필터 결과
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1:12:00EN
Lex 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.
45:12EN
Sequoia Capital팟캐스트Inside a Robot Data Factory: How Mecka Sensorizes Humans to Train RobotsMecka AI founder Josh Gao explains how a company collects, quality-checks and licenses egocentric human data, and why robotics will arrive as a steady unlock of tasks rather than a single breakthrough.
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.
11:23ENLex Fridman팟캐스트 · 2명Science Is a Self-Improving System That Isn't Exploding: Chollet on the Limits of an Intelligence Explosion게스트 François Chollet · 진행자 Lex FridmanFrançois Chollet uses the history of science to argue that recursive self-improvement meets exponential friction and yields linear, not explosive, progress.
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.