored by Pessimists Archive podcast. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode): 00:00 – Introduction 02:45 – Influence from literature and journalism 07:39 – Are most people good? 13:05 – Ethical algorithm 24:28 – Algorithmic fairness of groups vs individuals 33:36 – Fairness tradeoffs 46:29 – Facebook, social networks, and algorithmic ethics 58:04 – Machine learning 58:05 – Machine learning 59:19 – Algorithm that determines what is fair 1:01:25 – Computer scientists should think about ethics 1:05:59 – Algorithmic privacy 1:11:50 – Differential privacy 1:19:10 – Privacy by misinformation 1:22:31 – Privacy of data in society 1:27:49 – Game theory 1:29:40 – Nash equilibrium 1:30:35 – Machine learning and game theory 1:34:52 – Mutual assured destruction 1:36:56 – Algorithmic trading 1:44:09 – Pivotal moment in graduate school

Elon Musk is the CEO of Tesla, SpaceX, Neuralink, and a co-founder of several other companies. This is the second time Elon has been on the podcast. You can watch the first time on YouTube or listen to the first time on its episode page. You can read the transcript (PDF) here. 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 or support it on Patreon. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode): 00:00 – Introduction 01:57 – Consciousness 05:58 – Regulation of AI Safety 09:39 – Neuralink – understanding the human brain 11:53 – Neuralink – expanding the capacity of the human mind 17:51 – Neuralink – future challenges, solutions, and impact 24:59 – Smart Summon 27:18 – Tesla Autopilot and Full Self-Driving 31:16 – Carl Sagan and the Pale Blue Dot

Bjarne Stroustrup is the creator of C++, a programming language that after 40 years is still one of the most popular and powerful languages in the world. Its focus on fast, stable, robust code underlies many of the biggest systems in the world that we have come to rely on as a society. If you’re watching this on YouTube, many of the critical back-end component of YouTube are written in C++. Same goes for Google, Facebook, Amazon, Twitter, most Microsoft applications, Adobe applications, most database systems, and most physical systems that operate in the real-world like cars, robots, rockets that launch us into space and one day will land us on Mars. 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 or support it on Patreon. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode): 00:00 – Introduction 01:40 – First program 02:18 – Journey to C++ 16:45 – Learning multiple languages 23:20 – Javascript 25:08 – Efficiency and reliability in C++ 31:53 – What does good code look like? 36:45 – Static checkers 41:16 – Zero-overhead principle in C++ 50:00 – Different implementation of C++ 54:46 – Key features of C++ 1:08:02 – C++ Concepts 1:18:06 – C++ Standards Process 1:28:05 – Constructors and destructors 1:31:52 – Unified theory of programming 1:44:20 – Proudest moment

Sean Carroll is a theoretical physicist at Caltech and Santa Fe Institute specializing in quantum mechanics, arrow of time, cosmology, and gravitation. He is the author of Something Deeply Hidden and several popular books and he is the host of a great podcast called Mindscape. This is the second time Sean has been on the podcast. You can watch the first time on YouTube or listen to the first time on its episode page. 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 or support it on Patreon. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode): 00:00 – Introduction 01:23 – Capacity of human mind to understand physics 10:49 – Perception vs reality 12:29 – Conservation of momentum 17:20 – Difference between math and physics 20:10 – Why is our world so compressable 22:53 – What would Newton think of quantum mechanics 25:44 – What is quantum mechanics? 27:54 – What is an atom? 30:34 – What is the wave function? 32:30 – What is quantum entanglement? 35:19 – What is Hilbert space? 37:32 – What is entropy? 39:31 – Infinity 42:43 – Many-worlds interpretation of quantum mechanics 1:01:13 – Quantum gravity and the emergence of spacetime 1:08:34 – Our branch of reality in many-worlds interpretation 1:10:40 – Time travel 1:12:54 – Arrow of time 1:16:18 – What is fundamental in physics 1:16:58 – Quantum computers 1:17:42 – Experimental validation of many-worlds and emergent spacetime 1:19:53 – Quantum mechanics and the human mind 1:21:51 – Mindscape podcast

Garry Kasparov is considered by many to be the greatest chess player of all time. From 1986 until his retirement in 2005, he dominated the chess world, ranking world number 1 for most of those 19 years. While he has many historic matches against human chess players, in the long arc of history he may be remembered for his match again a machine, IBM’s Deep Blue. His initial victories and eventual loss to Deep Blue captivated the imagination of the world of what role Artificial Intelligence systems may play in our civilization’s future. That excitement inspired an entire generation of AI researchers, including myself, to get into the field. Garry is also a pro-democracy political thinker and leader, a fearless human-rights activist, and author of several books including How Life Imitates Chess which is a book on strategy and decision-making, Winter Is Coming which is a book articulating his opposition to the Putin regime, and Deep Thinking which is a book the role of both artificial intelligence and human intelligence in defining our future. 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 or support it on Patreon. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode): 00:00 – Introduction 01:33 – Love of winning and hatred of losing 04:54 – Psychological elements 09:03 – Favorite games 16:48 – Magnus Carlsen 23:06 – IBM Deep Blue 37:39 – Morality 38:59 – Autonomous vehicles 42:03 – Fall of the Soviet Union 45:50 – Putin 52:25 – Life

Michio Kaku is a theoretical physicist, futurist, and professor at the City College of New York. He is the author of many fascinating books on the nature of our reality and the future of our civilization. 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 or support it on Patreon. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode): 00:00 – Introduction 01:14 – Contact with Aliens in the 21st century 06:36 – Multiverse and Nirvana 09:46 – String Theory 11:07 – Einstein’s God 15:01 – Would aliens hurt us? 17:34 – What would aliens look like? 22:13 – Brain-machine interfaces 27:35 – Existential risk from AI 30:22 – Digital immortality 34:02 – Biological immortality 37:42 – Does mortality give meaning? 43:42 – String theory 47:16 – Universe as a computer and a simulation 53:16 – First human on Mars

David Ferrucci led the team that built Watson, the IBM question-answering system that beat the top humans in the world at the game of Jeopardy. He is also the Founder, CEO, and Chief Scientist of Elemental Cognition, a company working engineer AI systems that understand the world the way people do. 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 iTunes or support it on Patreon. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode): 00:00 – Introduction 01:06 – Biological vs computer systems 08:03 – What is intelligence? 31:49 – Knowledge frameworks 52:02 – IBM Watson winning Jeopardy 1:24:21 – Watson vs human difference in approach 1:27:52 – Q&A vs dialogue 1:35:22 – Humor 1:41:33 – Good test of intelligence 1:46:36 – AlphaZero, AlphaStar accomplishments 1:51:29 – Explainability, induction, deduction in medical diagnosis 1:59:34 – Grand challenges 2:04:03 – Consciousness 2:08:26 – Timeline for AGI 2:13:55 – Embodied AI 2:17:07 – Love and companionship 2:18:06 – Concerns about AI 2:21:56 – Discussion with AGI

Gary Marcus is a professor emeritus at NYU, founder of Robust.AI and Geometric Intelligence, the latter is a machine learning company acquired by Uber in 2016. He is the author of several books on natural and artificial intelligence, including his new book Rebooting AI: Building Machines We Can Trust. Gary has been a critical voice highlighting the limits of deep learning and discussing the challenges before the AI community that must be solved in order to achieve artificial general intelligence. 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 iTunes or support it on Patreon. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode): 00:00 – Introduction 01:37 – Singularity 05:48 – Physical and psychological knowledge 10:52 – Chess 14:32 – Language vs physical world 17:37 – What does AI look like 100 years from now 21:28 – Flaws of the human mind 25:27 – General intelligence 28:25 – Limits of deep learning 44:41 – Expert systems and symbol manipulation 48:37 – Knowledge representation 52:52 – Increasing compute power 56:27 – How human children learn 57:23 – Innate knowledge and learned knowledge 1:06:43 – Good test of intelligence 1:12:32 – Deep learning and symbol manipulation 1:23:35 – Guitar

Peter Norvig is a research director at Google and the co-author with Stuart Russell of the book Artificial Intelligence: A Modern Approach that educated and inspired a whole generation of researchers including myself to get into the field. 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 iTunes or support it on Patreon. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode): 00:00 – Introduction 00:37 – Artificial Intelligence: A Modern Approach 09:11 – Covering the entire field of AI 15:42 – Expert systems and knowledge representation 18:31 – Explainable AI 23:15 – Trust 25:47 – Education – Intro to AI – MOOC 32:43 – Learning to program in 10 years 37:12 – Changing nature of mastery 40:01 – Code review 41:17 – How have you changed as a programmer 43:05 – LISP 47:41 – Python 48:32 – Early days of Google Search 53:24 – What does it take to build human-level intelligence 55:14 – Her 57:00 – Test of intelligence 58:41 – Future threats from AI 1:00:58 – Exciting open problems in AI