絞り込み結果
248 件並び順
35:24ENThe Author of Attention Is All You Need Now Compiles mRNA Like Bytecodeホスト Sarah Guo, Elad GilJakob Uszkoreit on why Transformers won, why models spend compute on the wrong things, and why drug discovery has to become generation rather than screening.
2:13:07ENHalf an Hour to a Circuit in GPT-2 Small — and a Quadratic Wall Before GPT-3ホスト Nathan LabenzArthur Conmy walks Nathan Labenz through ACDC, the algorithm that automates the most tedious step of mechanistic interpretability: finding which transformer components actually do a task.
12:20ENTesting UltraLM 13B: A Small Model’s Strengths and Reasoning ErrorsMatthew Berman · Forward FutureA 2023 local test finds useful writing and simple coding alongside failures in games, arithmetic and logic.
1:33:56ENThe AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models Understandホスト 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 Awayホスト 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 Seeホスト 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.
59:28ENFrom $500,000 to $50,000: MosaicML Made Training Your Own Model a Priced Productホスト Nathan LabenzJonathan Frankle and Abhi Venigalla explain who is training custom LLMs, why extraction beats chat, and how ALiBi stretches a 65k context window.
2:00:19EN1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2ホスト Nathan LabenzTry this fill-in: Lily likes cats and dogs. She asked her mom for a dog. Mom said no, so she asked for a…
1:25:35ENTwo and a Half Years From 33.3% to 86.5%: Google Sent Foundation Models to Medical Schoolホスト Nathan LabenzVivek Natarajan walks Nathan Labenz through Med-PaLM and Med-PaLM 2: the data-light techniques, the nine-axis clinician evals, and why a high USMLE-style score is the wrong trophy.
42:18ENTen Percent of Search Is Health — and the Doctor Is Googling Tooホスト Sarah Guo, Elad GilGoogle researcher Karan Singhal on Med-PaLM 2, the real bar for medical language models, and why medicine is a harder alignment lab than chatbots.
5:24ENGoogle I/O 2023: Bard, PaLM 2 and AI Across Google's ProductsMatthew Berman · Forward FutureMatthew Berman reviews Google's 2023 AI announcements and weighs its focus on improving established products.
4:14:33ENThe Next Token Is Not a Proof: Stephen Wolfram on ChatGPT and Computational Truthゲスト Stephen Wolfram · ホスト Lex FridmanIn his fourth conversation with Lex Fridman, Stephen Wolfram contrasts large language models with Wolfram|Alpha, then follows computational irreducibility from ChatGPT through observers, thermodynamics, and the three great theories of twentieth-century physics.
40:25ENA 6-Billion-Parameter Clone of ChatGPT’s Core Trickホスト 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.
3:28:47ENKarpathy Calls Trained Neural Nets Complicated Alien Artifactsゲスト Andrej Karpathy · ホスト Lex FridmanOn Lex Fridman's podcast, Andrej Karpathy refuses the brain analogy, walks the Fermi paradox through Nick Lane's chemistry, and treats the Transformer as a general-purpose differentiable computer.
2:10:08ENGato Is Only the Beginningゲスト Oriol Vinyals · ホスト Lex FridmanDeepMind's Oriol Vinyals talks with Lex Fridman about generalist agents, thrown-away weights, and whether human-level AGI arrives in his lifetime.
2:10:39ENDemis Hassabis: Solve Intelligence, Then Solve Everything Elseゲスト Demis Hassabis · ホスト Lex FridmanThe DeepMind CEO tells Lex Fridman why the Turing Test is the wrong exam, how AlphaFold rewrote biology, and why the quiet radio sky suggests we are probably alone.
2:45:10ENA Teenager Learns to Drive in Twenty Hours. A Car Still Cannot.ゲスト Yann Lecun · ホスト Lex FridmanYann LeCun tells Lex Fridman why self-supervised world models are the missing dark matter of intelligence, and why machines are not even at the level of a house cat.
2:31:57ENJay McClelland on How Thought Emerges From Neural Networksゲスト Jay McClelland · ホスト 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 Silenceゲスト Wojciech Zaremba · ホスト 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 Cupゲスト Jeff Hawkins · ホスト 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 Stepゲスト Ishan Misra · ホスト 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?ゲスト Risto Miikkulainen · ホスト Lex FridmanRisto Miikkulainen on neuroevolution, hyena raids, basil that never sleeps, and why variation-plus-selection still finds solutions backpropagation cannot see.
2:01:07ENMichael Littman isn’t waiting for a runaway god. He’s watching us.ゲスト Michael Littman · ホスト 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.
2:10:06ENYou Might Have to Understand the World to Understand the Letter Aゲスト Dileep George · ホスト Lex FridmanDileep George on recursive cortical networks, why a five-year-old can read any new CAPTCHA, and what the Wright brothers actually learned from birds.
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