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37:00EN
Lex FridmanPodcastInside 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.Modelle & TrainingRecommendation systemsSearch & retrieval+3
1:31:52EN
Lex FridmanPodcastFrom 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.Modelle & TrainingDeep LearningMultimodal AI+2
17:40EN
Lex FridmanPodcastFrom 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.Modelle & TrainingReinforcement LearningAI research+3
1:10:32EN
Lenny's PodcastPodcast · 2 StimmenA Billion Dollars, No VC, Under 100 People: Surge AI's Edwin Chen on Data Quality and Where AI Is HeadingGast: Edwin Chen · Moderation: 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.Modelle & TrainingCompany BuildingData quality & curation+3
1:10:19EN
Unsupervised Learning: With Jacob EffronPodcastThe Robot That Folded the Laundry: Physical Intelligence’s Three FrontiersModeration: Jacob EffronKarol Hausman and Danny Driess explain how robot foundation models moved from coded primitives to web-scale pretraining, and why performance is now the hard gate.Modelle & TrainingRoboticsAI research+3
1:07:35EN
Lex FridmanPodcastWhy 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.Modelle & TrainingHuman-AI CollaborationML-Grundlagen+3
10:16EN
Lex FridmanPodcast · 2 StimmenReasoning Without Logic: Yann LeCun on Teaching Neural Networks to ThinkGast: Yann Lecun · Moderation: Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.Modelle & TrainingML-GrundlagenDeep Learning+3
1:24:01EN
Lenny's PodcastPodcastFrom Sandwich Math to Expert Data: Scale AI's CEO on What Actually Makes Models SmarterModeration: Lenny RachitskyJason Droege explains what really happened with Meta's investment in Scale AI, how data labeling turned into expert work, and what Uber Eats taught him about building businesses.Modelle & TrainingCompany BuildingData quality & curation+3
16:41EN
Lex FridmanPodcastAlphaFold 2 and the Protein-Folding Breakthrough: Why Lex Fridman Calls It a Landmark for AI and BiologyModeration: Lex FridmanA quick explainer on DeepMind's CASP14 result, how protein folding works, how AlphaFold 1 and 2 likely differ, and what the advance could unlock.Modelle & TrainingAI for scienceDeep Learning+2
23:54EN
No Priors: AI, Machine Learning, Tech, & StartupsPodcast · 2 StimmenBeyond GPT-7: The Customization Toolkit, Meta's Open Source Bet and the Return of Consumer AIModeration: 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.Modelle & TrainingFine-TuningRetrieval-Augmented Generation+3
1:19:52EN
Lex FridmanPodcastFrom 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.Modelle & TrainingAutonomous DrivingDeep Learning+3
57:21EN
Cognitive Revolution "How AI Changes Everything"PodcastGemini 2.0 Flash Goes to Production: Pricing, Model Lineup and What Developers Should Build NextModeration: Nathan LabenzGoogle DeepMind's Logan Kilpatrick walks through launch-day Gemini 2.0 releases, rate limits, model naming, and where he sees startup opportunities.Modelle & TrainingBenchmarksAI cost & performance+3
1:27:30EN
Lex FridmanPodcastFrom 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.Modelle & TrainingReinforcement LearningDeep Learning+2
1:05:15EN
Lex FridmanPodcastTaming 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.Modelle & TrainingAutonomous DrivingRobotics+3
1:27:15EN
Lex FridmanPodcastPolicy 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.Modelle & TrainingReinforcement LearningML-Grundlagen+3
1:19:21EN
Lex FridmanPodcastVapnik'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.Modelle & TrainingML-GrundlagenAI research+2
1:03:25EN
Lex FridmanPodcastTheano 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.Modelle & TrainingDeep LearningML-Grundlagen+3
1:08:06EN
Lex FridmanPodcast · 2 StimmenDeep Learning in One Lecture: From Perceptrons to AutoML and the Limits of LearningGast: Deep Learning Basics · Moderation: 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.Modelle & TrainingDeep LearningML-Grundlagen+3
57:25EN
Unsupervised Learning: With Jacob EffronPodcastFrom RAG to Reasoning: Why the Future of AI Is a System, Not a ModelModeration: Jacob EffronDouwe Kiela explains what OpenAI's o1 reveals about test-time compute, why enterprise AI must be specialized, and where post-training and evaluations are heading.Modelle & TrainingReasoning & test-time computePost-training+3
13:58EN
Lex FridmanPodcastDeep 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.Modelle & TrainingReinforcement LearningML-Grundlagen+3
57:55EN
Lex FridmanPodcastFrom 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.Modelle & TrainingReinforcement LearningDeep Learning+3
1:13:22EN
Lex FridmanPodcastThe 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.Modelle & TrainingAutonomous DrivingRobotics+3
57:15EN
Lex FridmanPodcastInside Torch and Autograd: How Deep Learning Libraries Compute GradientsModeration: Lex FridmanAlex Wiltschko of Twitter walks through Lua Torch, then explains automatic differentiation and what separates modern deep learning frameworks.Modelle & TrainingML-GrundlagenDeep Learning+1
1:12:12EN
Lenny's PodcastPodcastModels Are Grown, Not Built: What Anthropic's Interpretability Research Sees Inside ClaudeModeration: Lenny RachitskyAnthropic researcher Emmanuel Ameisen explains how studying a language model's intermediate computations reveals concepts, plans and surprising quirks, and why that matters for alignment.Modelle & TrainingModel InterpretabilityAI research+3
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