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Lex Fridman· 19 abr 2021Pódcast · 2 vocesIf You Reran Earth a Million Times, Would Anything Like Us Appear?Invitado: Risto Miikkulainen · Presenta: Lex FridmanRisto Miikkulainen on neuroevolution, hyena raids, basil that never sleeps, and why variation-plus-selection still finds solutions backpropagation cannot see.
2:39:15ENLex Fridman· 18 feb 2021Pódcast · 2 vocesCraftsmanship, Graphs and Consciousness: Jim Keller on Building Computers That ThinkInvitado: Jim Keller · Presenta: Lex FridmanLegendary chip architect Jim Keller talks with Lex Fridman about design philosophy, the future of AI hardware, the human mind and how to live well.
2:08:26ENLex Fridman· 23 ago 2020Pódcast · 2 vocesSatisfying Lectures Don't Stick: Grant Sanderson on Feynman, Fear, and Hitting PublishInvitado: Grant Sanderson · Presenta: Lex FridmanThe 3Blue1Brown creator talks with Lex Fridman about why the nicest explanations can fail, why he envies Bell Labs, and why the experts most qualified to teach are the ones least willing to post.
2:29:24ENLex Fridman· 31 jul 2020Pódcast · 2 vocesWe Are the Descendants of the First Digital ComputerInvitado: Manolis Kellis · Presenta: Lex FridmanMIT computational biologist Manolis Kellis tells Lex Fridman why life is digital, why any two humans are 99.9 percent identical, and what evolutionary signatures reveal inside SARS-CoV-2.
1:41:37ENLex Fridman· 21 jul 2020Pódcast · 2 vocesThe Fallacy of the Successful First Step: Jitendra Malik on Why Seeing Still Isn't SolvedInvitado: Jitendra Malik · Presenta: Lex FridmanBerkeley's Jitendra Malik tells Lex Fridman why computer vision keeps underestimating itself, why full self-driving still needs cognition, and why vision is the parent of language.
1:37:31ENLex Fridman· 14 jul 2020Pódcast · 2 vocesThe Robot That Brought the Beer Was Being TeleoperatedInvitado: Sergey Levine · Presenta: 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.
2:00:32ENLex Fridman· 3 jul 2020Pódcast · 2 vocesSlow Synapses Grow Fast Learners — Warmth Is the Harder ProblemInvitado: Matt Botvinick · Presenta: 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.
1:37:28ENLex Fridman· 8 may 2020Pódcast · 2 vocesWe Are Still Massively Underestimating Deep LearningInvitado: Ilya Sutskever · Presenta: 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.
1:12:03ENLex Fridman· 5 may 2020Pódcast · 2 vocesMice Don't Get Alzheimer's: Daphne Koller on Disease in a DishInvitado: Daphne Koller · Presenta: Lex FridmanThe Coursera co-founder and insitro CEO on why most diseases are not one disease, how to turn cells into data, and why overconfident neural nets can kill.
2:09:02ENLex Fridman· 22 abr 2020Pódcast · 2 vocesA Hundred-Nanometer Machine That Is Not Quite AliveInvitado: Dmitry Korkin · Presenta: Lex FridmanDmitry Korkin walks through the structural genomics of SARS-CoV-2, from spike-ACE2 docking to drug-binding sites, vaccines, and cruise-ship simulations, recorded in the first spring of the pandemic.
1:48:01ENLex Fridman· 3 abr 2020Pódcast · 2 vocesDavid Silver: AlphaGo Won by Taking Human Knowledge OutInvitado: David Silver · Presenta: 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.
1:29:10ENLex Fridman· 20 feb 2020Pódcast · 2 vocesAndrew Ng: The Hardest Part of AI Is Rarely the AlgorithmInvitado: Andrew Ng · Presenta: Lex FridmanIn a conversation with Lex Fridman, Ng traces a path from late-night Coursera recordings to factory floors, arguing that messy data, habits, and starting small matter more than hype.
1:44:55ENLex Fridman· 14 feb 2020Pódcast · 2 vocesIf You Need 60,000 Digits, You Have Not Found IntelligenceInvitado: Vladimir Vapnik · Presenta: 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.
1:18:41ENLex Fridman· 14 ene 2020Pódcast · 2 vocesThe SS Soldier Who Hugged a Jewish Boy, and the Systems That Still Fool UsInvitado: Daniel Kahneman · Presenta: Lex FridmanDaniel Kahneman on System 1, deep learning that does not know what it is saying, and why a machine that must call a human is almost as hard as a machine that never needs one.
1:52:40ENLex Fridman· 28 dic 2019Pódcast · 2 vocesWithout Analogies, There Can Be No ConceptsInvitado: Melanie Mitchell · Presenta: Lex FridmanMelanie Mitchell talks with Lex Fridman about why analogy sits at the core of thought, why scaled-up learning still fails at common sense, and why she thinks human-level AI is more than a century away.
35:46ENLex Fridman· 29 nov 2019Pódcast · 2 vocesYou never hear structure: Noam Chomsky on language, cognition, and deep learningInvitado: Noam Chomsky · Presenta: Lex FridmanLex Fridman asks the linguist whether we could talk to aliens, how far language goes in the brain, and whether neural networks tell us anything about the mind.
49:53ENLex Fridman· 25 nov 2019Pódcast · 2 vocesGilbert Strang still teaches with thick chalk and four subspacesInvitado: Gilbert Strang · Presenta: 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.
1:25:01ENLex Fridman· 3 oct 2019Pódcast · 2 vocesGary Marcus: Deep Learning Imitates Intelligence. It Does Not Understand.Invitado: Gary Marcus · Presenta: 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.
1:17:29ENLex Fridman· 23 sept 2019Pódcast · 2 vocesThe Algorithm Is Already Better. The Clinic Still Uses 1967.Invitado: Regina Barzilay · Presenta: Lex FridmanMIT's Regina Barzilay on why deep learning already beats the breast-cancer 'density' score — and why data, liability, and incentives still will not move.
1:59:49ENLex Fridman· 14 sept 2019Pódcast · 2 vocesIntelligence is not the height of a building: François Chollet on Keras, deep learning, and why AGI will not explodeInvitado: François Chollet · Presenta: 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.
1:44:11ENLex Fridman· 27 ago 2019Pódcast · 2 vocesA Single GPU Is Enough: Jeremy Howard on Teaching Deep LearningInvitado: Jeremy Howard · Presenta: 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.
1:10:58ENLex Fridman· 3 jun 2019Pódcast · 2 vocesThe Hadoop Lesson That Made Google Give TensorFlow AwayInvitado: Rajat Monga · Presenta: Lex FridmanIn November 2015, Google put its core internal machine-learning system on GitHub. Rajat Monga explains why a company of that size would hand over the hard-to-copy part.
1:46:01ENLex Fridman· 29 abr 2019Pódcast · 2 vocesThe Night AlphaStar Beat TLO, the Team Went to a BarInvitado: Oriol Vinyals · Presenta: Lex FridmanOriol Vinyals on how DeepMind turned StarCraft II into a sequence problem, built a league of agents, and still refuses to call the game solved.
1:08:37ENLex Fridman· 18 abr 2019Pódcast · 2 vocesA Bar in Montreal, Then a Night of Code: Ian Goodfellow on Why GANs WorkInvitado: Ian Goodfellow · Presenta: 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.Cargar másMostrando 24 de 101