필터 결과
7개정렬
2:59:44EN
Cognitive Revolution "How AI Changes Everything"· 2026년 6월 3일팟캐스트Two Endangered Languages Broke Transformers. Nested Learning Gave Models a Sleep Button.진행자 Nathan LabenzGoogle researcher Ali Behrouz tells Nathan Labenz how stacking update frequencies — not just layers — and an offline sleep phase could give language models genuine continual learning.
1:01:14ENCognitive Revolution "How AI Changes Everything"· 2026년 5월 20일팟캐스트The Model Eats the Scaffolding: How Google Plans to Ship Agents Everywhere진행자 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· 2025년 2월 19일팟캐스트The Cheaper Intelligence Gets, the More of It We'll Buy진행자 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.
2:49:35ZH
Zhang Xiaojun Podcast· 2025년 2월 4일팟캐스트 · 2명潘嘉怡解读 R1、o1 与 Kimi K1.5：强化学习如何教模型学会推理게스트 潘嘉怡 · 진행자 张小珺伯克利博士生潘嘉怡带读 DeepSeek、OpenAI 与 Kimi 的推理模型报告，拆解长思维链、GRPO、奖励设计与蒸馏。R1-Zero 的“顿悟时刻”展示了推理策略如何在强化学习中涌现。
42:08EN
Sequoia Capital· 2024년 7월 30일팟캐스트Meta 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 Effron팟캐스트The Open-Model Vibe Check: China, Containment, and the Data Moat진행자 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 Effron팟캐스트The Long Tail, the World Model, and the 1995 Problem: Inside Waymo's Scaling Phase and Robotics' Missing Moment진행자 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.