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2:41:47EN
Lex Fridman· 2021年6月12日播客 · 2 人对谈Geometry Is the Cilantro of Math嘉宾 Jordan Ellenberg · 主持 Lex FridmanJordan Ellenberg tells Lex Fridman that the last 150 years of mathematics have been about one question: when two things count as the same.MathematicsMachine LearningScience Communication+2
1:49:51EN
Lex Fridman· 2020年6月27日播客 · 2 人对谈The Idea Once Called a Devolution Now Powers Almost Every New Chip嘉宾 David Patterson · 主持 Lex FridmanTuring Award winner David Patterson talks with Lex Fridman about RISC, RAID, Moore's Law, open hardware, and why chips without a software stack die.基础设施与部署Open-source ecosystemsBenchmarks+1
57:30EN
Lex Fridman· 2019年9月26日播客 · 2 人对谈400 Spins in a Pocket, and a Universe Too Small to Store Them嘉宾 Leonard Susskind · 主持 Lex FridmanLeonard Susskind tells Lex Fridman why quantum computers are not faster laptops, why string theory is a tool rather than a citizenship, and why nobody should claim to have found the bottom of the well.Black HolesConsciousnessPhilosophy of Science+2
1:28:53EN
Lex Fridman播客 · 2 人对谈Machine Learning for Business Students: Powerful, Narrow, and Easier to Fool Than You Think嘉宾 MIT Sloan · 主持 Lex FridmanLex Fridman's non-technical MIT Sloan lecture explains how machine learning works, why it is so capable at perception, and where it still breaks in the real world.模型与训练机器学习基础AI Strategy+3
37:00EN
Lex Fridman播客Inside the YouTube Algorithm: Related Graphs, Satisfaction Surveys and the Viewers Who Are Part of the Code主持 Lex FridmanYouTube's head of search and discovery, Cristos Goodrow, explains to Lex Fridman how search and recommendations work, which signals matter, and how the team measures whether viewers are better off.模型与训练Recommendation systemsSearch & retrieval+3
10:39EN
Lex Fridman播客Statistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between Them主持 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.模型与训练机器学习基础Decision Making+2