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344 件
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1:25:16EN
Lex FridmanポッドキャストFrom Hand-Built Features to ResNets: Andrej Karpathy's Crash Course in Convolutional Networksホスト 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.モデルと学習Deep LearningComputer Vision+3
1:28:53EN
Lex Fridmanポッドキャスト · 2 人Machine Learning for Business Students: Powerful, Narrow, and Easier to Fool Than You Thinkゲスト MIT Sloan · ホスト 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.モデルと学習機械学習の基礎AI Strategy+3
11:11EN
Lex Fridmanポッドキャスト · 2 人Yann LeCun on Benchmarks, Interactive Environments, and Why Human Intelligence Is Not Generalゲスト Yann Lecun · ホスト 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.モデルと学習BenchmarksAGI(汎用人工知能)+3
1:24:43EN
Lex FridmanポッドキャストFrom Sparse Coding to GANs: A Tour of Unsupervised Deep Learning in 2016ホスト 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.モデルと学習Deep Learning機械学習の基礎
1:17:38EN
Lex Fridmanポッドキャスト · 2 人Brains Are Body Regulators: Lisa Feldman Barrett on Why Emotions Are Constructed, Not Detectedゲスト Lisa Feldman Barrett · ホスト 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.モデルと学習Neuroscience & AIAGI(汎用人工知能)+3
58:46EN
Lex FridmanポッドキャストCaging the Neural Network: How Aptiv Thinks About Safe Self-Drivingホスト 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.モデルと学習Autonomous DrivingDeep Learning+3
1:23:18EN
Cognitive Revolution "How AI Changes Everything"ポッドキャストFrom GPT-2 to GPT-3 Moments in Robotics: Six DeepMind Papers on Bridging Robots and the Internetホスト Nathan LabenzKeerthana Gopalakrishnan and Ted Xiao walk through RT-2, RT-X, RT-Trajectory, AutoRT, Learning to Learn Faster and PIVOT, and say what is still missing for general-purpose robots.モデルと学習RoboticsMultimodal AI+3
1:15:59EN
Lex FridmanポッドキャストSteering Through Time: How Recurrent Networks and LSTMs Learn from Sequencesホスト 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.モデルと学習Deep Learning機械学習の基礎+2
1:03:59EN
Cognitive Revolution "How AI Changes Everything"ポッドキャストBuilding a World Model of Life: Biohub's Alex Rives on Why Better Measurement Must Come Before Virtual Cellsホスト Nathan LabenzBiohub's head of science explains why today's virtual cell models fall short, how a $1.8 billion data push aims to fix it, and what a five-year information-level model of the cell could unlock.モデルと学習AI for scienceComputational Biology+3
18:46EN
Lex FridmanポッドキャストDeep Learning Plus One Idea: Ilya Sutskever on Building AGIホスト Lex FridmanIn a May 2020 conversation, Sutskever discusses self-play, simulation, bodies and consciousness, tests of intelligence, and how an AGI might be governed and aligned.モデルと学習AGI(汎用人工知能)Reinforcement Learning+3
37:00EN
Lex FridmanポッドキャストInside the YouTube Algorithm: Related Graphs, Satisfaction Surveys and the Viewers Who Are Part of the Codeホスト 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.モデルと学習Recommendation systemsSearch & retrieval+3
1:31:52EN
Lex FridmanポッドキャストFrom Phonemes to CTC: How Deep Learning Rebuilt Speech Recognitionホスト 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.モデルと学習Deep LearningMultimodal AI+2
17:40EN
Lex FridmanポッドキャストFrom AlphaGo Zero to MuZero: How Self-Play Strips Knowledge Out of AIホスト 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.モデルと学習Reinforcement LearningAI research+3
1:10:32EN
Lenny's Podcastポッドキャスト · 2 人A Billion Dollars, No VC, Under 100 People: Surge AI's Edwin Chen on Data Quality and Where AI Is Headingゲスト Edwin Chen · ホスト 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.モデルと学習Company BuildingData quality & curation+3
1:10:19EN
Unsupervised Learning: With Jacob EffronポッドキャストThe Robot That Folded the Laundry: Physical Intelligence’s Three Frontiersホスト 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.モデルと学習RoboticsAI research+3
1:07:35EN
Lex FridmanポッドキャストWhy AI Will Never Be Perfect: Lex Fridman's Case for Keeping Humans in the Loopホスト 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.モデルと学習Human-AI Collaboration機械学習の基礎+3
10:16EN
Lex Fridmanポッドキャスト · 2 人Reasoning Without Logic: Yann LeCun on Teaching Neural Networks to Thinkゲスト Yann Lecun · ホスト Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.モデルと学習機械学習の基礎Deep Learning+3
1:24:01EN
Lenny's PodcastポッドキャストFrom Sandwich Math to Expert Data: Scale AI's CEO on What Actually Makes Models Smarterホスト 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.モデルと学習Company BuildingData quality & curation+3
16:41EN
Lex FridmanポッドキャストAlphaFold 2 and the Protein-Folding Breakthrough: Why Lex Fridman Calls It a Landmark for AI and Biologyホスト 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.モデルと学習AI for scienceDeep Learning+2
23:54EN
No Priors: AI, Machine Learning, Tech, & Startupsポッドキャスト · 2 人Beyond GPT-7: The Customization Toolkit, Meta's Open Source Bet and the Return of Consumer AIホスト 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.モデルと学習ファインチューニング検索拡張生成(RAG)+3
1:19:52EN
Lex FridmanポッドキャストFrom Pixels to Steering Wheels: Convolutional Networks and End-to-End Drivingホスト 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.モデルと学習Autonomous DrivingDeep Learning+3
57:21EN
Cognitive Revolution "How AI Changes Everything"ポッドキャストGemini 2.0 Flash Goes to Production: Pricing, Model Lineup and What Developers Should Build Nextホスト Nathan LabenzGoogle DeepMind's Logan Kilpatrick walks through launch-day Gemini 2.0 releases, rate limits, model naming, and where he sees startup opportunities.モデルと学習BenchmarksAI cost & performance+3
1:27:30EN
Lex FridmanポッドキャストFrom NAND Gates to Self-Driving Traffic: Deep Reinforcement Learning in One Lectureホスト Lex FridmanLex Fridman's second MIT 6.S094 lecture walks from the humble perceptron to deep Q-learning and the browser-based DeepTraffic competition.モデルと学習Reinforcement LearningDeep Learning+2
1:05:15EN
Lex FridmanポッドキャストTaming the Long Tail: How Waymo Builds a Self-Driving Factoryホスト Lex FridmanWaymo principal scientist Drago Anguelov explains how machine learning infrastructure, hybrid design and smart simulated agents tackle the rare events of autonomous driving.モデルと学習Autonomous DrivingRobotics+3
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