筛选结果
7 篇排序
2:59:44EN → 简体中文
Cognitive Revolution "How AI Changes Everything"· 2026年6月3日播客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。
1:01:14ENCognitive Revolution "How AI Changes Everything"· 2026年5月20日播客The Model Eats the Scaffolding: How Google Plans to Ship Agents Everywhere主持 Nathan LabenzThree years after the leaked “no moat” memo, DeepMind’s Gemini leads walk through 3.5 Flash, Omni, and the shared agent harness now running through Google’s products.
44:34EN
Unsupervised Learning: With Jacob Effron· 2025年2月19日播客The Cheaper Intelligence Gets, the More of It We'll Buy主持 Jacob EffronAmazon AGI Lab head David Luan on what DeepSeek actually meant, why next-token prediction is not enough, and why agents are still stuck on reliability.
26:28EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年9月12日播客 · 2 人对谈API prices fell 200x. The LLM table still is not set.主持 Sarah Guo, Elad GilSarah Guo and Elad Gil, with no guest this week, argue over whether the language-model market has already consolidated — and why falling prices, five-person labs, and AMD’s shopping spree all cut against a simple ending.
42:08EN
Sequoia Capital· 2024年7月30日播客Meta Trained a 405B Teacher on 16,000 GPUs — Then Told the World to Distill ItTwo days after Llama 3.1 405B shipped, Meta's generative-AI product lead walks Sequoia through why the most expensive model in the family is free, and why he thinks the frontier itself is heading toward commodity.
1:12:15ENUnsupervised Learning: With Jacob Effron播客The Open-Model Vibe Check: China, Containment, and the Data Moat主持 Jacob EffronJacob Effron and guests debate Chinese open-weight models, distillation, containment policy, the OpenAI–Hugging Face incident, and Cursor's data advantage.
1:13:40ENUnsupervised Learning: With Jacob Effron播客The Long Tail, the World Model, and the 1995 Problem: Inside Waymo's Scaling Phase and Robotics' Missing Moment主持 Jacob EffronVincent, a distinguished engineer at Waymo, explains how foundation models became teachers for self-driving, why the long tail and causality are the real frontier, and where robotics still lacks its generalist proof point.