필터 결과
83개정렬
52:28EN
Lex Fridman팟캐스트 · 2명Ray Kurzweil on Hierarchies, the Neocortex and Merging with AI게스트 Ray Kurzweil · 진행자 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 Fridman팟캐스트 · 2명Circuit Search, Hindsight and Self-Play: Ilya Sutskever's Case for Where Learning Is Going게스트 Ilya Sutskever · 진행자 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.
20:50ENLex Fridman팟캐스트Glitches as Features: What John Hopfield Says Artificial Neural Networks Are Missing진행자 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.
11:59ENLex Fridman팟캐스트 · 2명From a Side Project to TensorFlow's Core: François Chollet on How Keras Came to Be게스트 François Chollet · 진행자 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 Fridman팟캐스트 · 2명Start Small, Build Habits, Pick Your Team: Andrew Ng's Practical Advice for Learning Deep Learning게스트 Andrew Ng · 진행자 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.
1:31:29ENLex Fridman팟캐스트Is Driving More Like Chess or Conversation? Lex Fridman's Opening Lecture on Deep Learning for Self-Driving Cars진행자 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.
1:12:00ENLex Fridman팟캐스트Why Deep Learning Works at All: Yoshua Bengio on Composition, Saddle Points and the Case for Unsupervised Learning진행자 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.
1:02:24ENLex Fridman팟캐스트From Looping Forklifts to Self-Driving Cities: Sertac Karaman on Motion Planning진행자 Lex FridmanAn MIT professor traces motion planning from the DARPA Urban Challenge to the future of transportation and the cities built around cars.
1:27:41ENLex Fridman팟캐스트Deep Learning in 2020: Lex Fridman's Tour of Transformers, Self-Play, Self-Driving and What Comes Next진행자 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.
51:18ENLex Fridman팟캐스트 · 2명Engineering Intelligence: Lex Fridman's Opening Lecture for MIT's AGI Course게스트 MIT AGI · 진행자 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.
1:02:06ENLex Fridman팟캐스트Deep Learning for Self-Driving Cars: Why Edge Cases and Human Nature Are the Real Problem진행자 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.