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1:35:29EN
Cognitive Revolution "How AI Changes Everything"· 1 sept 2026PódcastUber Burned a Year of Tokens in 13 Weeks. Retrieval Is How Agents WinPresenta: Nathan LabenzMongoDB Field CTO of AI Pete Johnson argues that agent performance now hinges on retrieval quality, not on stuffing million-token context windows.RAG y conocimientoGeneración aumentada por recuperaciónAgent memory+3
1:26:16EN
Cognitive Revolution "How AI Changes Everything"· 9 may 2026PódcastMilliseconds to Match: How Criteo Bids on a Billion Anonymous ProfilesPresenta: Nathan LabenzCriteo’s CTO and AI lab head walk through the millisecond auction, the DeepKNN-to-foundation-model stack, and why a live product catalog still beats a frozen LLM.Empresa y estrategiaRecommendation systemsEmbeddings+3
1:22:07EN
Cognitive Revolution "How AI Changes Everything"· 25 sept 2025PódcastEvery payment is a frame in a movie. Stripe’s foundation model took card-testing detection from 59% to 97%Presenta: Nathan LabenzEmily Sands, Stripe’s head of data and AI, explains how a domain-specific payments model, shared embeddings, and mixed rules turn a $1.4 trillion network into compounding advantage.Modelos y entrenamientoIA en finanzasEmbeddings+3
1:13:23EN
Cognitive Revolution "How AI Changes Everything"· 12 oct 2024PódcastAn AI Found a GABA-Cancer Link. The Next Step Is Talking to the Cells.Presenta: Nathan LabenzMichael Levin and Leo Pio Lopez on a multilayer embedding that predicted melanoma, a cancer with no mutations, and why biology's real interface is cognitive.AI for scienceComputational BiologyAI research+3
1:25:10EN
Cognitive Revolution "How AI Changes Everything"· 15 jun 2024PódcastA Tenth of Google's Index, 99.9% Recall: How 20 People Built Brave SearchPresenta: Nathan LabenzJosep M. Pujol walks Cognitive Revolution through Brave's privacy-first index, hybrid retrieval stack, and the Search API Nathan Labenz now wants his own product team to use.RAG y conocimientoSearch & retrievalHybrid search+3
36:20EN
No Priors: AI, Machine Learning, Tech, & Startups· 6 jun 2024Pódcast · 2 vocesA Hundred Million Tokens in Context Would Cost $10 Million. Tengyu Ma Runs the RAG MathPresenta: Sarah Guo, Elad GilThe Stanford professor and Voyage AI founder argues that retrieval, not a giant context window, is how enterprises should manage private knowledge.RAG y conocimientoGeneración aumentada por recuperaciónEmbeddings+3
1:06:54EN
Cognitive Revolution "How AI Changes Everything"· 5 mar 2024PódcastGoogle Gives You 33 Million Results. Exa Wants 54.Presenta: Nathan LabenzWill Bryk, CEO of Exa, tells Nathan Labenz why search and research are different jobs, and why an AI librarian for the web has to be neural from the first filter.RAG y conocimientoSearch & retrievalGeneración aumentada por recuperación+3
42:07EN
No Priors: AI, Machine Learning, Tech, & Startups· 15 feb 2024Pódcast · 2 vocesNotion’s Ivan Zhao: RAG Will Make Organizing OptionalPresenta: Sarah Guo, Elad GilThe Notion CEO tells No Priors why he bet the company on GPT-4, why embeddings matter as much as agents, and why software is entering a bundling cycle.RAG y conocimientoGeneración aumentada por recuperaciónEmbeddings+3
1:26:30EN
Cognitive Revolution "How AI Changes Everything"· 24 oct 2023PódcastMost of this data has never been in a database: Chroma's wartime CTO on RAGPresenta: Nathan LabenzAnton Troynikov returns to The Cognitive Revolution to argue that retrieval quality is mostly the embedding, that Chroma should own the RAG loop, and that GPT still fails a basic Game of Life test of reasoning.RAG y conocimientoGeneración aumentada por recuperaciónEmbeddings+3
1:26:20EN
Cognitive Revolution "How AI Changes Everything"· 2 mar 2023PódcastForty Dollars to Index Joe Rogan: Chroma, Shovels, and the Wright Flyer Era of MLPresenta: Nathan LabenzChroma co-founder Anton Troynikov on embeddings as the storage layer for language-model apps, Stable Attribution, and why machine learning is still flying on intuition.RAG y conocimientoGeneración aumentada por recuperaciónEmbeddings+3
1:29:03EN
Lex Fridman· 27 sept 2016PódcastFrom Word Vectors to Memory Networks: The Building Blocks of Deep NLPPresenta: Lex FridmanRichard Socher explains how word embeddings, recurrent models, GRUs, pointer mixtures, and dynamic memory networks turn language tasks into trainable end-to-end systems.Modelos y entrenamientoDeep LearningFundamentos de ML+2