筛选结果
32 篇排序
1:08:06ENDeep Learning in One Lecture: From Perceptrons to AutoML and the Limits of Learning嘉宾 Deep Learning Basics · 主持 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.
1:00:25ENCan Machines Think? A Guided Tour of Turing's Test, Its Critics and Its Successors嘉宾 Turing Test · 主持 Lex FridmanLex Fridman walks through Alan Turing's 1950 paper, the objections to it, and the benchmarks that have tried to replace or extend the imitation game.
40:25ENA Language That Knows the World: Stephen Wolfram on Computable Knowledge主持 Lex FridmanIn this clip from the Lex Fridman Podcast, Stephen Wolfram explains what makes the Wolfram Language different from ordinary programming languages and how it grew into a computable knowledge base, from Wolfram Alpha to computational contracts.
1:20:58ENFrom Bag of Words to Attention: Quoc Le Teaches Sequence-to-Sequence Learning Through Email Replies主持 Lex FridmanA Deep Learning School talk that builds sequence-to-sequence models step by step around one running example, automatic email reply, and then surveys memory and operation-augmented networks.
52:28ENRay Kurzweil on Hierarchies, the Neocortex and Merging with AI嘉宾 Ray Kurzweil · 主持 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.
32:41ENHow IBM Watson Won Jeopardy: A Moonshot Built on Engineering, Not a Breakthrough嘉宾 David Ferrucci · 主持 Lex FridmanDavid Ferrucci tells Lex Fridman how he talked IBM into the Jeopardy challenge and how his team built Watson's search, scoring and confidence machinery in about four years.
11:59ENFrom a Side Project to TensorFlow's Core: François Chollet on How Keras Came to Be嘉宾 François Chollet · 主持 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.
1:27:41ENDeep Learning in 2020: Lex Fridman's Tour of Transformers, Self-Play, Self-Driving and What Comes Next主持 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.









