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1:37:31ENThe Robot That Brought the Beer Was Being TeleoperatedGast: Sergey Levine · Moderation: Lex FridmanSergey Levine tells Lex Fridman why hardware is almost closed, why common sense has to be lived, and why offline reinforcement learning is the missing on-ramp from logs to robots that keep getting better.
2:00:32ENSlow Synapses Grow Fast Learners — Warmth Is the Harder ProblemGast: Matt Botvinick · Moderation: Lex FridmanDeepMind neuroscience director Matt Botvinick tells Lex Fridman how prefrontal cortex, meta-reinforcement learning, and dopamine research meet the human question of what a better life with AI would actually look like.
3:00:18ENOnly a Simulation Can Be ConsciousGast: Joscha Bach · Moderation: Lex FridmanJoscha Bach tells Lex Fridman why a neuron cannot feel anything, why existence is the default, and why this dying world is still the best level of the game.
1:37:28ENWe Are Still Massively Underestimating Deep LearningGast: Ilya Sutskever · Moderation: Lex FridmanIlya Sutskever tells Lex Fridman why 2012 was about conviction, why bigger models can get worse before they get better, and what he would do with an AGI.
1:48:01ENDavid Silver: AlphaGo Won by Taking Human Knowledge OutGast: David Silver · Moderation: Lex FridmanThe DeepMind researcher behind AlphaGo and AlphaZero tells Lex Fridman how an unclaimed million-dollar Go prize, a BBC Micro at age seven, and a honeymoon insight led to superhuman self-play.
1:12:48ENJohn Hopfield's Physics of Mind: Glitches Become FeaturesGast: John Hopfield · Moderation: Lex FridmanThe Princeton physicist walks Lex Fridman from a wobbly London bridge to Hopfield networks, consciousness-as-story, and why his famous model still does not explain learning.
1:44:55ENIf You Need 60,000 Digits, You Have Not Found IntelligenceGast: Vladimir Vapnik · Moderation: Lex FridmanVladimir Vapnik tells Lex Fridman that intelligence is the art of shrinking a sea of functions with a few good predicates — and that MNIST, solved with sixty examples per class, is the test.
49:53ENGilbert Strang still teaches with thick chalk and four subspacesGast: Gilbert Strang · Moderation: Lex FridmanThe MIT linear-algebra legend talks with Lex Fridman about OpenCourseWare, singular values, neural nets as a million folds, and why calculus hogged the undergraduate timetable.
55:24ENThe Machine Has a Steady Hand: Garry Kasparov on Deep Blue, AlphaZero, and PutinGast: Garry Kasparov · Moderation: Lex FridmanThe former world champion tells Lex Fridman why losing to IBM still hurt, why chess was never the peak of intellect, and why he lives in New York.
1:25:01ENGary Marcus: Deep Learning Imitates Intelligence. It Does Not Understand.Gast: Gary Marcus · Moderation: Lex FridmanOn Lex Fridman's podcast, the NYU emeritus and Rebooting AI co-author argues that perception and reasoning need different tools — and that trustworthy AI is impossible until machines can represent harm, bottles, and intent.
1:03:13ENYou Can't Have Both Kinds of Fairness, Says Peter NorvigGast: Peter Norvig · Moderation: Lex FridmanThe co-author of Artificial Intelligence: A Modern Approach walks Lex Fridman through recidivism scores, utility functions, Lisp, MOOCs, and why human-level intelligence should not be the goal.
1:59:49ENIntelligence is not the height of a building: François Chollet on Keras, deep learning, and why AGI will not explodeGast: François Chollet · Moderation: Lex FridmanThe Keras creator tells Lex Fridman that intelligence lives in a brain, a body, and a problem, that science already is a recursively self-improving superhuman system with linear output, and that the real near-term danger is the newsfeed.
1:15:58ENHuman Intelligence Is a Ridiculously Narrow SliverGast: Yann Lecun · Moderation: Lex FridmanYann LeCun tells Lex Fridman why giant nets plus gradient descent broke the textbooks, why "AGI" is the wrong word, and why the next leap is a world model learned like a baby.
1:44:11ENA Single GPU Is Enough: Jeremy Howard on Teaching Deep LearningGast: Jeremy Howard · Moderation: Lex FridmanThe fast.ai founder tells Lex Fridman why domain experts, not more papers or more TPUs, will decide whether deep learning actually helps people.
2:09:42ENThe Brain Does Not Classify Images. It Navigates Maps.Gast: Jeff Hawkins · Moderation: Lex FridmanJeff Hawkins tells Lex Fridman why the neocortex stores the world as thousands of voting reference frames, and why scaling today's networks will not get us there.
1:08:37ENA Bar in Montreal, Then a Night of Code: Ian Goodfellow on Why GANs WorkGast: Ian Goodfellow · Moderation: Lex FridmanLex Fridman talks with Ian Goodfellow about the 2014 invention of generative adversarial networks, adversarial examples as a security problem, and what still has to be built before machines can hold a fact the way a person does.
1:20:21ENEinstein Was the Worst of Five Ph.D. StudentsGast: Tomaso Poggio · Moderation: Lex FridmanMIT neuroscientist Tomaso Poggio on why intelligence is science's deepest problem, and what brains still have to teach machines.
46:25ENDeep Learning at the Edge of 2019: BERT, Autopilot, and the Ideas That Broke ThroughModeration: Lex FridmanA tour of the architectural, applied, and automation breakthroughs that defined deep learning in 2018 and set up 2019.
1:19:59ENSchmidhuber’s bet: true meta-learning rewrites the learner, not the last layerGast: Juergen Schmidhuber · Moderation: Lex FridmanJürgen Schmidhuber tells Lex Fridman how Gödel machines, compression-as-science, Power Play, LSTMs, and world models all descend from a teenage plan to multiply one person’s curiosity into a better physicist than he could ever be.
42:45ENA one-legged robot learned a backflip from "this is better"Gast: Pieter Abbeel · Moderation: Lex FridmanPieter Abbeel tells Lex Fridman why preference comparisons, self-play, ten-minute demos, and messy ensembles of simulators are the practical path through deep reinforcement learning.
54:03ENThe Hardest Part of Learning Was Left Out of the TextbooksGast: Vladimir Vapnik · Moderation: Lex FridmanVladimir Vapnik tells Lex Fridman why intelligence is the work of inventing predicates that shrink the admissible set of functions, not of fitting a rule to a mountain of data.
42:18ENAn Adult Can Change Last Year's Mind. A Network Still Cannot.Gast: Yoshua Bengio · Moderation: Lex FridmanYoshua Bengio tells Lex Fridman that the missing pieces of deep learning sit in credit assignment, training objectives, and a kind of compositionality borrowed from classical AI — not in ten thousand layers.
1:29:03ENFrom Word Vectors to Memory Networks: The Building Blocks of Deep NLPModeration: Lex FridmanRichard Socher explains how word embeddings, recurrent models, GRUs, pointer mixtures, and dynamic memory networks turn language tasks into trainable end-to-end systems.
1:00:51ENDeep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network FoundationsModeration: Lex FridmanA fast, equation-level walk through feed-forward networks, backpropagation, practical training tricks, dropout and batch normalization, from the 2016 Deep Learning School.
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