필터 결과
344개정렬
1:03:13EN
Lex Fridman· 2019년 9월 30일팟캐스트 · 2명You Can't Have Both Kinds of Fairness, Says Peter Norvig게스트 Peter Norvig · 진행자 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· 2019년 9월 14일팟캐스트 · 2명Intelligence is not the height of a building: François Chollet on Keras, deep learning, and why AGI will not explode게스트 François Chollet · 진행자 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· 2019년 8월 31일팟캐스트 · 2명Human Intelligence Is a Ridiculously Narrow Sliver게스트 Yann Lecun · 진행자 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· 2019년 8월 27일팟캐스트 · 2명A Single GPU Is Enough: Jeremy Howard on Teaching Deep Learning게스트 Jeremy Howard · 진행자 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· 2019년 7월 1일팟캐스트 · 2명The Brain Does Not Classify Images. It Navigates Maps.게스트 Jeff Hawkins · 진행자 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· 2019년 4월 18일팟캐스트 · 2명A Bar in Montreal, Then a Night of Code: Ian Goodfellow on Why GANs Work게스트 Ian Goodfellow · 진행자 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· 2019년 1월 19일팟캐스트 · 2명Einstein Was the Worst of Five Ph.D. Students게스트 Tomaso Poggio · 진행자 Lex FridmanMIT neuroscientist Tomaso Poggio on why intelligence is science's deepest problem, and what brains still have to teach machines.
46:25ENLex Fridman· 2019년 1월 17일팟캐스트Deep Learning at the Edge of 2019: BERT, Autopilot, and the Ideas That Broke Through진행자 Lex FridmanA tour of the architectural, applied, and automation breakthroughs that defined deep learning in 2018 and set up 2019.
1:19:59ENLex Fridman· 2018년 12월 23일팟캐스트 · 2명Schmidhuber’s bet: true meta-learning rewrites the learner, not the last layer게스트 Juergen Schmidhuber · 진행자 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· 2018년 12월 16일팟캐스트 · 2명A one-legged robot learned a backflip from "this is better"게스트 Pieter Abbeel · 진행자 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· 2018년 11월 16일팟캐스트 · 2명The Hardest Part of Learning Was Left Out of the Textbooks게스트 Vladimir Vapnik · 진행자 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· 2018년 10월 20일팟캐스트 · 2명An Adult Can Change Last Year's Mind. A Network Still Cannot.게스트 Yoshua Bengio · 진행자 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· 2016년 9월 27일팟캐스트From Word Vectors to Memory Networks: The Building Blocks of Deep NLP진행자 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 Fridman팟캐스트Deep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network Foundations진행자 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"팟캐스트From Autocomplete to Alien Optimizer: Riley Goodside on Three Eras of Prompting진행자 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 Fridman팟캐스트Deep Reinforcement Learning from First Principles: Rewards, Q-Learning, Policy Gradients and AlphaZero진행자 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 Fridman팟캐스트 · 2명Craftsmanship, First Principles and the Road to Self-Driving: Jim Keller on Working with Elon Musk게스트 Jim Keller · 진행자 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 Fridman팟캐스트Is Language or Vision Harder? Ilya Sutskever on the Unity of Machine Learning진행자 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 Fridman팟캐스트 · 2명Deep Learning Is a System 1 Machine: Daniel Kahneman on What AI Still Lacks게스트 Daniel Kahneman · 진행자 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, & Startups팟캐스트 · 2명Biohub's Bet: Building Open AI World Models of Biology From Proteins Up to Cells진행자 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 Fridman팟캐스트From Hand-Built Features to ResNets: Andrej Karpathy's Crash Course in Convolutional Networks진행자 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 Fridman팟캐스트 · 2명Machine Learning for Business Students: Powerful, Narrow, and Easier to Fool Than You Think게스트 MIT Sloan · 진행자 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 Fridman팟캐스트 · 2명Yann LeCun on Benchmarks, Interactive Environments, and Why Human Intelligence Is Not General게스트 Yann Lecun · 진행자 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 Fridman팟캐스트From Sparse Coding to GANs: A Tour of Unsupervised Deep Learning in 2016진행자 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.더 보기344개 중 24개 표시