絞り込み結果
49 件並び順
1:25:01EN
Lex 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.
1:17:29ENLex Fridman· 2019年9月23日ポッドキャスト · 2 人The Algorithm Is Already Better. The Clinic Still Uses 1967.ゲスト Regina Barzilay · ホスト Lex FridmanMIT's Regina Barzilay on why deep learning already beats the breast-cancer 'density' score — and why data, liability, and incentives still will not move.
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.
1:44:11ENLex 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.
1:10:58ENLex Fridman· 2019年6月3日ポッドキャスト · 2 人The Hadoop Lesson That Made Google Give TensorFlow Awayゲスト Rajat Monga · ホスト Lex FridmanIn November 2015, Google put its core internal machine-learning system on GitHub. Rajat Monga explains why a company of that size would hand over the hard-to-copy part.
1:46:01ENLex Fridman· 2019年4月29日ポッドキャスト · 2 人The Night AlphaStar Beat TLO, the Team Went to a Barゲスト Oriol Vinyals · ホスト Lex FridmanOriol Vinyals on how DeepMind turned StarCraft II into a sequence problem, built a league of agents, and still refuses to call the game solved.
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.
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.
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.
54:33ENLex Fridmanポッドキャスト · 2 人Vision or Lidar, Human or Machine: Lex Fridman's 2019 Map of the Self-Driving Raceゲスト Self-Driving Cars · ホスト Lex FridmanThe opening lecture of MIT's 6.S094 course surveys 2018's milestones, the industry's optimistic predictions, and the beliefs about driving and humans that will decide who wins.
10:16ENLex 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.
17:21ENLex Fridmanポッドキャスト · 2 人How Alexa Learned to Hear From Across the Room: Rohit Prasad on Far-Field Speech and Intentゲスト Rohit Prasad · ホスト Lex FridmanAlexa's head scientist recalls the early days of the Echo, when far-field speech recognition seemed unsolvable and there were no customers to collect data from.
1:08:06ENLex Fridmanポッドキャスト · 2 人Deep Learning in One Lecture: From Perceptrons to AutoML and the Limits of Learningゲスト Deep Learning Basics · ホスト Lex FridmanLex Fridman's opening lecture for MIT's 6.S094 course surveys why deep learning took off, how neural networks learn, and where the technique still falls short.
24:08ENLex Fridmanポッドキャスト · 2 人Intelligence Is Not Just Prediction: David Ferrucci on Explanation, Persuasion and Shared Meaningゲスト David Ferrucci · ホスト Lex FridmanThe lead of IBM's Watson team argues that predicting well is only half of intelligence, and that being understood by others is the other half.
10:34ENLex Fridmanポッドキャスト · 2 人Why Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AIゲスト François Chollet · ホスト Lex FridmanFrançois Chollet explains why neural networks only interpolate between points they have seen, and why the best AI systems pair them with symbolic rules.
52:28ENLex Fridmanポッドキャスト · 2 人Ray Kurzweil on Hierarchies, the Neocortex and Merging with AIゲスト Ray Kurzweil · ホスト Lex FridmanIn an MIT AGI course lecture, Kurzweil connects the history of neural networks to his hierarchical theory of the neocortex, language understanding at Google, longevity, jobs and existential risk.
1:00:15ENLex Fridmanポッドキャスト · 2 人Circuit Search, Hindsight and Self-Play: Ilya Sutskever's Case for Where Learning Is Goingゲスト Ilya Sutskever · ホスト Lex FridmanIn an MIT AGI lecture, OpenAI's co-founder explains why deep learning works, then walks through meta-learning, hindsight experience replay, sim-to-real transfer, self-play and learning goals from human feedback.
11:59ENLex Fridmanポッドキャスト · 2 人From a Side Project to TensorFlow's Core: François Chollet on How Keras Came to Beゲスト François Chollet · ホスト Lex FridmanThe creator of Keras recalls why he built it in 2015 around recurrent networks, how it moved onto TensorFlow, and what TensorFlow 2.0 means for its design.
26:45ENLex Fridmanポッドキャスト · 2 人Start Small, Build Habits, Pick Your Team: Andrew Ng's Practical Advice for Learning Deep Learningゲスト Andrew Ng · ホスト Lex FridmanIn a clip from the Lex Fridman Podcast, Andrew Ng explains how to begin in deep learning, how to study effectively, and how to choose a career path.
51:18ENLex Fridmanポッドキャスト · 2 人Engineering Intelligence: Lex Fridman's Opening Lecture for MIT's AGI Courseゲスト MIT AGI · ホスト Lex FridmanLex Fridman lays out an engineering-first approach to artificial general intelligence, previews the course's projects and guest speakers, and frames deep learning's limits.