Podcast
Lex Fridman Podcast
Hosted by Lex Fridman · EN
Conversations that explore technology, history, philosophy, physics, mathematics, biology, chemistry, engineering, AI, robotics, programming, music, film, art, sports, psychology, neuroscience, geopolitics, business, economics, religion, astronomy, and the human condition with people from all walks of life.
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#103 – Ben Goertzel: Artificial General Intelligence
Jun 22, 2020Tap to summarizeBen 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
Transcribe →#93 – Daphne Koller: Biomedicine and Machine Learning
May 5, 2020Tap to summarizeDaphne Koller is a professor of computer science at Stanford University, a co-founder of Coursera with Andrew Ng and Founder and CEO of insitro, a company at the intersection of machine learning and biomedicine. 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: Daphne’s Twitter: https://twitter.com/daphnekoller Daphne’s Website: https://ai.stanford.edu/users/koller/index.html Insitro: http://insitro.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 02:22 – Will we one day cure all disease? 06:31 – Longevity 10:16 – Role of machine learning in treating diseases 13:05 – A personal journey to medicine 16:25 – Insitro and disease-in-a-dish models 33:25 – What diseases can be helped with disease-in-a-dish approaches? 36:43 – Coursera and education 49:04 – Advice to people interested in AI 50:52 – Beautiful idea in deep learning 55:10 – Uncertainty in AI 58:29 – AGI and AI safety 1:06:52 – Are most people good? 1:09:04 – Meaning of life
Transcribe →#90 – Dmitry Korkin: Computational Biology of Coronavirus
Apr 22, 2020Tap to summarizeDmitry Korkin is a professor of bioinformatics and computational biology at Worcester Polytechnic Institute, where he specializes in bioinformatics of complex disease, computational genomics, systems biology, and biomedical data analytics. I came across Dmitry’s work when in February his group used the viral genome of the COVID-19 to reconstruct the 3D structure of its major viral proteins and their interactions with human proteins, in effect creating a structural genomics map of the coronavirus and making this data open and available to researchers everywhere. We talked about the biology of COVID-19, SARS, and viruses in general, and how computational methods can help us understand their structure and function in order to develop antiviral drugs and vaccines. 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: Dmitry’s Website: http://korkinlab.org/ Dmitry’s Twitter: https://twitter.com/dmkorkin Dmitry’s Paper that we discuss: https://bit.ly/3eKghEM 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 02:33 – Viruses are terrifying and fascinating 06:02 – How hard is it to engineer a virus? 10:48 – What makes a virus contagious? 29:52 – Figuring out the function of a protein 53:27 – Functional regions of viral proteins 1:19:09 – Biology of a coronavirus treatment 1:34:46 – Is a virus alive? 1:37:05 – Epidemiological modeling 1:55:27 – Russia 2:02:31 – Science bobbleheads 2:06:31 – Meaning of life
Transcribe →#87 – Richard Dawkins: Evolution, Intelligence, Simulation, and Memes
Apr 9, 2020Tap to summarizeRichard Dawkins is an evolutionary biologist, and author of The Selfish Gene, The Blind Watchmaker, The God Delusion, The Magic of Reality, The Greatest Show on Earth, and his latest Outgrowing God. He is the originator and popularizer of a lot of fascinating ideas in evolutionary biology and science in general, including funny enough the introduction of the word meme in his 1976 book The Selfish Gene, which in the context of a gene-centered view of evolution is an exceptionally powerful idea. He is outspoken, bold, and often fearless in his defense of science and reason, and in this way, is one of the most influential thinkers of our time. 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: Richard’s Website: https://www.richarddawkins.net/ Richard’s Twitter: https://twitter.com/RichardDawkins Richard’s Books: – Selfish Gene: https://amzn.to/34tpHQy – The Magic of Reality: https://amzn.to/3c0aqZQ – The Blind Watchmaker: https://amzn.to/2RqV5tH – The God Delusion: https://amzn.to/2JPrxlc – Outgrowing God: https://amzn.to/3ebFess – The Greatest Show on Earth: https://amzn.to/2Rp2j1h 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 02:31 – Intelligent life in the universe 05:03 – Engineering intelligence (are there shortcuts?) 07:06 – Is the evolutionary process efficient? 10:39 – Human brain and AGI 15:31 – Memes 26:37 – Does society need religion? 33:10 – Conspiracy theories 39:10 – Where do morals come from in humans? 46:10 – AI began with the ancient wish to forge the gods 49:18 – Simulation 56:58 – Books that influenced you 1:02:53 – Meaning of life
Transcribe →#86 – David Silver: AlphaGo, AlphaZero, and Deep Reinforcement Learning
Apr 3, 2020Tap to summarizeDavid Silver leads the reinforcement learning research group at DeepMind and was lead researcher on AlphaGo, AlphaZero and co-lead on AlphaStar, and MuZero and lot of important work in reinforcement learning. Support this podcast by signing up with 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 EPISODE LINKS: Reinforcement learning (book): https://amzn.to/2Jwp5zG 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 04:09 – First program 11:11 – AlphaGo 21:42 – Rule of the game of Go 25:37 – Reinforcement learning: personal journey 30:15 – What is reinforcement learning? 43:51 – AlphaGo (continued) 53:40 – Supervised learning and self play in AlphaGo 1:06:12 – Lee Sedol retirement from Go play 1:08:57 – Garry Kasparov 1:14:10 – Alpha Zero and self play 1:31:29 – Creativity in AlphaZero 1:35:21 – AlphaZero applications 1:37:59 – Reward functions 1:40:51 – Meaning of life
Transcribe →Cristos Goodrow: YouTube Algorithm
Jan 25, 2020Tap to summarizeCristos 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?
Transcribe →Melanie Mitchell: Concepts, Analogies, Common Sense & Future of AI
Dec 28, 2019Tap to summarizeMelanie Mitchell is a professor of computer science at Portland State University and an external professor at Santa Fe Institute. She has worked on and written about artificial intelligence from fascinating perspectives including adaptive complex systems, genetic algorithms, and the Copycat cognitive architecture which places the process of analogy making at the core of human cognition. From her doctoral work with her advisors Douglas Hofstadter and John Holland to today, she has contributed a lot of important ideas to the field of AI, including her recent book, simply called Artificial Intelligence: A Guide for Thinking Humans. 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: AI: A Guide for Thinking Humans (book) 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 02:33 – The term “artificial intelligence” 06:30 – Line between weak and strong AI 12:46 – Why have people dreamed of creating AI? 15:24 – Complex systems and intelligence 18:38 – Why are we bad at predicting the future with regard to AI? 22:05 – Are fundamental breakthroughs in AI needed? 25:13 – Different AI communities 31:28 – Copycat cognitive architecture 36:51 – Concepts and analogies 55:33 – Deep learning and the formation of concepts 1:09:07 – Autonomous vehicles 1:20:21 – Embodied AI and emotion 1:25:01 – Fear of superintelligent AI 1:36:14 – Good test for intelligence 1:38:09 – What is complexity? 1:43:09 – Santa Fe Institute 1:47:34 – Douglas Hofstadter 1:49:42 – Proudest moment
Transcribe →Gary Marcus: Toward a Hybrid of Deep Learning and Symbolic AI
Oct 3, 2019Tap to summarizeGary 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
Transcribe →Vijay Kumar: Flying Robots
Sep 8, 2019Tap to summarizeVijay Kumar is one of the top roboticists in the world, professor at the University of Pennsylvania, Dean of Penn Engineering, former director of GRASP lab, or the General Robotics, Automation, Sensing and Perception Laboratory at Penn that was established back in 1979, 40 years ago. Vijay is perhaps best known for his work in multi-robot systems (or robot swarms) and micro aerial vehicles, robots that elegantly cooperate in flight under all the uncertainty and challenges that real-world conditions present. 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.
Transcribe →Yann LeCun: Deep Learning, Convolutional Neural Networks, and Self-Supervised Learning
Aug 31, 2019Tap to summarizeYann LeCun is one of the fathers of deep learning, the recent revolution in AI that has captivated the world with the possibility of what machines can learn from data. He is a professor at New York University, a Vice President & Chief AI Scientist at Facebook, co-recipient of the Turing Award for his work on deep learning. He is probably best known as the founder of convolutional neural networks, in particular their early application to optical character recognition. 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.
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