필터 결과
7개정렬
9:59EN
Lex Fridman팟캐스트Is Language or Vision Harder? Ilya Sutskever on the Unity of Machine Learning진행자 Lex FridmanIn a short clip from his 2020 conversation with Lex Fridman, Ilya Sutskever explains why vision, language and reinforcement learning share the same few principles, and why asking which problem is harder is the wrong question.
11:11ENLex Fridman팟캐스트 · 2명Yann LeCun on Benchmarks, Interactive Environments, and Why Human Intelligence Is Not General게스트 Yann Lecun · 진행자 Lex FridmanIn a 2019 clip, LeCun defends benchmarks as a test of AI claims, explains why interactive environments break the usual train-test protocol, and argues that "general intelligence" is a misnomer.
10:16ENLex Fridman팟캐스트 · 2명Reasoning Without Logic: Yann LeCun on Teaching Neural Networks to Think게스트 Yann Lecun · 진행자 Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.
13:58ENLex Fridman팟캐스트Deep Reinforcement Learning, Explained by the Researcher Behind AlphaGo진행자 Lex FridmanDavid Silver of DeepMind walks through what reinforcement learning is, how its building blocks fit together, and why deep neural networks keep surprising even the experts.
10:34ENLex Fridman팟캐스트 · 2명Why Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AI게스트 François Chollet · 진행자 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.
10:39ENLex Fridman팟캐스트Statistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between Them진행자 Lex FridmanBerkeley's Michael I. Jordan explains where statistics came from, why it is really about decisions, and how empirical Bayes and false discovery rate bridge two rival philosophies.
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.