필터 결과
86개정렬
1:13:33ENA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still Miss진행자 Jacob EffronThe Attention Is All You Need co-author tells Unsupervised Learning that transformers will probably keep winning — and that they still do not learn the way people do.
2:02:17ENSAEs Find the Nouns. Parameter Decomposition Looks for the Verbs진행자 Nathan LabenzGoodfire's Lee Sharkey argues that sparse autoencoders recover a network's variables, not the algorithms those variables run, and shows how stochastic parameter decomposition tries to recover the computations themselves.
1:37:05ENAn Algebraic Geometer Quit Tenure Because the Bowl-Shaped Loss Plot Is a Lie진행자 Nathan LabenzJesse Hoogland and Daniel Murfet of Timaeus argue that singularities in the loss landscape — not the smooth valleys in every slide deck — are what actually govern how neural networks learn, generalize, and go wrong.
2:12:00ENGradient Descent at Test Time: Titans Pushes LLM Context to 10 Million Tokens진행자 Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.
2:33:39ENThe Attacker Goes Second: Why Nicholas Carlini Keeps Breaking AI Defenses진행자 Nathan LabenzA Google DeepMind security researcher on simple loss functions, 70% robust accuracy, and why the fix has to live in the system, not the model.
1:40:22ENTwenty-atom crystals taught an AI the physics of a heartbeat진행자 Nathan LabenzOrbital Materials' Jonathan Godwin and Tim Duignan tell Nathan Labenz how a neural network trained on tiny crystals simulated a potassium ion channel on a single 2017 GPU.
1:42:16EN267 Papers in Seven Months: Albert Gu Explains the State Space Model Revolution진행자 Nathan LabenzThe Mamba co-author walks through five years of recurrent-model work, why selective compression beats a growing KV cache, and what Mamba 2 trades away to train faster.
1:54:01ENA Tiny Network Grew a Salt Crystal the Training Data Never Showed진행자 Nathan LabenzTim Duignan trained tens of thousands of parameters on salt water. The model invented a crystal — and a continuum solvent theory he had spent a PhD failing to write by hand.
1:58:43ENA Worm With 302 Neurons Already Makes Trade-offs. AI Is Still on the Same Ladder.진행자 Nathan LabenzMax Bennett maps 600 million years of brain evolution onto five breakthroughs — and asks which rungs today's models have actually reached.
1:19:24ENA Toy Paper Did Not Prove LLMs Cannot Generalize. The Discourse Still Ran With It.진행자 Nathan LabenzNathan Labenz and Erik Torenberg unpack a viral DeepMind result, a weekend reproduction, and a premature war over voluntary AI commitments.
43:45ENEight Years of No Growth, Then OpenAI and NVIDIA Became His Customers진행자 Sarah Guo, Elad GilWeights & Biases CEO Lukas Biewald tells No Priors how a labeling company with no chasm to cross funded the ML developer tools that trained most of the large language models.
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.
1:52:46ENCharge Neural Nets by Connection Length, and the Black Box Splits Into Modules You Can See진행자 Nathan LabenzMIT physicist Ziming Liu walks Nathan Labenz through BIMT, a brain-inspired training trick that sparsifies networks until you can read them by looking.
1:25:50ENPrompt Engineering Became Programming, and Riley Goodside Is Fine With That진행자 Nathan LabenzScale AI’s first staff prompt engineer on tool use, fine-tuning without amnesia, generate-then-rank, and why the 2023 job-of-the-future story never arrived.
46:57EN95% of Drug Programs Fail. Daphne Koller Is Betting Machine Learning on the First Decision진행자 Sarah Guo, Elad GilA new medicine still takes more than a decade and well over a billion dollars — and Insitro's founder argues the expensive failures start with the wrong target, indication, or patient.
2:31:57ENJay McClelland on How Thought Emerges From Neural Networks게스트 Jay McClelland · 진행자 Lex FridmanThe Stanford cognitive scientist talks with Lex Fridman about connectionism, backpropagation, David Rumelhart, and why intuition discovers what logic later proves.
2:51:33ENThe Self Is a GPT Prompt: Wojciech Zaremba on Consciousness, Codex, and Fermi's Silence게스트 Wojciech Zaremba · 진행자 Lex FridmanOpenAI co-founder Wojciech Zaremba tells Lex Fridman he thinks we might be alone in the universe — and that GPT-3, Codex, and a one-handed robot already hint at how intelligence is built.
2:18:29ENYour Cortex Is 150,000 Brains Voting on a Coffee Cup게스트 Jeff Hawkins · 진행자 Lex FridmanJeff Hawkins tells Lex Fridman that intelligence is learning a model of the world, the neocortex is tens of thousands of independent maps that vote, and the fastest path to machine intelligence is reverse-engineering that sheet.
2:30:30ENThe Fallacy of the Successful First Step게스트 Ishan Misra · 진행자 Lex FridmanBerkeley's Jitendra Malik explains why computer vision still looks easy, why fully autonomous driving is not near, and what children know that today's networks do not.
1:56:17ENIf You Reran Earth a Million Times, Would Anything Like Us Appear?게스트 Risto Miikkulainen · 진행자 Lex FridmanRisto Miikkulainen on neuroevolution, hyena raids, basil that never sleeps, and why variation-plus-selection still finds solutions backpropagation cannot see.
1:49:56ENA 21-Year-Old at a Party Called GameStop. He Was Right.게스트 Richard Craib · 진행자 Lex FridmanNumerai founder Richard Craib talks with Lex Fridman about WallStreetBets, crowdsourced machine learning, and why finance is a machine for pulling the future into the present.
2:02:45ENSteal the Code: You Get Zero Points for Making the Algorithm Run진행자 Lex FridmanCharles Isbell and Michael Littman tell Lex Fridman why machine learning is not just statistics, why struggle has to stay hopeful, and what a $6,600 online master's actually sells.
2:01:07ENMichael Littman isn’t waiting for a runaway god. He’s watching us.게스트 Michael Littman · 진행자 Lex FridmanThe Brown reinforcement-learning researcher tells Lex Fridman why accidental superintelligence feels like a 747 assembling itself — and why social media, rote language, and theory of mind worry him more.
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