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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
1:56:16EN
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
35:36EN
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
1:44:19EN
Cognitive Revolution "How AI Changes Everything"· 2023年3月14日ポッドキャストTen People Versus Google’s $282 Billion Search Machineホスト Nathan LabenzPerplexity founder Aravind Srinivas tells Nathan Labenz why citations, speed, and value per minute — not chat bubbles — are how a tiny team competes with Google.プロダクトと UXAI startups & small teamsProduct Strategy+3
1:26:20EN
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
2:24:31EN
Lex Fridman· 2019年10月11日ポッドキャスト · 2 人Watson Won Jeopardy Without Understanding. Ferrucci Wants AI That Can Explain Itselfゲスト David Ferrucci · ホスト Lex FridmanLex Fridman talks with David Ferrucci about IBM Watson, why prediction is not understanding, and why high-stakes decisions need machines that can reason with us.RAG とナレッジ検索拡張生成(RAG)AI research+3
1:22:36EN
Lenny's Podcastポッドキャスト · 2 人Stop Chasing AI News: Chip Huyen on What Actually Makes AI Products Betterゲスト Chip Huyen · ホスト Lenny RachitskyAuthor of AI Engineering walks through pre-training, post-training, RL, evals and RAG, then explains what separates companies that get value from AI from those that stall.プロダクトと UXファインチューニング検索拡張生成(RAG)+3
1:11:56EN
Lenny's Podcastポッドキャスト · 2 人How to Get ChatGPT to Recommend Your Product: A Practical Guide to Answer Engine Optimizationゲスト Ethan Smith · ホスト Lenny RachitskyGraphite CEO Ethan Smith explains how AEO differs from SEO, which levers move the needle, and how to measure and test what works.企業と戦略AI in sales & marketingGo-to-Market+3
1:36:57EN
Unsupervised Learning: With Jacob EffronポッドキャストDo the Dumb Thing First: A CTO's Playbook for Building an AI Coding Assistantホスト Jacob EffronSourcegraph CTO Beyang Liu on which AI coding features actually earn daily use, how new models get shipped and evaluated, and why retrieval is still a search problem.コーディングエージェント評価(Evals)検索拡張生成(RAG)+3
23:54EN
No Priors: AI, Machine Learning, Tech, & Startupsポッドキャスト · 2 人Beyond GPT-7: The Customization Toolkit, Meta's Open Source Bet and the Return of Consumer AIホスト Sarah Guo, Elad GilIn a host-only episode, the No Priors hosts map the six levers for 10x to 100x better AI, explain fine-tuning, RAG and AI feedback, and ask why consumer social is suddenly interesting again.モデルと学習ファインチューニング検索拡張生成(RAG)+3
57:25EN
Unsupervised Learning: With Jacob EffronポッドキャストFrom RAG to Reasoning: Why the Future of AI Is a System, Not a Modelホスト Jacob EffronDouwe Kiela explains what OpenAI's o1 reveals about test-time compute, why enterprise AI must be specialized, and where post-training and evaluations are heading.モデルと学習Reasoning & test-time computePost-training+3
51:55EN
Unsupervised Learning: With Jacob EffronポッドキャストThe Search Engine Under AI Apps: Why Bigger Context Windows Aren't Enoughホスト Jacob EffronTurbopuffer CEO Simon on object-storage databases, vector-search trade-offs, and the AI features that will become SaaS table stakes.インフラとデプロイVector search検索拡張生成(RAG)+3
46:48EN
Unsupervised Learning: With Jacob EffronポッドキャストSnowflake's AI Stack: Small Models, the Text-to-SQL Iceberg, and Why Governance Comes Firstホスト Jacob EffronSnowflake head of AI Baris Gekken explains how Arctic and Cortex were built, where enterprise AI actually works today, and why precision, governance and evaluation matter more than raw model capability.企業と戦略インフラとデプロイエンタープライズ AI+3
1:06:22EN
Unsupervised Learning: With Jacob EffronポッドキャストPerplexity's Bet: Search Becomes Answers, and Answers Become a Businessホスト Jacob EffronThe Perplexity CEO explains how a consumer AI search product is built, sequenced, and defended against Google.RAG とナレッジ検索拡張生成(RAG)Search & retrieval+3
32:41EN
Lex Fridmanポッドキャスト · 2 人How IBM Watson Won Jeopardy: A Moonshot Built on Engineering, Not a Breakthroughゲスト David Ferrucci · ホスト Lex FridmanDavid Ferrucci tells Lex Fridman how he talked IBM into the Jeopardy challenge and how his team built Watson's search, scoring and confidence machinery in about four years.モデルと学習AI HistorySearch & retrieval+1
1:01:13EN
Unsupervised Learning: With Jacob EffronポッドキャストPinecone's Vector Database Bet: Scaling Through the ChatGPT Tsunamiホスト Jacob EffronEdo Liberty explains how Pinecone scaled through the generative AI boom, why vector search is more than a database add-on, and where he sees the next AI infrastructure shifts.インフラとデプロイVector search検索拡張生成(RAG)+3