#474 – DHH: Future of Programming, AI, Ruby on Rails, Productivity & Parenting

Sara Seager is a planetary scientist at MIT, known for her work on the search for exoplanets. Support this podcast by supporting our sponsors. Click links, get discount: - Public Goods at https://publicgoods.com/lex and use code LEX - PowerDot: https://powerdot.com/lex and use code 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: Sara's Twitter: https://twitter.com/profsaraseager Sara's Website: https://www.saraseager.com/ The Smallest Lights in the Universe (book): https://amzn.to/3g3LfHA 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 - Falling in love with the stars 09:55 - Are we alone in the universe? 15:27 - Seager equation for number of habitable planets 27:48 - Exoplanets 34:44 - Earth-like exoplanets 40:43 - Intelligent life 52:34 - Number of planets per star 55:09 - Space exploration 57:36 - Traveling to Proxima Centauri 1:00:52 - Starshade 1:07:34 - Using the sun as a gravitational lens 1:09:44 - Starshot 1:12:45 - Rogue planets 1:15:44 - The Smallest Lights in the Universe 1:30:15 - Book recommendations 1:37:48 - Advice for a young person 1:39:29 - Meaning of life

16 Elo 20201h 42min

Dileep George is a researcher at the intersection of neuroscience and artificial intelligence, co-founder of Vicarious, formerly co-founder of Numenta. From the early work on Hierarchical temporal memory to Recursive Cortical Networks to today, Dileep's always sought to engineer intelligence that is closely inspired by the human brain. Support this channel by supporting our sponsors. Click links, get discount: - Babbel: https://babbel.com and use code LEX - MasterClass: https://masterclass.com/lex - Raycon: https://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: 0:00 - Introduction 4:50 - Building a model of the brain 17:11 - Visual cortex 27:50 - Probabilistic graphical models 31:35 - Encoding information in the brain 36:56 - Recursive Cortical Network 51:09 - Solving CAPTCHAs algorithmically 1:06:48 - Hype around brain-inspired AI 1:18:21 - How does the brain learn? 1:21:32 - Perception and cognition 1:25:43 - Open problems in brain-inspired AI 1:30:33 - GPT-3 1:40:41 - Memory 1:45:08 - Neuralink 1:51:32 - Consciousness 1:57:59 - Book recommendations 2:06:49 - Meaning of life

14 Elo 20202h 10min

Russ Tedrake is a roboticist and professor at MIT and vice president of robotics research at TRI. He works on control of robots in interesting, complicated, underactuated, stochastic, difficult to model situations. Support this podcast by supporting our sponsors. Click links, get discount: - Magic Spoon: https://magicspoon.com/lex & use code LEX at checkout - BetterHelp: https://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 04:29 - Passive dynamic walking 09:40 - Animal movement 13:34 - Control vs Dynamics 15:49 - Bipedal walking 20:56 - Running barefoot 33:01 - Think rigorously with machine learning 44:05 - DARPA Robotics Challenge 1:07:14 - When will a robot become UFC champion 1:18:32 - Black Mirror Robot Dog 1:34:01 - Robot control 1:47:00 - Simulating robots 2:00:33 - Home robotics 2:03:40 - Soft robotics 2:07:25 - Underactuated robotics 2:20:42 - Touch 2:28:55 - Book recommendations 2:40:08 - Advice to young people 2:44:20 - Meaning of life

9 Elo 20202h 49min

Manolis Kellis is a professor at MIT and head of the MIT Computational Biology Group. He is interested in understanding the human genome from a computational, evolutionary, biological, and other cross-disciplinary perspectives. Support this podcast by supporting our sponsors: - Blinkist: https://blinkist.com/lex - Eight Sleep: https://eightsleep.com/lex - MasterClass: https://masterclass.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 03:54 - Human genome 17:47 - Sources of knowledge 29:15 - Free will 33:26 - Simulation 35:17 - Biological and computing 50:10 - Genome-wide evolutionary signatures 56:54 - Evolution of COVID-19 1:02:59 - Are viruses intelligent? 1:12:08 - Humans vs viruses 1:19:39 - Engineered pandemics 1:23:23 - Immune system 1:33:22 - Placebo effect 1:35:39 - Human genome source code 1:44:40 - Mutation 1:51:46 - Deep learning 1:58:08 - Neuralink 2:07:07 - Language 2:15:19 - Meaning of life

31 Heinä 20202h 29min

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 Heinä 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 Heinä 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 Heinä 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 Heinä 20201h 43min