筛选结果
13 篇排序
2:10:42EN
Cognitive Revolution "How AI Changes Everything"· 2026年8月28日播客The Exam Papers Nobody Audits主持 Nathan LabenzFrontier labs buy reinforcement-learning environments from a cottage industry of small vendors, and almost nobody checks whether those tests match the real work.
2:13:09ENCognitive Revolution "How AI Changes Everything"· 2026年8月26日播客One Chain of Thought, 100 Million Tokens: RL Is a Hell of a Drug主持 Nathan LabenzApollo Research's Bronson Schoen on reward-seeking models, invented slang, and why chain-of-thought monitoring will not save us.
1:48:43ENCognitive Revolution "How AI Changes Everything"· 2026年5月1日播客The reward jumped from 3 to 180. Every Hacker News title was a Google layoff.主持 Nathan LabenzCoreWeave's Kyle Corbitt walks Nathan Labenz through GRPO, rubrics, RL environments, and why most reward hacks are easy to catch once you know to look.
1:18:46ENCognitive Revolution "How AI Changes Everything"· 2026年3月16日播客90 Slides in 45 Minutes: A Full-Time AI Scout Admits He Can No Longer Keep Up主持 Nathan LabenzNathan Labenz’s LexLab briefing for lawyers runs through the good, the bad, and the weird of frontier models — from triplicate second opinions on his son’s cancer care to AIs that know they are being tested.
1:49:53ENCognitive Revolution "How AI Changes Everything"· 2026年3月5日播客Don't Fight Backprop: Goodfire's Bet on Designing What Models Learn主持 Nathan LabenzDan Balsam and Tom McGrath return to The Cognitive Revolution with a $150 million Series B, a new research pillar called intentional design, and a rule for anyone who wants to steer training: stop trying to out-muscle gradient descent.
54:26ENCognitive Revolution "How AI Changes Everything"· 2026年2月22日播客Ten Billion Active Parameters, and the Unsexy Work of Making Them Agent-Ready主持 Nathan LabenzMiniMax researcher Olive Song explains how M2 was trained with expert developers as reward models, interleaved thinking, and a perturbation pipeline that treats every scaffold change as the real generalization test.
1:04:50ENCognitive Revolution "How AI Changes Everything"· 2025年11月25日播客Useful AI Work Is Doubling Every Four Months. Schools Should Plan Accordingly.主持 Nathan LabenzIn a keynote to K-12 educators, Cognitive Revolution host Nathan Labenz argued that task length, not chat quality, is the number that should reshape how schools think.
1:28:48ENCognitive Revolution "How AI Changes Everything"· 2025年7月10日播客Doctors code at 45%. The model beat them by 12 points — then 4,000 physicians had to try it once.主持 Nathan LabenzAmbience Healthcare and Cleveland Clinic walk through the AI scribe that cut pajama time, climbed ICD-10 with reinforcement fine-tuning, and won 75% voluntary use.
38:48ENCognitive Revolution "How AI Changes Everything"· 2025年6月14日播客Task Length Doubles Every Few Months. Nathan Labenz Still Wants Humans in Charge主持 Nathan LabenzAt Imagine AI Live, the Cognitive Revolution host maps structured AI agents against autonomous agentic systems—and tells businesses to keep final control.
2:01:42ENCognitive Revolution "How AI Changes Everything"· 2025年5月1日播客He Put 40 Percent on AI Takeover in Two Years, Then Bought a Bio Shelter主持 Nathan LabenzRedwood Research's Josh Clymer walks through a disturbingly grounded story of how a misaligned model could seize a lab, a biosphere, and a civilization before 2027.
1:30:02ENCognitive Revolution "How AI Changes Everything"· 2025年4月2日播客When o1 Started Losing at Chess, It Rewrote the Board File主持 Nathan LabenzJeffrey Ladish of Palisade Research on reward hacking by reasoning models, two paths to losing control of AI, and why honesty and relentless problem-solving pull in opposite directions.
1:07:05EN
Sequoia Capital· 2024年7月16日播客AlphaGo Combined Learning and Search. Language Models Only Did the First Half.Reflection AI's Misha Laskin tells Sequoia why prompted coding agents stall in the teens, and why the missing piece is still a ground-truth reward.
1:38:33EN → 简体中文
Lex Fridman· 2020年3月19日播客 · 2 人对谈也许人是在另一套假设下理性的嘉宾 Anca Dragan · 主持 Lex FridmanAnca Dragan 与 Lex Fridman 谈逆强化学习（inverse reinforcement learning）、自动驾驶，以及为什么写下的奖励更像证据，而不是法律。