필터 결과
70개정렬
1:33:56ENThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models Understand진행자 Nathan LabenzNathan Labenz spends a solo episode walking through what general-purpose models can do, how they are trained, how capabilities unlock, and whether they understand anything at all.
1:32:22ENA Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer Away진행자 Nathan LabenzLili Yu of Meta AI explains a multiscale transformer that predicts million-byte sequences, skips tokenization, and aims to model any file as raw bytes.
5:47ENSam Altman on Scaling AI Models: Matthew Berman Examines OpenAI’s 2023 DirectionMatthew Berman · Forward FutureA 2023 discussion of model scaling, possible ways to improve GPT-4 and the safety priorities OpenAI described at the time.
40:25ENA 6-Billion-Parameter Clone of ChatGPT’s Core Trick진행자 Sarah Guo, Elad GilDatabricks CTO Matei Zaharia tells Sarah Guo and Elad Gil how Dolly learned instruction-following on fifty times less data than GPT-3 — and why he would rather look facts up than bake them into a trillion-parameter network.
45:03ENEmad Mostaque’s bet: open small models, not one giant brain진행자 Sarah Guo, Elad GilStability AI’s founder tells No Priors why foundation models should be public infrastructure, why small models can beat giant ones, and what that means for media, medicine, and AGI.
1:00:36ENForty games of Diplomacy, and nobody spotted the bot: Noam Brown’s 100,000x lesson진행자 Sarah Guo, Elad GilMeta’s Noam Brown tells Sarah Guo and Elad Gil how Cicero negotiated in English for 40 games on a tiny web community — and why thinking at decision time beat three years of scaling.
1:36:11EN2029 Passes the Turing Test. 2045 Is Still the Singularity게스트 Ray Kurzweil · 진행자 Lex FridmanRay Kurzweil tells Lex Fridman why his dates have not moved, how to invent in the present tense, and why the replica of his father answered the meaning of life with one word: love.
1:29:10ENAndrew Ng: The Hardest Part of AI Is Rarely the Algorithm게스트 Andrew Ng · 진행자 Lex FridmanIn a conversation with Lex Fridman, Ng traces a path from late-night Coursera recordings to factory floors, arguing that messy data, habits, and starting small matter more than hype.
26:02ENMoore’s Law Is Not Dead—It Is a Cascade of S-Curves진행자 Lex FridmanJim Keller explains why transistor scaling keeps outrunning its obituary, and how exponential hardware changes the mathematics AI can attempt.
1:10:19ENThe Robot That Folded the Laundry: Physical Intelligence’s Three Frontiers진행자 Jacob EffronKarol Hausman and Danny Driess explain how robot foundation models moved from coded primitives to web-scale pretraining, and why performance is now the hard gate.
1:14:59ENWhy Anthropic's Ben Mann Thinks the Exponential Hasn't Slowed, and Why He Still Worries About Safety진행자 Lenny RachitskyThe Anthropic co-founder on the AI talent war, scaling laws, the economic Turing test, jobs, leaving OpenAI, constitutional AI and how he forecasts the risk of superintelligence.
1:19:48ENBigger Models, Cleaner Splits and Human Baselines: Andrew Ng's Practical Recipe for Applied Deep Learning진행자 Lex FridmanIn a 2016 Deep Learning School talk, Andrew Ng distills lessons from leading Baidu's AI team into a workflow for diagnosing models, handling data and building a career.
1:13:40ENThe Long Tail, the World Model, and the 1995 Problem: Inside Waymo's Scaling Phase and Robotics' Missing Moment진행자 Jacob EffronVincent, a distinguished engineer at Waymo, explains how foundation models became teachers for self-driving, why the long tail and causality are the real frontier, and where robotics still lacks its generalist proof point.
52:28ENRay Kurzweil on Hierarchies, the Neocortex and Merging with AI게스트 Ray Kurzweil · 진행자 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.









