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1:35:29EN
Cognitive Revolution "How AI Changes Everything"· 1 sept. 2026PodcastUber Burned a Year of Tokens in 13 Weeks. Retrieval Is How Agents WinAnime : 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 et connaissancesGénération augmentée par récupérationAgent memory+3
1:26:16EN
Cognitive Revolution "How AI Changes Everything"· 9 mai 2026PodcastMilliseconds to Match: How Criteo Bids on a Billion Anonymous ProfilesAnime : 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.Entreprise et stratégieRecommendation systemsEmbeddings+3
1:22:07EN
Cognitive Revolution "How AI Changes Everything"· 25 sept. 2025PodcastEvery payment is a frame in a movie. Stripe’s foundation model took card-testing detection from 59% to 97%Anime : 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.Modèles et entraînementIA en financeEmbeddings+3
1:13:23EN
Cognitive Revolution "How AI Changes Everything"· 12 oct. 2024PodcastAn AI Found a GABA-Cancer Link. The Next Step Is Talking to the Cells.Anime : 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 juin 2024PodcastA Tenth of Google's Index, 99.9% Recall: How 20 People Built Brave SearchAnime : 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 et connaissancesSearch & retrievalHybrid search+3
36:20EN
No Priors: AI, Machine Learning, Tech, & Startups· 6 juin 2024Podcast · 2 voixA Hundred Million Tokens in Context Would Cost $10 Million. Tengyu Ma Runs the RAG MathAnime : 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 et connaissancesGénération augmentée par récupérationEmbeddings+3
1:06:54EN
Cognitive Revolution "How AI Changes Everything"· 5 mars 2024PodcastGoogle Gives You 33 Million Results. Exa Wants 54.Anime : 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 et connaissancesSearch & retrievalGénération augmentée par récupération+3
42:07EN
No Priors: AI, Machine Learning, Tech, & Startups· 15 févr. 2024Podcast · 2 voixNotion’s Ivan Zhao: RAG Will Make Organizing OptionalAnime : 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 et connaissancesGénération augmentée par récupérationEmbeddings+3
1:26:30EN
Cognitive Revolution "How AI Changes Everything"· 24 oct. 2023PodcastMost of this data has never been in a database: Chroma's wartime CTO on RAGAnime : 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 et connaissancesGénération augmentée par récupérationEmbeddings+3
1:26:20EN
Cognitive Revolution "How AI Changes Everything"· 2 mars 2023PodcastForty Dollars to Index Joe Rogan: Chroma, Shovels, and the Wright Flyer Era of MLAnime : 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 et connaissancesGénération augmentée par récupérationEmbeddings+3
1:29:03EN
Lex Fridman· 27 sept. 2016PodcastFrom Word Vectors to Memory Networks: The Building Blocks of Deep NLPAnime : 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.Modèles et entraînementDeep LearningFondamentaux du ML+2