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42:45EN
Lex Fridman· 16 dic 2018Pódcast · 2 vocesA one-legged robot learned a backflip from "this is better"Invitado: Pieter Abbeel · Presenta: 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· 16 nov 2018Pódcast · 2 vocesThe Hardest Part of Learning Was Left Out of the TextbooksInvitado: Vladimir Vapnik · Presenta: 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· 20 oct 2018Pódcast · 2 vocesAn Adult Can Change Last Year's Mind. A Network Still Cannot.Invitado: Yoshua Bengio · Presenta: 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.
24:22ENLex FridmanPódcast · 2 vocesCraftsmanship, First Principles and the Road to Self-Driving: Jim Keller on Working with Elon MuskInvitado: Jim Keller · Presenta: 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.
15:01ENLex FridmanPódcast · 2 vocesDeep Learning Is a System 1 Machine: Daniel Kahneman on What AI Still LacksInvitado: Daniel Kahneman · Presenta: 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, & StartupsPódcast · 2 vocesBiohub's Bet: Building Open AI World Models of Biology From Proteins Up to CellsPresenta: 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.
1:28:53ENLex FridmanPódcast · 2 vocesMachine Learning for Business Students: Powerful, Narrow, and Easier to Fool Than You ThinkInvitado: MIT Sloan · Presenta: 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 FridmanPódcast · 2 vocesYann LeCun on Benchmarks, Interactive Environments, and Why Human Intelligence Is Not GeneralInvitado: Yann Lecun · Presenta: 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.
1:17:38ENLex FridmanPódcast · 2 vocesBrains Are Body Regulators: Lisa Feldman Barrett on Why Emotions Are Constructed, Not DetectedInvitado: Lisa Feldman Barrett · Presenta: 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.
1:10:32EN
Lenny's PodcastPódcast · 2 vocesA Billion Dollars, No VC, Under 100 People: Surge AI's Edwin Chen on Data Quality and Where AI Is HeadingInvitado: Edwin Chen · Presenta: 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.
10:16ENLex FridmanPódcast · 2 vocesReasoning Without Logic: Yann LeCun on Teaching Neural Networks to ThinkInvitado: Yann Lecun · Presenta: Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.
23:54ENNo Priors: AI, Machine Learning, Tech, & StartupsPódcast · 2 vocesBeyond GPT-7: The Customization Toolkit, Meta's Open Source Bet and the Return of Consumer AIPresenta: 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.
1:08:06ENLex FridmanPódcast · 2 vocesDeep Learning in One Lecture: From Perceptrons to AutoML and the Limits of LearningInvitado: Deep Learning Basics · Presenta: 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.
10:34ENLex FridmanPódcast · 2 vocesWhy Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AIInvitado: François Chollet · Presenta: 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.
52:28ENLex FridmanPódcast · 2 vocesRay Kurzweil on Hierarchies, the Neocortex and Merging with AIInvitado: Ray Kurzweil · Presenta: 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.
1:00:15ENLex FridmanPódcast · 2 vocesCircuit Search, Hindsight and Self-Play: Ilya Sutskever's Case for Where Learning Is GoingInvitado: Ilya Sutskever · Presenta: 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.
1:35:09ENLex FridmanPódcast · 2 vocesReverse-Engineering the Child: Josh Tenenbaum's Case for Human-Like AIInvitado: MIT AGI · Presenta: 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.
32:41ENLex FridmanPódcast · 2 vocesHow IBM Watson Won Jeopardy: A Moonshot Built on Engineering, Not a BreakthroughInvitado: David Ferrucci · Presenta: 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.
26:45ENLex FridmanPódcast · 2 vocesStart Small, Build Habits, Pick Your Team: Andrew Ng's Practical Advice for Learning Deep LearningInvitado: Andrew Ng · Presenta: 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.
11:23ENLex FridmanPódcast · 2 vocesScience Is a Self-Improving System That Isn't Exploding: Chollet on the Limits of an Intelligence ExplosionInvitado: François Chollet · Presenta: Lex FridmanFrançois Chollet uses the history of science to argue that recursive self-improvement meets exponential friction and yields linear, not explosive, progress.
51:18ENLex FridmanPódcast · 2 vocesEngineering Intelligence: Lex Fridman's Opening Lecture for MIT's AGI CourseInvitado: MIT AGI · Presenta: 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.