絞り込み結果
278 件並び順
46:25EN
Lex Fridman· 2019年1月17日ポッドキャストDeep Learning at the Edge of 2019: BERT, Autopilot, and the Ideas That Broke Throughホスト Lex FridmanA tour of the architectural, applied, and automation breakthroughs that defined deep learning in 2018 and set up 2019.
1:19:59ENLex Fridman· 2018年12月23日ポッドキャスト · 2 人Schmidhuber’s bet: true meta-learning rewrites the learner, not the last layerゲスト Juergen Schmidhuber · ホスト Lex FridmanJürgen Schmidhuber tells Lex Fridman how Gödel machines, compression-as-science, Power Play, LSTMs, and world models all descend from a teenage plan to multiply one person’s curiosity into a better physicist than he could ever be.
42:45ENLex Fridman· 2018年12月16日ポッドキャスト · 2 人A one-legged robot learned a backflip from "this is better"ゲスト Pieter Abbeel · ホスト 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· 2018年11月16日ポッドキャスト · 2 人The Hardest Part of Learning Was Left Out of the Textbooksゲスト Vladimir Vapnik · ホスト 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· 2018年10月20日ポッドキャスト · 2 人An Adult Can Change Last Year's Mind. A Network Still Cannot.ゲスト Yoshua Bengio · ホスト 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.
1:29:03ENLex Fridman· 2016年9月27日ポッドキャストFrom Word Vectors to Memory Networks: The Building Blocks of Deep NLPホスト Lex FridmanRichard Socher explains how word embeddings, recurrent models, GRUs, pointer mixtures, and dynamic memory networks turn language tasks into trainable end-to-end systems.
1:00:51ENLex FridmanポッドキャストDeep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network Foundationsホスト Lex FridmanA fast, equation-level walk through feed-forward networks, backpropagation, practical training tricks, dropout and batch normalization, from the 2016 Deep Learning School.
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.
1:07:29ENLex 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.
24:22ENLex 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.
9:59ENLex 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.
15:01ENLex 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.
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.
1:25:16ENLex 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.
1:28:53ENLex 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.
11:11ENLex 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.
1:24:43ENLex 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.
1:17:38ENLex 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.
58:46ENLex 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.
1:23:18ENCognitive 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.
1:15:59ENLex 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.
1:03:59ENCognitive 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.
18:46ENLex 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.
37:00ENLex 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.もっと見る278 件中 24 件を表示