筛选结果
51 篇排序
1:13:33EN
Unsupervised Learning: With Jacob Effron· 2026年6月3日播客A Trillion Tokens Later, the Concept Finally Lands: Łukasz Kaiser on What Transformers Still Miss主持 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日播客SAEs 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.
1:37:05EN → 简体中文Cognitive Revolution "How AI Changes Everything"· 2025年6月19日播客一位代数几何学家辞去终身教职，因为碗形的损失曲线图是假的主持 Nathan LabenzTimaeus 的 Jesse Hoogland 和 Daniel Murfet 认为，真正决定神经网络如何学习、如何泛化、又如何出错的，是损失景观中的奇点，而不是每份幻灯片里那些平滑的山谷。
2:12:00ENCognitive Revolution "How AI Changes Everything"· 2025年5月15日播客Gradient Descent at Test Time: Titans Pushes LLM Context to 10 Million Tokens主持 Nathan LabenzA Cornell PhD student's neural memory module holds about 70% accuracy where GPT-4's scores fall off a cliff.
2:33:39EN → 简体中文Cognitive Revolution "How AI Changes Everything"· 2025年2月27日播客攻击者后出手：Nicholas Carlini 为什么总能攻破 AI 防御主持 Nathan Labenz谷歌 DeepMind 安全研究员谈简单的损失函数、70% 的稳健准确率，以及为什么修复必须落在系统层面，单靠模型本身不够。
1:40:22ENCognitive Revolution "How AI Changes Everything"· 2025年1月22日播客Twenty-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.
1:42:16ENCognitive Revolution "How AI Changes Everything"· 2024年7月4日播客267 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.
1:54:01ENCognitive Revolution "How AI Changes Everything"· 2024年6月26日播客A 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.
1:58:43ENCognitive Revolution "How AI Changes Everything"· 2024年2月7日播客A Worm With 302 Neurons Already Makes Trade-offs. AI Is Still on the Same Ladder.主持 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日播客A Toy Paper Did Not Prove LLMs Cannot Generalize. The Discourse Still Ran With It.主持 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日播客The AI Scouting Report: 90 Minutes From GPT-4's Exam Scores to Whether Models Understand主持 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日播客A Million Bytes and 256 Symbols: Meta's MEGABYTE Wants to Throw the Tokenizer Away主持 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日播客Charge 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.
1:25:50ENCognitive Revolution "How AI Changes Everything"· 2023年4月18日播客Prompt Engineering Became Programming, and Riley Goodside Is Fine With That主持 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 → 简体中文
Lex Fridman· 2020年12月26日播客偷代码：让算法跑起来本身一分不给主持 Lex FridmanCharles Isbell 和 Michael Littman 与 Lex Fridman 聊了为什么机器学习不只是统计，为什么挣扎必须保有希望，以及 6600 美元的在线硕士到底卖的是什么。
45:31ENLex Fridman· 2020年7月1日播客You Are Your Own Existence Proof: Karl Friston on Free Energy, Life, and Self-Awareness主持 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日播客Moore’s Law Is Not Dead—It Is a Cascade of S-Curves主持 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日播客Deep Learning at the Edge of 2019: BERT, Autopilot, and the Ideas That Broke Through主持 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日播客From Word Vectors to Memory Networks: The Building Blocks of Deep NLP主持 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播客Deep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network Foundations主持 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播客Deep Reinforcement Learning from First Principles: Rewards, Q-Learning, Policy Gradients and AlphaZero主持 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播客Is Language or Vision Harder? Ilya Sutskever on the Unity of Machine Learning主持 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播客From Hand-Built Features to ResNets: Andrej Karpathy's Crash Course in Convolutional Networks主持 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播客From Sparse Coding to GANs: A Tour of Unsupervised Deep Learning in 2016主持 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.加载更多已显示 24 篇，共 51 篇