絞り込み結果
51 件並び順
1:13:33EN
Unsupervised Learning: With Jacob Effron· 2026年6月3日ポッドキャストA Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still Missホスト 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.
2:02:17EN
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.
1:37:05ENCognitive 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.
2:12:00ENCognitive 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.
2:33:39ENCognitive 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.
1:40:22ENCognitive 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.
1:42:16ENCognitive 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.
1:54:01ENCognitive 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.
1:58:43ENCognitive 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.
1:19:24ENCognitive 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.
1:33:56ENCognitive 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.
1:32:22ENCognitive 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.
1:52:46ENCognitive 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.
1:25:50ENCognitive 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.
2:02:45EN
Lex Fridman· 2020年12月26日ポッドキャストSteal the Code: You Get Zero Points for Making the Algorithm Runホスト 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.
45:31ENLex Fridman· 2020年7月1日ポッドキャストYou Are Your Own Existence Proof: Karl Friston on Free Energy, Life, and Self-Awarenessホスト Lex FridmanKarl Friston explains how the free energy principle reframes existence, life, planning, and self-awareness as inference problems.
26:02ENLex Fridman· 2020年2月9日ポッドキャストMoore’s Law Is Not Dead—It Is a Cascade of S-Curvesホスト Lex FridmanJim Keller explains why transistor scaling keeps outrunning its obituary, and how exponential hardware changes the mathematics AI can attempt.
46:25ENLex Fridman· 2019年1月17日ポッドキャストDeep Learning at the Edge of 2019: BERT, Autopilot, and the Ideas That Broke Throughホスト Lex FridmanA tour of the architectural, applied, and automation breakthroughs that defined deep learning in 2018 and set up 2019.
1:29:03ENLex Fridman· 2016年9月27日ポッドキャストFrom Word Vectors to Memory Networks: The Building Blocks of Deep NLPホスト Lex FridmanRichard Socher explains how word embeddings, recurrent models, GRUs, pointer mixtures, and dynamic memory networks turn language tasks into trainable end-to-end systems.
1:00:51ENLex FridmanポッドキャストDeep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network Foundationsホスト Lex FridmanA fast, equation-level walk through feed-forward networks, backpropagation, practical training tricks, dropout and batch normalization, from the 2016 Deep Learning School.
1:07:29ENLex FridmanポッドキャストDeep Reinforcement Learning from First Principles: Rewards, Q-Learning, Policy Gradients and AlphaZeroホスト Lex FridmanLex Fridman's MIT 6.S091 opening lecture walks from the basic RL framework through DQN, actor-critic methods and AlphaZero to the unsolved problem of moving from simulation to the real world.
9:59ENLex FridmanポッドキャストIs Language or Vision Harder? Ilya Sutskever on the Unity of Machine Learningホスト Lex FridmanIn a short clip from his 2020 conversation with Lex Fridman, Ilya Sutskever explains why vision, language and reinforcement learning share the same few principles, and why asking which problem is harder is the wrong question.
1:25:16ENLex FridmanポッドキャストFrom Hand-Built Features to ResNets: Andrej Karpathy's Crash Course in Convolutional Networksホスト Lex FridmanA 2016 Deep Learning School talk covering how convnets work, how the ImageNet winners evolved, and how to apply them without being a hero.
1:24:43ENLex FridmanポッドキャストFrom Sparse Coding to GANs: A Tour of Unsupervised Deep Learning in 2016ホスト Lex FridmanRuslan Salakhutdinov walks through autoencoders, Boltzmann machines, variational autoencoders and adversarial networks at the Deep Learning School, with candid notes on what did and did not work.もっと見る51 件中 24 件を表示