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46:25EN
Lex Fridman· 17 janv. 2019PodcastDeep Learning at the Edge of 2019: BERT, Autopilot, and the Ideas That Broke ThroughAnime : Lex FridmanA tour of the architectural, applied, and automation breakthroughs that defined deep learning in 2018 and set up 2019.Modèles et entraînementDeep LearningAI research+3
1:19:59EN
Lex Fridman· 23 déc. 2018Podcast · 2 voixSchmidhuber’s bet: true meta-learning rewrites the learner, not the last layerInvité : Juergen Schmidhuber · Anime : 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.Modèles et entraînementFondamentaux du MLAGI Timelines+3
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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
1:26:21EN
Lex Fridman· 9 déc. 2018Podcast · 2 voixThe Machine Should Never Treat Its Objective as GospelInvité : Stuart Russell · Anime : Lex FridmanStuart Russell talks with Lex Fridman about metareasoning, self-driving reliability, and why uncertainty about human goals is the core of the control problem.SécuritéSécurité et gouvernanceAutonomous Driving+3
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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
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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
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Lex Fridman· 27 sept. 2016PodcastFrom Word Vectors to Memory Networks: The Building Blocks of Deep NLPAnime : 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.Modèles et entraînementDeep LearningFondamentaux du ML+2
1:00:51EN
Lex FridmanPodcastDeep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network FoundationsAnime : Lex FridmanA fast, equation-level walk through feed-forward networks, backpropagation, practical training tricks, dropout and batch normalization, from the 2016 Deep Learning School.Modèles et entraînementDeep LearningFondamentaux du ML+1
1:07:29EN
Lex FridmanPodcastDeep Reinforcement Learning from First Principles: Rewards, Q-Learning, Policy Gradients and AlphaZeroAnime : Lex FridmanLex Fridman's MIT 6.S091 opening lecture walks from the basic RL framework through DQN, actor-critic methods and AlphaZero to the unsolved problem of moving from simulation to the real world.Modèles et entraînementReinforcement LearningDeep Learning+3
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Lex FridmanPodcastIs Language or Vision Harder? Ilya Sutskever on the Unity of Machine LearningAnime : Lex FridmanIn a short clip from his 2020 conversation with Lex Fridman, Ilya Sutskever explains why vision, language and reinforcement learning share the same few principles, and why asking which problem is harder is the wrong question.Modèles et entraînementFondamentaux du MLDeep Learning+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
1:25:16EN
Lex FridmanPodcastFrom Hand-Built Features to ResNets: Andrej Karpathy's Crash Course in Convolutional NetworksAnime : Lex FridmanA 2016 Deep Learning School talk covering how convnets work, how the ImageNet winners evolved, and how to apply them without being a hero.Modèles et entraînementDeep LearningComputer Vision+3
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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
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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
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Lex FridmanPodcastFrom Sparse Coding to GANs: A Tour of Unsupervised Deep Learning in 2016Anime : Lex FridmanRuslan Salakhutdinov walks through autoencoders, Boltzmann machines, variational autoencoders and adversarial networks at the Deep Learning School, with candid notes on what did and did not work.Modèles et entraînementDeep LearningFondamentaux du ML
1:22:36EN
Lenny's PodcastPodcast · 2 voixStop Chasing AI News: Chip Huyen on What Actually Makes AI Products BetterInvité : Chip Huyen · Anime : Lenny RachitskyAuthor of AI Engineering walks through pre-training, post-training, RL, evals and RAG, then explains what separates companies that get value from AI from those that stall.Produit et UXFine-tuningGénération augmentée par récupération+3
1:15:59EN
Lex FridmanPodcastSteering Through Time: How Recurrent Networks and LSTMs Learn from SequencesAnime : Lex FridmanLex Fridman's MIT 6.S094 lecture walks from backpropagation basics to LSTMs and the way they are used to predict steering in self-driving cars.Modèles et entraînementDeep LearningFondamentaux du ML+2
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Lex FridmanPodcastInside the YouTube Algorithm: Related Graphs, Satisfaction Surveys and the Viewers Who Are Part of the CodeAnime : Lex FridmanYouTube's head of search and discovery, Cristos Goodrow, explains to Lex Fridman how search and recommendations work, which signals matter, and how the team measures whether viewers are better off.Modèles et entraînementRecommendation systemsSearch & retrieval+3
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Lex FridmanPodcastFrom Phonemes to CTC: How Deep Learning Rebuilt Speech RecognitionAnime : Lex FridmanBaidu's Adam Coates walks through the traditional speech pipeline, the CTC-based end-to-end engine that replaced much of it, and what it takes to scale and deploy it.Modèles et entraînementDeep LearningMultimodal AI+2
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No Priors: AI, Machine Learning, Tech, & StartupsPodcast · 2 voixIt's Six Months In: Misconceptions About AI, Regulation, Context Windows and IncumbentsAnime : Sarah Guo, Elad GilSarah Guo and Elad Gil answer listener questions on what the market gets wrong about generative AI, from the nuclear-regulation analogy to where startups can still beat incumbents.Entreprise et stratégieAI StrategyIA en entreprise+3
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Lex FridmanPodcastFrom AlphaGo Zero to MuZero: How Self-Play Strips Knowledge Out of AIAnime : Lex FridmanDeepMind's David Silver explains why removing human knowledge made AlphaZero stronger and more general, and how MuZero extended the idea to worlds with unknown rules.Modèles et entraînementReinforcement LearningAI research+3
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Lex FridmanPodcastWhy AI Will Never Be Perfect: Lex Fridman's Case for Keeping Humans in the LoopAnime : Lex FridmanIn MIT 6.S093's opening lecture, Lex Fridman lays out five research areas of human-centered AI, from machine teaching to arguing machines that flag their own uncertainty.Modèles et entraînementHuman-AI CollaborationFondamentaux du ML+3
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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
1:19:52EN
Lex FridmanPodcastFrom Pixels to Steering Wheels: Convolutional Networks and End-to-End DrivingAnime : Lex FridmanLex Fridman's MIT 6.S094 lecture explains how convolutional neural networks work, where deep learning helps a self-driving car, and how to train a network to steer from video.Modèles et entraînementAutonomous DrivingDeep Learning+3
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