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1:06:31EN
No Priors: AI, Machine Learning, Tech, & Startups· 2026年3月20日ポッドキャスト · 2 人Andrej Karpathy Hasn't Typed a Line of Code Since Decemberホスト Sarah Guo, Elad GilOn No Priors, the OpenAI cofounder and former Tesla AI director describes the loopy era of coding agents, AutoResearch, and why the bottleneck is now the human.コーディングエージェントAutoresearchAgent orchestration+3
36:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年10月9日ポッドキャスト · 2 人STaR's Author Wants Models With EQ, Not Just More IQホスト Sarah Guo, Elad GilEric Zelikman, the Stanford and xAI researcher behind STaR and Quiet-STaR, launches humans& on the bet that three years of IQ scaling left models emotionally unintelligent.モデルと学習Post-trainingReinforcement Learning+3
27:32EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年3月28日ポッドキャスト · 2 人From a side project to 700 integrations: Harrison Chase on what still blocks agentsホスト Sarah Guo, Elad GilThe LangChain CEO walks through a two-year churn of patterns, and why memory, planning, and few-shot data still matter more than swapping models.エージェントエージェント工学Agent memory+3
37:18EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年5月4日ポッドキャスト · 2 人Use Retrieval for a Wi-Fi Password. Use Experts for a Company's Private Language.ホスト Sarah Guo, Elad GilGoogle Brain's Kelvin Guu on REALM, FLAN, model surgery, and why personalizing large models is still an unsolved interface problem.RAG とナレッジ検索拡張生成(RAG)ファインチューニング+3
1:30:52EN
Lex Fridmanポッドキャスト · 2 人Building Minds from Unified Theories: Nate Derbinsky on Cognitive Architectures, Soar, and the Case for Forgettingゲスト MIT AGI · ホスト Lex FridmanA guest lecture in MIT's AGI course on how cognitive architectures try to assemble human-level intelligence, and how studying human memory made the Soar architecture faster and better.エージェントAGI(汎用人工知能)AI research+3
10:16EN
Lex Fridmanポッドキャスト · 2 人Reasoning Without Logic: Yann LeCun on Teaching Neural Networks to Thinkゲスト Yann Lecun · ホスト Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.モデルと学習機械学習の基礎Deep Learning+3