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16 slides23:39ZH → English
跟李沐学AI· Jul 23, 2025Li Mu: Adding Ten Million Hours of Speech to a Text LLM, Open-Sourcing Higgs Audio李沐 · Boson AILi Mu explains how the team added a huge amount of speech data to text LLM training, built an AudioLLM that can listen and speak, and open-sourced Higgs Audio. He covers architecture trade-offs, token compression, where the data came from, and how to run it on a 3090.Voice AIMultimodal AIModels & training+2
18:46EN
Lex FridmanPodcastDeep Learning Plus One Idea: Ilya Sutskever on Building AGIHost Lex FridmanIn a May 2020 conversation, Sutskever discusses self-play, simulation, bodies and consciousness, tests of intelligence, and how an AGI might be governed and aligned.Models & trainingAGIReinforcement Learning+3
17:40EN
Lex FridmanPodcastFrom AlphaGo Zero to MuZero: How Self-Play Strips Knowledge Out of AIHost Lex FridmanDeepMind's David Silver explains why removing human knowledge made AlphaZero stronger and more general, and how MuZero extended the idea to worlds with unknown rules.Models & trainingReinforcement LearningAI research+3
16:41EN
Lex FridmanPodcastAlphaFold 2 and the Protein-Folding Breakthrough: Why Lex Fridman Calls It a Landmark for AI and BiologyHost Lex FridmanA quick explainer on DeepMind's CASP14 result, how protein folding works, how AlphaFold 1 and 2 likely differ, and what the advance could unlock.Models & trainingAI for scienceDeep Learning+2
20:36EN
Lex FridmanPodcastWhy Simple Theories Win: Marcus Hutter on Occam's Razor, Compression and Kolmogorov ComplexityHost Lex FridmanDeepMind researcher Marcus Hutter explains Solomonoff induction, why compression equals understanding, and how a simple universe can still look rich.Models & trainingML fundamentalsAI research+3
20:50EN
Lex FridmanPodcastGlitches as Features: What John Hopfield Says Artificial Neural Networks Are MissingHost 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.Models & trainingNeuroscience & AIML fundamentals+3