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42:45EN
Lex Fridman· 16 déc. 2018Podcast · 2 voixA one-legged robot learned a backflip from "this is better"Invité : Pieter Abbeel · Anime : 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.Modèles et entraînementReinforcement LearningRobotics+2
54:03EN
Lex Fridman· 16 nov. 2018Podcast · 2 voixThe Hardest Part of Learning Was Left Out of the TextbooksInvité : Vladimir Vapnik · Anime : 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.Modèles et entraînementFondamentaux du MLDeep Learning Critique+3
42:18EN
Lex Fridman· 20 oct. 2018Podcast · 2 voixAn Adult Can Change Last Year's Mind. A Network Still Cannot.Invité : Yoshua Bengio · Anime : 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.Modèles et entraînementDeep LearningFondamentaux du ML+3
24:22EN
Lex FridmanPodcast · 2 voixCraftsmanship, First Principles and the Road to Self-Driving: Jim Keller on Working with Elon MuskInvité : Jim Keller · Anime : Lex FridmanThe veteran chip architect explains why he thinks autonomous driving is a tractable engineering problem, how Tesla's cost constraints shaped its hardware, and what he took from Musk's way of thinking.Modèles et entraînementAutonomous DrivingAutonomous Systems+3
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Lex FridmanPodcast · 2 voixDeep Learning Is a System 1 Machine: Daniel Kahneman on What AI Still LacksInvité : Daniel Kahneman · Anime : Lex FridmanThe Nobel-winning psychologist tells Lex Fridman how his fast-and-slow thinking framework maps onto deep learning, and why driving among pedestrians may require understanding people.Modèles et entraînementDeep LearningAI research+3
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No Priors: AI, Machine Learning, Tech, & StartupsPodcast · 2 voixBiohub's Bet: Building Open AI World Models of Biology From Proteins Up to CellsAnime : Sarah Guo, Elad GilMark Zuckerberg, Priscilla Chan and Alex Rives explain why Biohub pairs a frontier AI lab with frontier wet-lab biology, and why it releases tools like ESMFold2 as open source.Modèles et entraînementAI for scienceOpen-source ecosystems+3
1:28:53EN
Lex FridmanPodcast · 2 voixMachine Learning for Business Students: Powerful, Narrow, and Easier to Fool Than You ThinkInvité : MIT Sloan · Anime : Lex FridmanLex Fridman's non-technical MIT Sloan lecture explains how machine learning works, why it is so capable at perception, and where it still breaks in the real world.Modèles et entraînementFondamentaux du MLAI Strategy+3
11:11EN
Lex FridmanPodcast · 2 voixYann LeCun on Benchmarks, Interactive Environments, and Why Human Intelligence Is Not GeneralInvité : Yann Lecun · Anime : Lex FridmanIn a 2019 clip, LeCun defends benchmarks as a test of AI claims, explains why interactive environments break the usual train-test protocol, and argues that "general intelligence" is a misnomer.Modèles et entraînementBenchmarksAGI+3
1:17:38EN
Lex FridmanPodcast · 2 voixBrains Are Body Regulators: Lisa Feldman Barrett on Why Emotions Are Constructed, Not DetectedInvité : Lisa Feldman Barrett · Anime : Lex FridmanIn a guest talk for MIT's AGI course, neuroscientist Lisa Feldman Barrett explains why faces don't display emotions, why brains exist to run bodies, and what that means for building human-like AI.Modèles et entraînementNeuroscience & AIAGI+3
1:10:32EN
Lenny's PodcastPodcast · 2 voixA Billion Dollars, No VC, Under 100 People: Surge AI's Edwin Chen on Data Quality and Where AI Is HeadingInvité : Edwin Chen · Anime : Lenny RachitskySurge AI's founder explains how a bootstrapped team built the leading human-data company, why he distrusts benchmarks, and how reinforcement learning environments are the next frontier.Modèles et entraînementCompany BuildingData quality & curation+3
10:16EN
Lex FridmanPodcast · 2 voixReasoning Without Logic: Yann LeCun on Teaching Neural Networks to ThinkInvité : Yann Lecun · Anime : Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.Modèles et entraînementFondamentaux du MLDeep Learning+3
23:54EN
No Priors: AI, Machine Learning, Tech, & StartupsPodcast · 2 voixBeyond GPT-7: The Customization Toolkit, Meta's Open Source Bet and the Return of Consumer AIAnime : Sarah Guo, Elad GilIn a host-only episode, the No Priors hosts map the six levers for 10x to 100x better AI, explain fine-tuning, RAG and AI feedback, and ask why consumer social is suddenly interesting again.Modèles et entraînementFine-tuningGénération augmentée par récupération+3
1:08:06EN
Lex FridmanPodcast · 2 voixDeep Learning in One Lecture: From Perceptrons to AutoML and the Limits of LearningInvité : Deep Learning Basics · Anime : Lex FridmanLex Fridman's opening lecture for MIT's 6.S094 course surveys why deep learning took off, how neural networks learn, and where the technique still falls short.Modèles et entraînementDeep LearningFondamentaux du ML+3
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Lex FridmanPodcast · 2 voixWhy Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AIInvité : François Chollet · Anime : Lex FridmanFrançois Chollet explains why neural networks only interpolate between points they have seen, and why the best AI systems pair them with symbolic rules.Modèles et entraînementFondamentaux du MLDeep Learning+2
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Lex FridmanPodcast · 2 voixRay Kurzweil on Hierarchies, the Neocortex and Merging with AIInvité : Ray Kurzweil · Anime : Lex FridmanIn an MIT AGI course lecture, Kurzweil connects the history of neural networks to his hierarchical theory of the neocortex, language understanding at Google, longevity, jobs and existential risk.Modèles et entraînementAGIAI History+3
1:00:15EN
Lex FridmanPodcast · 2 voixCircuit Search, Hindsight and Self-Play: Ilya Sutskever's Case for Where Learning Is GoingInvité : Ilya Sutskever · Anime : Lex FridmanIn an MIT AGI lecture, OpenAI's co-founder explains why deep learning works, then walks through meta-learning, hindsight experience replay, sim-to-real transfer, self-play and learning goals from human feedback.Modèles et entraînementFondamentaux du MLDeep Learning+3
1:35:09EN
Lex FridmanPodcast · 2 voixReverse-Engineering the Child: Josh Tenenbaum's Case for Human-Like AIInvité : MIT AGI · Anime : Lex FridmanMIT cognitive scientist Josh Tenenbaum argues that real machine intelligence needs models of the world, built by studying how babies think and learn, not just better pattern recognition.Modèles et entraînementAGIFondamentaux du ML+3
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Lex FridmanPodcast · 2 voixHow IBM Watson Won Jeopardy: A Moonshot Built on Engineering, Not a BreakthroughInvité : David Ferrucci · Anime : Lex FridmanDavid Ferrucci tells Lex Fridman how he talked IBM into the Jeopardy challenge and how his team built Watson's search, scoring and confidence machinery in about four years.Modèles et entraînementAI HistorySearch & retrieval+1
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Lex FridmanPodcast · 2 voixStart Small, Build Habits, Pick Your Team: Andrew Ng's Practical Advice for Learning Deep LearningInvité : Andrew Ng · Anime : Lex FridmanIn a clip from the Lex Fridman Podcast, Andrew Ng explains how to begin in deep learning, how to study effectively, and how to choose a career path.Modèles et entraînementDeep LearningFondamentaux du ML+3
11:23EN
Lex FridmanPodcast · 2 voixScience Is a Self-Improving System That Isn't Exploding: Chollet on the Limits of an Intelligence ExplosionInvité : François Chollet · Anime : Lex FridmanFrançois Chollet uses the history of science to argue that recursive self-improvement meets exponential friction and yields linear, not explosive, progress.Modèles et entraînementAGIAI research+3
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Lex FridmanPodcast · 2 voixEngineering Intelligence: Lex Fridman's Opening Lecture for MIT's AGI CourseInvité : MIT AGI · Anime : Lex FridmanLex Fridman lays out an engineering-first approach to artificial general intelligence, previews the course's projects and guest speakers, and frames deep learning's limits.Modèles et entraînementAGIAI research+3