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絞り込み結果

26 件

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トピックRAG とナレッジ

Cognitive Revolution "How AI Changes Everything"· 2026年9月1日ポッドキャストUber Burned a Year of Tokens in 13 Weeks. Retrieval Is How Agents Winホスト 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 とナレッジ検索拡張生成（RAG）Agent memory+3

Sequoia Capital· 2026年6月24日ポッドキャストAn 80GB Wikipedia Cache Versus a 100GB Model That Holds the InternetEngram’s founders argue that stuffing a company into the context window is the expensive kind of memory—and that baking it into the weights can cut token use by two orders of magnitude.RAG とナレッジContinual learningファインチューニング+3

No Priors: AI, Machine Learning, Tech, & Startups· 2025年9月5日ポッドキャスト · 2 人How OpenEvidence Became the Clinical OS for 40% of U.S. Doctorsホスト Sarah Guo, Elad GilFounder Daniel Nadler tells No Priors how a free search tool for physicians, not an answer engine for patients, took high-stakes clinical knowledge in 18 months.RAG とナレッジ検索拡張生成（RAG）Search & retrieval+3

No Priors: AI, Machine Learning, Tech, & Startups· 2025年5月15日ポッドキャスト · 2 人One Company Now Holds as Many Documents as the Entire Early Webホスト Sarah Guo, Elad GilGlean CEO Arvind Jain tells No Priors why enterprise search was a graveyard, how transformers finally made it work, and why a product that searches too well can be too dangerous to sell.RAG とナレッジエンタープライズ AISearch & retrieval+3

Cognitive Revolution "How AI Changes Everything"· 2025年2月15日ポッドキャストShe Never Sees a Row of Your Data. The Answers Still Clear 95%.ホスト Nathan LabenzIllumex CEO Inna Tokarev Sela tells Nathan Labenz how a Platonic industry ontology, trained only on metadata, collapses enterprise software into one conversation.RAG とナレッジコンテキストエンジニアリングKnowledge graphs+3

Sequoia Capital· 2025年2月13日ポッドキャストAn AI Application Is Still an ApplicationMongoDB's Sahir Azam tells Sequoia why vector search is a new primitive for unstructured data, not a replacement for transactions, and why retrieval quality is the last mile that gets AI into conservative enterprises.RAG とナレッジVector search検索拡張生成（RAG）+3

Cognitive Revolution "How AI Changes Everything"· 2024年7月19日ポッドキャストHippoRAG Matches Iterative Retrieval at One-Tenth the Online Costホスト Nathan LabenzA hippocampus-inspired knowledge graph lets multi-hop questions skip the brute-force document crawl.RAG とナレッジ検索拡張生成（RAG）Knowledge graphs+3

Lex Fridman· 2024年6月19日ポッドキャスト · 2 人The journey begins after the answer: Perplexity CEO Aravind Srinivas on search and curiosityゲスト Aravind Srinivas · ホスト Lex FridmanIn 2022, Perplexity's first hire asked about health insurance — and four PhDs could not get a clear answer from Google.RAG とナレッジProduct StrategyAI interface design+3

Cognitive Revolution "How AI Changes Everything"· 2024年6月15日ポッドキャストA Tenth of Google's Index, 99.9% Recall: How 20 People Built Brave Searchホスト 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 とナレッジSearch & retrievalHybrid search+3

No Priors: AI, Machine Learning, Tech, & Startups· 2024年6月6日ポッドキャスト · 2 人A Hundred Million Tokens in Context Would Cost $10 Million. Tengyu Ma Runs the RAG Mathホスト 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 とナレッジ検索拡張生成（RAG）Embeddings+3

Cognitive Revolution "How AI Changes Everything"· 2024年4月8日ポッドキャストThree Million Emails, Ten LLM Calls, Six Models: Shortwave's RAG Playbookホスト Nathan LabenzFirebase founder Andrew Lee walks Nathan Labenz through the retrieval stack that turned a Gmail inbox into a working AI assistant.RAG とナレッジ検索拡張生成（RAG）Vector search+3

Cognitive Revolution "How AI Changes Everything"· 2024年4月3日ポッドキャストThe Right Size of an AI Task Is Whatever You Can Checkホスト Nathan LabenzElicit founders Andreas Stuhlmüller and Jungwon Byun walk through how a twelve-person research assistant cut summary hallucinations from 1.5 to 0.5, ships about every week and a half, and treats task decomposition as the way to spend more compute without a black box.RAG とナレッジ検索拡張生成（RAG）Search & retrieval+3

Cognitive Revolution "How AI Changes Everything"· 2024年3月5日ポッドキャストGoogle Gives You 33 Million Results. Exa Wants 54.ホスト 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 とナレッジSearch & retrieval検索拡張生成（RAG）+3

No Priors: AI, Machine Learning, Tech, & Startups· 2024年2月22日ポッドキャスト · 2 人Bolt Memory onto GPT-4 and Cut Hallucinations by up to 50%ホスト Sarah Guo, Elad GilPinecone CEO Edo Liberty tells Sarah Guo and Elad Gil how RAG, serverless vectors, and keeping knowledge outside the model make LLMs more reliable.RAG とナレッジ検索拡張生成（RAG）Vector search+3

No Priors: AI, Machine Learning, Tech, & Startups· 2024年2月15日ポッドキャスト · 2 人Notion’s Ivan Zhao: RAG Will Make Organizing Optionalホスト 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 とナレッジ検索拡張生成（RAG）Embeddings+3

Cognitive Revolution "How AI Changes Everything"· 2024年1月18日ポッドキャストWhen GPT-4 Writes SQL, Four Tables Is the Breaking Pointホスト Nathan LabenzJuan Sequeda on why a knowledge graph is the accurate, explainable brain that foundation models do not have.RAG とナレッジKnowledge graphs検索拡張生成（RAG）+3

Cognitive Revolution "How AI Changes Everything"· 2023年10月31日ポッドキャストThe Historian Who Taught GPT-3.5 to Beat GPT-4ホスト Nathan LabenzMark Humphries on a kilometer of unread paper, fur-trade jargon that embeddings cannot see, and why cheap processing will force history to be rewritten.RAG とナレッジ検索拡張生成（RAG）ファインチューニング+3

Cognitive Revolution "How AI Changes Everything"· 2023年10月24日ポッドキャストMost of this data has never been in a database: Chroma's wartime CTO on RAGホスト 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 とナレッジ検索拡張生成（RAG）Embeddings+3

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

Cognitive Revolution "How AI Changes Everything"· 2023年4月6日ポッドキャストThey Used Humans as Stand-In AIs. Now 250,000 People Run Elicit.ホスト Nathan LabenzOught's founders tell Nathan Labenz why open-ended reasoning has to be decomposed, supervised, and grounded—not scaled until it merely looks persuasive.RAG とナレッジエージェント工学評価（Evals）+3

No Priors: AI, Machine Learning, Tech, & Startups· 2023年3月16日ポッドキャスト · 2 人Featured snippets stalled at 7%. A Google veteran used language models to hit 70%ホスト Sarah Guo, Elad GilNeeva founder Sridhar Ramaswamy tells No Priors why answers always beat links, why privacy never sold the switch, and why most queries do not need the biggest model.RAG とナレッジ検索拡張生成（RAG）Search & retrieval+3

Cognitive Revolution "How AI Changes Everything"· 2023年3月2日ポッドキャストForty Dollars to Index Joe Rogan: Chroma, Shovels, and the Wright Flyer Era of MLホスト 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 とナレッジ検索拡張生成（RAG）Embeddings+3

Lex Fridman· 2021年9月15日ポッドキャスト · 2 人Douglas Lenat's 37-Year Bet That Common Sense Can Be Written Downゲスト Douglas Lenat · ホスト Lex FridmanSince 1984, Cyc has been encoding the unstated rules of the world so machines can reason instead of merely pattern-match.RAG とナレッジAI HistoryAGI（汎用人工知能）+1

Lex Fridman· 2019年10月26日ポッドキャスト · 2 人Teaching Machines the Frameworks Humans Take for Grantedゲスト David Ferrucci · ホスト Lex FridmanDavid Ferrucci explains why AI needs shared interpretive frameworks, not just data and pattern matching, to reason and communicate like humans.RAG とナレッジAI researchAGI（汎用人工知能）+3

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