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248 件
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1:56:35EN
Cognitive Revolution "How AI Changes Everything"ポッドキャストFrom Autocomplete to Alien Optimizer: Riley Goodside on Three Eras of Promptingホスト 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.モデルと学習Prompting & in-context learningPrompt injection+2
1:07:29EN
Lex FridmanポッドキャストDeep Reinforcement Learning from First Principles: Rewards, Q-Learning, Policy Gradients and AlphaZeroホスト 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.モデルと学習Reinforcement LearningDeep Learning+3
24:22EN
Lex Fridmanポッドキャスト · 2 人Craftsmanship, First Principles and the Road to Self-Driving: Jim Keller on Working with Elon Muskゲスト Jim Keller · ホスト 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.モデルと学習Autonomous DrivingAutonomous Systems+3
9:59EN
Lex FridmanポッドキャストIs Language or Vision Harder? Ilya Sutskever on the Unity of Machine Learningホスト 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.モデルと学習機械学習の基礎Deep Learning+3
15:01EN
Lex Fridmanポッドキャスト · 2 人Deep Learning Is a System 1 Machine: Daniel Kahneman on What AI Still Lacksゲスト Daniel Kahneman · ホスト 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.モデルと学習Deep LearningAI research+3
56:20EN
No Priors: AI, Machine Learning, Tech, & Startupsポッドキャスト · 2 人Biohub's Bet: Building Open AI World Models of Biology From Proteins Up to Cellsホスト 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.モデルと学習AI for scienceOpen-source ecosystems+3
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
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