絞り込み結果
345 件並び順
55:24EN
Lex Fridman· 2019年10月27日ポッドキャスト · 2 人The Machine Has a Steady Hand: Garry Kasparov on Deep Blue, AlphaZero, and Putinゲスト Garry Kasparov · ホスト Lex FridmanThe former world champion tells Lex Fridman why losing to IBM still hurt, why chess was never the peak of intellect, and why he lives in New York.
1:25:01ENLex Fridman· 2019年10月3日ポッドキャスト · 2 人Gary Marcus: Deep Learning Imitates Intelligence. It Does Not Understand.ゲスト Gary Marcus · ホスト Lex FridmanOn Lex Fridman's podcast, the NYU emeritus and Rebooting AI co-author argues that perception and reasoning need different tools — and that trustworthy AI is impossible until machines can represent harm, bottles, and intent.
1:03:13ENLex Fridman· 2019年9月30日ポッドキャスト · 2 人You Can't Have Both Kinds of Fairness, Says Peter Norvigゲスト Peter Norvig · ホスト Lex FridmanThe co-author of Artificial Intelligence: A Modern Approach walks Lex Fridman through recidivism scores, utility functions, Lisp, MOOCs, and why human-level intelligence should not be the goal.
1:59:49ENLex Fridman· 2019年9月14日ポッドキャスト · 2 人Intelligence is not the height of a building: François Chollet on Keras, deep learning, and why AGI will not explodeゲスト François Chollet · ホスト Lex FridmanThe Keras creator tells Lex Fridman that intelligence lives in a brain, a body, and a problem, that science already is a recursively self-improving superhuman system with linear output, and that the real near-term danger is the newsfeed.
1:15:58ENLex Fridman· 2019年8月31日ポッドキャスト · 2 人Human Intelligence Is a Ridiculously Narrow Sliverゲスト Yann Lecun · ホスト Lex FridmanYann LeCun tells Lex Fridman why giant nets plus gradient descent broke the textbooks, why "AGI" is the wrong word, and why the next leap is a world model learned like a baby.
1:44:11ENLex Fridman· 2019年8月27日ポッドキャスト · 2 人A Single GPU Is Enough: Jeremy Howard on Teaching Deep Learningゲスト Jeremy Howard · ホスト Lex FridmanThe fast.ai founder tells Lex Fridman why domain experts, not more papers or more TPUs, will decide whether deep learning actually helps people.
2:09:42ENLex Fridman· 2019年7月1日ポッドキャスト · 2 人The Brain Does Not Classify Images. It Navigates Maps.ゲスト Jeff Hawkins · ホスト Lex FridmanJeff Hawkins tells Lex Fridman why the neocortex stores the world as thousands of voting reference frames, and why scaling today's networks will not get us there.
1:08:37ENLex Fridman· 2019年4月18日ポッドキャスト · 2 人A Bar in Montreal, Then a Night of Code: Ian Goodfellow on Why GANs Workゲスト Ian Goodfellow · ホスト Lex FridmanLex Fridman talks with Ian Goodfellow about the 2014 invention of generative adversarial networks, adversarial examples as a security problem, and what still has to be built before machines can hold a fact the way a person does.
1:20:21ENLex Fridman· 2019年1月19日ポッドキャスト · 2 人Einstein Was the Worst of Five Ph.D. Studentsゲスト Tomaso Poggio · ホスト Lex FridmanMIT neuroscientist Tomaso Poggio on why intelligence is science's deepest problem, and what brains still have to teach machines.
46:25ENLex 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.もっと見る345 件中 24 件を表示