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1:18:05EN → 简体中文
Unsupervised Learning: With Jacob Effron· 2025年12月18日播客AI 研究的真正瓶颈是算力,缺的不是点子主持 Jacob EffronNeurIPS 之后,Ari Morcos 和 Rob Toews 与 Jacob Effron 坐下来争论:模型是否遇到平台期、SSI 的保密、OpenAI 的烧钱,以及几项尖锐的 2026 年押注。企业与战略AI StrategyScaling Laws+3
48:00EN
Unsupervised Learning: With Jacob Effron· 2025年12月15日播客A Two-Second Vote Is Training Models to Be Clickbait主持 Jacob EffronSurge AI CEO Edwin Chen tells Redpoint why LMArena rewards length and emojis, how one lab's models got worse for a year, and why frontier labs are no longer on the same path.评估与可观测性RL environments & simulatorsData quality & curation+3
1:16:53EN → 简体中文
Unsupervised Learning: With Jacob Effron· 2025年10月24日播客Karpathy 说今天的模型是“slop”,他的十年时间表听起来仍然很乐观主持 Jacob EffronDatology AI 的 Ari Morcos 与 Radical Ventures 的 Rob Toews 梳理了 Karpathy 的一盆冷水、OpenAI 的交易,以及究竟是谁在为数据中心热潮买单。企业与战略AI StrategyCompany Building+3
57:52EN
Unsupervised Learning: With Jacob Effron· 2025年8月13日播客They Aimed for 1%. The Lab Hit 20%. Chai Discovery Moved Antibody Design Onto the Computer.主持 Jacob EffronJoshua Meier on three waves of AI drug discovery, why Chai-2 behaves like Photoshop for molecules, and why a 1% hit rate was supposed to be enough to change the industry.模型与训练AI for scienceComputational Biology+3
44:21EN
Unsupervised Learning: With Jacob Effron· 2025年6月4日播客Mercor's CEO: Knowledge Work's Next Job Is Writing Evals主持 Jacob EffronBrendan Foody tells Redpoint how Mercor turned AI hiring and expert data into a $100 million round — and why he thinks a huge slice of white-collar work will become creating tests for models.评估与可观测性评估Data quality & curation+3
56:10EN
Unsupervised Learning: With Jacob Effron· 2024年10月29日播客DeepL Beat Google at Translation by Refusing to Be a General Model主持 Jacob EffronOnly 18 percent of the world speaks English—and a Cologne startup turned that fact into a $2 billion language-AI company.企业与战略Data quality & curation评估+3
1:10:19EN
Unsupervised Learning: With Jacob Effron播客The Robot That Folded the Laundry: Physical Intelligence’s Three Frontiers主持 Jacob EffronKarol Hausman and Danny Driess explain how robot foundation models moved from coded primitives to web-scale pretraining, and why performance is now the hard gate.模型与训练RoboticsAI research+3