필터 결과
347개정렬
37:00EN
Lex Fridman팟캐스트Inside the YouTube Algorithm: Related Graphs, Satisfaction Surveys and the Viewers Who Are Part of the Code진행자 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.
1:31:52ENLex Fridman팟캐스트From Phonemes to CTC: How Deep Learning Rebuilt Speech Recognition진행자 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.
17:40ENLex Fridman팟캐스트From AlphaGo Zero to MuZero: How Self-Play Strips Knowledge Out of AI진행자 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.
1:10:32EN
Lenny's Podcast팟캐스트 · 2명A Billion Dollars, No VC, Under 100 People: Surge AI's Edwin Chen on Data Quality and Where AI Is Heading게스트 Edwin Chen · 진행자 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.
1:10:19EN
Unsupervised Learning: With Jacob Effron팟캐스트The Robot That Folded the Laundry: Physical Intelligence’s Three Frontiers진행자 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.
1:07:35ENLex Fridman팟캐스트Why AI Will Never Be Perfect: Lex Fridman's Case for Keeping Humans in the Loop진행자 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.
10:16ENLex Fridman팟캐스트 · 2명Reasoning Without Logic: Yann LeCun on Teaching Neural Networks to Think게스트 Yann Lecun · 진행자 Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.
1:24:01ENLenny's Podcast팟캐스트From Sandwich Math to Expert Data: Scale AI's CEO on What Actually Makes Models Smarter진행자 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.
16:41ENLex Fridman팟캐스트AlphaFold 2 and the Protein-Folding Breakthrough: Why Lex Fridman Calls It a Landmark for AI and Biology진행자 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.
23:54EN
No Priors: AI, Machine Learning, Tech, & Startups팟캐스트 · 2명Beyond GPT-7: The Customization Toolkit, Meta's Open Source Bet and the Return of Consumer AI진행자 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.
1:19:52ENLex Fridman팟캐스트From Pixels to Steering Wheels: Convolutional Networks and End-to-End Driving진행자 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.
57:21EN
Cognitive Revolution "How AI Changes Everything"팟캐스트Gemini 2.0 Flash Goes to Production: Pricing, Model Lineup and What Developers Should Build Next진행자 Nathan LabenzGoogle DeepMind's Logan Kilpatrick walks through launch-day Gemini 2.0 releases, rate limits, model naming, and where he sees startup opportunities.
1:27:30ENLex Fridman팟캐스트From NAND Gates to Self-Driving Traffic: Deep Reinforcement Learning in One Lecture진행자 Lex FridmanLex Fridman's second MIT 6.S094 lecture walks from the humble perceptron to deep Q-learning and the browser-based DeepTraffic competition.
1:05:15ENLex Fridman팟캐스트Taming the Long Tail: How Waymo Builds a Self-Driving Factory진행자 Lex FridmanWaymo principal scientist Drago Anguelov explains how machine learning infrastructure, hybrid design and smart simulated agents tackle the rare events of autonomous driving.
1:27:15ENLex Fridman팟캐스트Policy Gradients vs. Q-Learning: John Schulman's Field Guide to Deep Reinforcement Learning진행자 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.
1:19:21ENLex Fridman팟캐스트Vapnik's Case for Learning by Predicates, Not Just by More Data진행자 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.
1:03:25ENLex Fridman팟캐스트Theano in an Hour: Symbolic Graphs, Automatic Gradients and a Compiler That Rewrites Your Math진행자 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.
1:08:06ENLex Fridman팟캐스트 · 2명Deep Learning in One Lecture: From Perceptrons to AutoML and the Limits of Learning게스트 Deep Learning Basics · 진행자 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.
57:25ENUnsupervised Learning: With Jacob Effron팟캐스트From RAG to Reasoning: Why the Future of AI Is a System, Not a Model진행자 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.
13:58ENLex Fridman팟캐스트Deep Reinforcement Learning, Explained by the Researcher Behind AlphaGo진행자 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.
57:55ENLex Fridman팟캐스트From Q-Tables to AlphaGo Zero: A Self-Driving Car Lecture on Deep Reinforcement Learning진행자 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.
1:13:22ENLex Fridman팟캐스트The Second 90%: What It Really Takes to Put Deep Learning on a Driverless Car진행자 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.
57:15ENLex Fridman팟캐스트Inside Torch and Autograd: How Deep Learning Libraries Compute Gradients진행자 Lex FridmanAlex Wiltschko of Twitter walks through Lua Torch, then explains automatic differentiation and what separates modern deep learning frameworks.
1:12:12ENLenny's Podcast팟캐스트Models Are Grown, Not Built: What Anthropic's Interpretability Research Sees Inside Claude진행자 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.더 보기347개 중 24개 표시