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14 件
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2:02:17EN
Cognitive Revolution "How AI Changes Everything"· 2025年8月27日ポッドキャストSAEs Find the Nouns. Parameter Decomposition Looks for the Verbsホスト 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.モデルと学習Model InterpretabilityAI research+3
1:37:05EN
Cognitive Revolution "How AI Changes Everything"· 2025年6月19日ポッドキャストAn Algebraic Geometer Quit Tenure Because the Bowl-Shaped Loss Plot Is a Lieホスト 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.モデルと学習Model Interpretability機械学習の基礎+3
2:12:00EN
Cognitive Revolution "How AI Changes Everything"· 2025年5月15日ポッドキャストGradient Descent at Test Time: Titans Pushes LLM Context to 10 Million Tokensホスト Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.モデルと学習Agent memoryコンテキストエンジニアリング+3
2:33:39EN
Cognitive Revolution "How AI Changes Everything"· 2025年2月27日ポッドキャストThe Attacker Goes Second: Why Nicholas Carlini Keeps Breaking AI Defensesホスト 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.セキュリティと安全性AI security安全性とガバナンス+3
1:40:22EN
Cognitive Revolution "How AI Changes Everything"· 2025年1月22日ポッドキャストTwenty-atom crystals taught an AI the physics of a heartbeatホスト 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.モデルと学習AI for science機械学習の基礎+3
1:42:16EN
Cognitive Revolution "How AI Changes Everything"· 2024年7月4日ポッドキャスト267 Papers in Seven Months: Albert Gu Explains the State Space Model Revolutionホスト 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.モデルと学習Transformers & attentionInference+3
1:54:01EN
Cognitive Revolution "How AI Changes Everything"· 2024年6月26日ポッドキャストA Tiny Network Grew a Salt Crystal the Training Data Never Showedホスト 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.モデルと学習AI for science機械学習の基礎+3
1:58:43EN
Cognitive Revolution "How AI Changes Everything"· 2024年2月7日ポッドキャストA Worm With 302 Neurons Already Makes Trade-offs. AI Is Still on the Same Ladder.ホスト Nathan LabenzMax Bennett maps 600 million years of brain evolution onto five breakthroughs — and asks which rungs today's models have actually reached.モデルと学習AI HistoryNeuroscience & AI+3
1:19:24EN
Cognitive Revolution "How AI Changes Everything"· 2023年11月17日ポッドキャストA Toy Paper Did Not Prove LLMs Cannot Generalize. The Discourse Still Ran With It.ホスト Nathan LabenzNathan Labenz and Erik Torenberg unpack a viral DeepMind result, a weekend reproduction, and a premature war over voluntary AI commitments.モデルと学習AI research機械学習の基礎+3
1:33:56EN
Cognitive Revolution "How AI Changes Everything"· 2023年7月6日ポッドキャストThe 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.モデルと学習機械学習の基礎Scaling Laws+3
1:32:22EN
Cognitive Revolution "How AI Changes Everything"· 2023年6月29日ポッドキャストA 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.モデルと学習Multimodal AITransformers & attention+3
1:52:46EN
Cognitive Revolution "How AI Changes Everything"· 2023年6月27日ポッドキャストCharge 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.モデルと学習Model Interpretability機械学習の基礎+3
1:25:50EN
Cognitive Revolution "How AI Changes Everything"· 2023年4月18日ポッドキャストPrompt Engineering Became Programming, and Riley Goodside Is Fine With Thatホスト 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.企業と戦略Prompting & in-context learningStructured outputs+3
1:03:10EN
Cognitive Revolution "How AI Changes Everything"ポッドキャストPause Tokens, Analogical Prompts and Ring Attention: A Tour of the Transformer's Next Upgradesホスト Nathan LabenzNathan Labenz and Trey Kollmer walk through five recent papers on pausing, backspacing, prompting, streaming and long-context training, and sketch how they might combine into a more capable model.モデルと学習Transformers & attention機械学習の基礎+3