筛选结果
14 篇排序
1:47:38EN19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every Chip主持 Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.
2:59:44EN → 简体中文两门濒危语言难倒了 Transformer,嵌套学习给模型加上“睡眠”按钮主持 Nathan LabenzGoogle 研究员 Ali Behrouz 向 Nathan Labenz 讲解,语言模型要获得真正的持续学习,不能只堆叠层数,还要堆叠更新频率,并加入一个离线的睡眠阶段。
2:02:17ENSAEs Find the Nouns. Parameter Decomposition Looks for the Verbs主持 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.
1:54:08EN → 简体中文为什么 Anthropic 的第一笔投资给了一家为神经网络造显微镜的公司主持 Nathan LabenzGoodfire 的 Dan Balsam 和 Tom McGrath 认为,机制可解释性(mechanistic interpretability)如今已经有了一个准范式,留着两道顽固的缺口,还有三款今天就值得推出的产品。
1:43:54ENA Year to Grow a Brain Organoid, an Hour to Simulate It主持 Nathan LabenzStanford postdoc Siyu He tells Nathan Labenz how Squidiff and CORAL move single-cell and spatial experiments into the computer — and where the models still cannot go.
1:40:22ENTwenty-atom crystals taught an AI the physics of a heartbeat主持 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 Revolution主持 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 Showed主持 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.
1:52:18ENTwo Weeks, Thirty Compiles: How Adam Majmudar Wrote a GPU From Scratch主持 Nathan LabenzA Cognitive Revolution walkthrough of TinyGPU, from SIMD kernels and memory latency to an open-source chip flow that runs on a laptop.
1:19:24ENA Gibberish Prompt Unlocks What GPT-J Plans Three Tokens Ahead主持 Nathan LabenzNortheastern’s Bau Lab shows that a 40,000-parameter soft prompt can read a model’s future words, and that about ten attention heads carry the task itself.
2:30:06EN → 简体中文训练 token 只有 GPT-4 的三十分之一,Mamba 在 Transformer 的主场赢了主持 Nathan Labenz在一期单人紧急节目中,Nathan Labenz 认为,Albert Gu 和 Tri Dao 提出的选择性状态空间模型,是 2017 年以来第一个有资格与 Transformer 分享王座的架构。
1:40:07ENThirty thousand voltage updates a second: inside Skydio’s autonomy stack主持 Nathan LabenzHayk Martiros walks Nathan Labenz through a two-pound drone that sees in every direction, runs more than ten neural nets on board, and will not be weaponized.
1:52:46ENCharge Neural Nets by Connection Length, and the Black Box Splits Into Modules You Can See主持 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.
1:21:20ENFreeze the eyes and the brain. Train only the optic nerve.主持 Nathan LabenzJunnan Li and Dongxu Li tell Nathan Labenz how BLIP cleaned noisy web captions, and how BLIP-2 connected a frozen vision model to a frozen language model in under ten days on one A100.









