絞り込み結果
83 件並び順
52:28ENRay 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.
1:00:15ENCircuit 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.
20:50ENGlitches as Features: What John Hopfield Says Artificial Neural Networks Are Missingホスト Lex FridmanIn a short conversation with Lex Fridman, physicist and neural-network pioneer John Hopfield compares evolved brains with today's artificial networks and explains why he expects AI to borrow from biology generation after generation.
11:59ENFrom 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.
26:45ENStart 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.
1:31:29ENIs Driving More Like Chess or Conversation? Lex Fridman's Opening Lecture on Deep Learning for Self-Driving Carsホスト Lex FridmanThe first lecture of MIT 6.S094 introduces neural networks, reinforcement learning and computer vision through the question of how hard driving really is.
1:12:00ENWhy Deep Learning Works at All: Yoshua Bengio on Composition, Saddle Points and the Case for Unsupervised Learningホスト Lex FridmanIn a 2016 Deep Learning School talk, Yoshua Bengio explains the assumptions that let neural nets beat the curse of dimensionality and lays out the open challenges ahead.
1:02:24ENFrom Looping Forklifts to Self-Driving Cities: Sertac Karaman on Motion Planningホスト Lex FridmanAn MIT professor traces motion planning from the DARPA Urban Challenge to the future of transportation and the cities built around cars.
1:27:41ENDeep Learning in 2020: Lex Fridman's Tour of Transformers, Self-Play, Self-Driving and What Comes Nextホスト Lex FridmanAn MIT lecture surveys the biggest advances of 2017 to 2019 across language, reinforcement learning, deep learning theory and autonomous vehicles, and lists Fridman's hopes for the year ahead.
51:18ENEngineering 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.
1:02:06ENDeep Learning for Self-Driving Cars: Why Edge Cases and Human Nature Are the Real Problemホスト Lex FridmanLex Fridman opens MIT's 6.S094 with a tour of what deep learning is, how it works, what it has achieved and where it still breaks, framed around autonomous vehicles.









