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1:15:58EN
Lex Fridman· Aug 31, 2019Podcast · 2 speakersHuman Intelligence Is a Ridiculously Narrow SliverGuest Yann Lecun · Host 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.
1:44:11ENLex Fridman· Aug 27, 2019Podcast · 2 speakersA Single GPU Is Enough: Jeremy Howard on Teaching Deep LearningGuest Jeremy Howard · Host 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.
2:09:42ENLex Fridman· Jul 1, 2019Podcast · 2 speakersThe Brain Does Not Classify Images. It Navigates Maps.Guest Jeff Hawkins · Host 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.
1:08:37ENLex Fridman· Apr 18, 2019Podcast · 2 speakersA Bar in Montreal, Then a Night of Code: Ian Goodfellow on Why GANs WorkGuest Ian Goodfellow · Host 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.
1:20:21ENLex Fridman· Jan 19, 2019Podcast · 2 speakersEinstein Was the Worst of Five Ph.D. StudentsGuest Tomaso Poggio · Host Lex FridmanMIT neuroscientist Tomaso Poggio on why intelligence is science's deepest problem, and what brains still have to teach machines.
46:25ENLex Fridman· Jan 17, 2019PodcastDeep Learning at the Edge of 2019: BERT, Autopilot, and the Ideas That Broke ThroughHost Lex FridmanA tour of the architectural, applied, and automation breakthroughs that defined deep learning in 2018 and set up 2019.
1:19:59ENLex Fridman· Dec 23, 2018Podcast · 2 speakersSchmidhuber’s bet: true meta-learning rewrites the learner, not the last layerGuest Juergen Schmidhuber · Host 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.
42:45ENLex Fridman· Dec 16, 2018Podcast · 2 speakersA one-legged robot learned a backflip from "this is better"Guest Pieter Abbeel · Host 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.
54:03ENLex Fridman· Nov 16, 2018Podcast · 2 speakersThe Hardest Part of Learning Was Left Out of the TextbooksGuest Vladimir Vapnik · Host 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.
42:18ENLex Fridman· Oct 20, 2018Podcast · 2 speakersAn Adult Can Change Last Year's Mind. A Network Still Cannot.Guest Yoshua Bengio · Host 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.
1:29:03ENLex Fridman· Sep 27, 2016PodcastFrom Word Vectors to Memory Networks: The Building Blocks of Deep NLPHost 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.
1:00:51ENLex FridmanPodcastDeep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network FoundationsHost Lex FridmanA fast, equation-level walk through feed-forward networks, backpropagation, practical training tricks, dropout and batch normalization, from the 2016 Deep Learning School.
1:56:35EN
Cognitive Revolution "How AI Changes Everything"PodcastFrom Autocomplete to Alien Optimizer: Riley Goodside on Three Eras of PromptingHost Nathan LabenzScale AI's first staff prompt engineer traces how language models went from pure text prediction to instruction following to RLHF, and what that means for building with them.
1:07:29ENLex FridmanPodcastDeep Reinforcement Learning from First Principles: Rewards, Q-Learning, Policy Gradients and AlphaZeroHost 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.
24:22ENLex FridmanPodcast · 2 speakersCraftsmanship, First Principles and the Road to Self-Driving: Jim Keller on Working with Elon MuskGuest Jim Keller · Host 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.
9:59ENLex FridmanPodcastIs Language or Vision Harder? Ilya Sutskever on the Unity of Machine LearningHost 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.
15:01ENLex FridmanPodcast · 2 speakersDeep Learning Is a System 1 Machine: Daniel Kahneman on What AI Still LacksGuest Daniel Kahneman · Host 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.
56:20EN
No Priors: AI, Machine Learning, Tech, & StartupsPodcast · 2 speakersBiohub's Bet: Building Open AI World Models of Biology From Proteins Up to CellsHost 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.
1:25:16ENLex FridmanPodcastFrom Hand-Built Features to ResNets: Andrej Karpathy's Crash Course in Convolutional NetworksHost 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.
1:28:53ENLex FridmanPodcast · 2 speakersMachine Learning for Business Students: Powerful, Narrow, and Easier to Fool Than You ThinkGuest MIT Sloan · Host 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.
11:11ENLex FridmanPodcast · 2 speakersYann LeCun on Benchmarks, Interactive Environments, and Why Human Intelligence Is Not GeneralGuest Yann Lecun · Host 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.
1:24:43ENLex FridmanPodcastFrom Sparse Coding to GANs: A Tour of Unsupervised Deep Learning in 2016Host 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.
1:17:38ENLex FridmanPodcast · 2 speakersBrains Are Body Regulators: Lisa Feldman Barrett on Why Emotions Are Constructed, Not DetectedGuest Lisa Feldman Barrett · Host 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.
58:46ENLex FridmanPodcastCaging the Neural Network: How Aptiv Thinks About Safe Self-DrivingHost Lex FridmanAptiv Autonomous Mobility's Karl Iagnemma and Oscar Beijbom explain why end-to-end learning is hard to trust in cars, and show how PointPillars and nuScenes push lidar perception forward.Load moreShowing 24 of 344