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2:02:45ENSteal the Code: You Get Zero Points for Making the Algorithm RunModeration: Lex FridmanCharles Isbell and Michael Littman tell Lex Fridman why machine learning is not just statistics, why struggle has to stay hopeful, and what a $6,600 online master's actually sells.
45:31ENYou Are Your Own Existence Proof: Karl Friston on Free Energy, Life, and Self-AwarenessModeration: Lex FridmanKarl Friston explains how the free energy principle reframes existence, life, planning, and self-awareness as inference problems.
26:02ENMoore’s Law Is Not Dead—It Is a Cascade of S-CurvesModeration: Lex FridmanJim Keller explains why transistor scaling keeps outrunning its obituary, and how exponential hardware changes the mathematics AI can attempt.
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.
1:29:03ENFrom 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.
1:07:29ENDeep Reinforcement Learning from First Principles: Rewards, Q-Learning, Policy Gradients and AlphaZeroModeration: 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.
9:59ENIs Language or Vision Harder? Ilya Sutskever on the Unity of Machine LearningModeration: 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.
1:25:16ENFrom Hand-Built Features to ResNets: Andrej Karpathy's Crash Course in Convolutional NetworksModeration: 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.
1:24:43ENFrom Sparse Coding to GANs: A Tour of Unsupervised Deep Learning in 2016Moderation: 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.
1:15:59ENSteering Through Time: How Recurrent Networks and LSTMs Learn from SequencesModeration: 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.
37:00ENInside the YouTube Algorithm: Related Graphs, Satisfaction Surveys and the Viewers Who Are Part of the CodeModeration: 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.
1:31:52ENFrom Phonemes to CTC: How Deep Learning Rebuilt Speech RecognitionModeration: 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.
17:40ENFrom AlphaGo Zero to MuZero: How Self-Play Strips Knowledge Out of AIModeration: 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.
1:07:35ENWhy AI Will Never Be Perfect: Lex Fridman's Case for Keeping Humans in the LoopModeration: 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.
1:19:52ENFrom Pixels to Steering Wheels: Convolutional Networks and End-to-End DrivingModeration: 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.
1:27:30ENFrom NAND Gates to Self-Driving Traffic: Deep Reinforcement Learning in One LectureModeration: Lex FridmanLex Fridman's second MIT 6.S094 lecture walks from the humble perceptron to deep Q-learning and the browser-based DeepTraffic competition.
1:05:15ENTaming the Long Tail: How Waymo Builds a Self-Driving FactoryModeration: Lex FridmanWaymo principal scientist Drago Anguelov explains how machine learning infrastructure, hybrid design and smart simulated agents tackle the rare events of autonomous driving.
1:27:15ENPolicy Gradients vs. Q-Learning: John Schulman's Field Guide to Deep Reinforcement LearningModeration: Lex FridmanOpenAI's John Schulman walks through the core methods of deep RL, from the score function estimator to TRPO and DQN, and says when each one is worth using.
1:19:21ENVapnik's Case for Learning by Predicates, Not Just by More DataModeration: Lex FridmanIn an MIT lecture, Vladimir Vapnik argues that complete statistical learning needs two mechanisms, data and invariants, and that choosing smart predicates is where intelligence lives.
1:03:25ENTheano in an Hour: Symbolic Graphs, Automatic Gradients and a Compiler That Rewrites Your MathModeration: Lex FridmanPascal Lamblin of MILA walks through how Theano builds, differentiates, optimizes and runs computation graphs, then trains logistic regression, a convnet and an LSTM.
13:58ENDeep Reinforcement Learning, Explained by the Researcher Behind AlphaGoModeration: Lex FridmanDavid Silver of DeepMind walks through what reinforcement learning is, how its building blocks fit together, and why deep neural networks keep surprising even the experts.
57:55ENFrom Q-Tables to AlphaGo Zero: A Self-Driving Car Lecture on Deep Reinforcement LearningModeration: Lex FridmanLex Fridman's MIT 6.S094 lecture walks from the AI pipeline and Q-learning to deep Q-networks, AlphaGo Zero and the DeepTraffic competition.
1:13:22ENThe Second 90%: What It Really Takes to Put Deep Learning on a Driverless CarModeration: Lex FridmanWaymo's Sacha Arnoud walks MIT students through Waymo's history, its perception stack, and the industrial-scale labeling, compute and testing behind it.
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