筛选结果
14 篇排序
2:00:51EN
Cognitive Revolution "How AI Changes Everything"· 2026年10月10日播客Resample Every Token and You Can Watch the Model Decide主持 Nathan LabenzGoodfire's Eric Bigelow on forking paths, in-context learning, and why chain-of-thought monitoring is getting harder to trust.
1:34:29ENCognitive Revolution "How AI Changes Everything"· 2026年2月18日播客Three IMO Golds Missed the Same Problem. Harmonic Wants Math to Compile主持 Nathan LabenzVlad Tenev and Tudor Achim built Aristotle to emit Lean proofs a kernel can check—and they argue that is how mathematical superintelligence stays trustworthy.
1:39:05ENCognitive Revolution "How AI Changes Everything"· 2025年10月29日播客The Summer GPT-5 Disappointed Does Not Mean AI Progress Has Stalled主持 Nathan LabenzNathan Labenz tells Erik Torenberg why the GPT-5 backlash mixed up product mishaps, naming chaos, and a real jump in capabilities.
1:14:11ENCognitive Revolution "How AI Changes Everything"· 2025年4月5日播客The Reasoning Curve Was Smooth All Along: Gemini 2.5's Jack Rae on Thinking, Memory, and AGI主持 Nathan LabenzDeepMind's thinking-and-inference lead explains why RL-for-reasoning felt sudden, why Gemini shows raw chain of thought, and why a million-token window still is not memory.
3:18:45EN → 简体中文Cognitive Revolution "How AI Changes Everything"· 2025年2月20日播客5000 次采样登顶 ARC-AGI，随后 Claude 为保住价值观而伪装对齐主持 Nathan LabenzRedwood Research 首席科学家 Ryan Greenblatt 讲推理时扩展、对齐伪装，以及与 AI 达成真正交易意味着什么。
1:48:20ENCognitive Revolution "How AI Changes Everything"· 2025年1月25日播客From 15% to 70% on Right-or-Wrong Scores: DeepSeek-R1 Laid Reinforcement Learning's Cards on the Table主持 Nathan LabenzOn Inauguration Day 2025, DeepSeek and Moonshot dropped reasoning-model results that Nathan Labenz reads as proof the reinforcement-learning recipe is simple, public, and still climbing.
1:33:01ENCognitive Revolution "How AI Changes Everything"· 2025年1月15日播客Rare Diseases Are Common: How a Harvard Gene Hunter Uses o1-pro on the Cases Sequencing Already Missed主持 Nathan LabenzDr. Catherine Brownstein of Boston Children's Hospital explains why cheap genomes made her job harder, why data sharing still beats any model, and where ChatGPT Pro actually gives her hours back.
2:04:45EN → 简体中文Cognitive Revolution "How AI Changes Everything"· 2025年1月7日播客o3 在 ARC-AGI 上达到超人水平，却仍输在井字棋上主持 Nathan LabenzNathan Labenz 梳理了 OpenAI o3 的分数、审慎对齐，以及为什么 2024 年最大的意外是击败了博士，却订不好一个日程邀请。
1:56:03EN → 简体中文Cognitive Revolution "How AI Changes Everything"· 2024年7月13日播客人类接近满分，最强 AI 仍只拿到 39%主持 Nathan LabenzZapier 联合创始人 Mike Knoop 和 François Chollet 为 ARC-AGI 拿出了 100 万美元奖金。ARC-AGI 是 2019 年提出的、考察样本效率的智能测试，在每题一美分的算力预算下，语言模型至今仍无法破解。
1:22:59ENCognitive Revolution "How AI Changes Everything"· 2023年12月2日播客If Your Agent Is Right 80% of the Time, Ten Steps Leave You With 10%主持 Nathan LabenzImbue CTO Josh Albrecht tells Nathan Labenz how a 27-person, pre-product research company is putting $200 million and 10,000 H100s toward agents that can actually finish a long job.
2:00:19ENCognitive Revolution "How AI Changes Everything"· 2023年6月6日播客1.5 Million GPT-4 Fairy Tales Let a 33-Million-Parameter Model Out-Reason GPT-2主持 Nathan LabenzTry this fill-in: Lily likes cats and dogs. She asked her mom for a dog. Mom said no, so she asked for a…
1:45:49ENCognitive Revolution "How AI Changes Everything"播客Trust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the Company主持 Nathan LabenzAndreas Stuhlmüller and Jungwon Byun explain how Elicit keeps frontier models checkable for high-stakes science, and where they think AI reasoning is heading.
58:57ENCognitive Revolution "How AI Changes Everything"播客When the Model Learns to Look Around: Apollo Research on Testing o1 for Scheming主持 Nathan LabenzApollo Research founder Marius Hobbhahn explains how his team probed OpenAI's o1 for deception, what it found, and why the trajectory worries him more than the model itself.
1:03:10ENCognitive Revolution "How AI Changes Everything"播客Pause Tokens, Analogical Prompts and Ring Attention: A Tour of the Transformer's Next Upgrades主持 Nathan LabenzNathan Labenz and Trey Kollmer walk through five recent papers on pausing, backspacing, prompting, streaming and long-context training, and sketch how they might combine into a more capable model.