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198 件
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32:48EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年11月16日ポッドキャスト · 2 人Fourteen People, $200 Million of Compute, and the Reliability Bottleneck for Agentsホスト Sarah Guo, Elad GilImbue co-founders Kanjun Qiu and Josh Albrecht on why agents fail, why language models cannot learn general algorithms, and why they are spending a giant round on GPUs rather than headcount.エージェントコーディングエージェントエージェント工学+3
1:23:10EN
Cognitive Revolution "How AI Changes Everything"· 2023年9月19日ポッドキャストLuxury Purses Under $500 Beat a Bank's Fraud Modelホスト Nathan LabenzArthur.ai CEO Adam Wenchel on boundary attacks, LLM firewalls, and why a model that eats raw text cannot be secured from the inside.セキュリティと安全性AI securityPrompt injection+3
55:08EN
Cognitive Revolution "How AI Changes Everything"· 2023年8月29日ポッドキャストOne Model Inside 38 Products: Paige Bailey on How Google Trains and Ships PaLM 2ホスト Nathan LabenzAt Google I/O in May 2023, PaLM 2 arrived already running in more than two dozen products — and its lead product manager walks through what that actually took.モデルと学習Multimodal AI評価(Evals)+3
39:45EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年7月27日ポッドキャスト · 2 人Palantir's 13th Employee: The Model Is a Stochastic Genie. Ontology Is the Stake That Tethers Itホスト Sarah Guo, Elad GilCTO Shyam Sankar on 550 microservices, air-gapped submarines, and why Palantir's ontology is what large language models were waiting for.企業と戦略エンタープライズ AIAI Strategy+3
スライド 3 枚12:20EN
Matthew Berman· 2023年7月10日Testing UltraLM 13B: A Small Model’s Strengths and Reasoning ErrorsMatthew Berman · Forward FutureA 2023 local test finds useful writing and simple coding alongside failures in games, arithmetic and logic.Open-weight models評価(Evals)モデルと学習+1
54:00EN
20VC with Harry Stebbings· 2023年6月30日ポッドキャスト · 2 人Douwe Kiela on Data, Retrieval and Enterprise AIゲスト Douwe Kiela · ホスト Harry StebbingsThe Contextual AI CEO discusses language model trade-offs, evaluation, open models and the hurdles to enterprise use.エンタープライズ AI検索拡張生成(RAG)Data quality & curation+2
スライド 6 枚18:08EN
Matthew Berman· 2023年6月29日Six Language Models Compared on an Informal 2023 Prompt SetMatthew Berman · Forward FutureMatthew Berman tests coding, writing and reasoning with H2O GPT, finding a tie under a small and subjective rubric.BenchmarksOpen-weight modelsモデルと学習+1
スライド 4 枚10:23EN
Matthew Berman· 2023年6月15日Testing Hosted Falcon 40B and Document Chat in h2oGPTMatthew Berman · Forward FutureMatthew Berman tries H2O.ai’s 2023 hosted Falcon 40B interface on coding, writing and reasoning prompts, then demonstrates document chat.Open-weight models評価(Evals)AI research+1
スライド 6 枚11:49EN
Matthew Berman· 2023年6月1日Testing Wizard-Vicuna 30B: Responses, Reasoning, and CodeMatthew Berman · Forward FutureMatthew Berman examines a model marketed as uncensored, demonstrates a 2023 cloud setup, and runs an informal collection of tests.モデルと学習評価(Evals)AI Safety+1
1:33:51EN
Cognitive Revolution "How AI Changes Everything"· 2023年5月23日ポッドキャストGPT-4 still writes SQL that will not run. She put a guardrail on the modelホスト Nathan LabenzShreya Rajpal built Guardrails AI so developers can encode what “correct” means, validate the pieces, and send failures back to the model instead of hoping the prompt holds.セキュリティと安全性評価(Evals)人による検証(HITL)+3
1:25:35EN
Cognitive Revolution "How AI Changes Everything"· 2023年5月21日ポッドキャストTwo and a Half Years From 33.3% to 86.5%: Google Sent Foundation Models to Medical Schoolホスト Nathan LabenzVivek Natarajan walks Nathan Labenz through Med-PaLM and Med-PaLM 2: the data-light techniques, the nine-axis clinician evals, and why a high USMLE-style score is the wrong trophy.モデルと学習AI in healthcareファインチューニング+3
42:18EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年5月18日ポッドキャスト · 2 人Ten Percent of Search Is Health — and the Doctor Is Googling Tooホスト Sarah Guo, Elad GilGoogle researcher Karan Singhal on Med-PaLM 2, the real bar for medical language models, and why medicine is a harder alignment lab than chatbots.モデルと学習AI in healthcare安全性とガバナンス+3
1:28:43EN
Cognitive Revolution "How AI Changes Everything"· 2023年5月16日ポッドキャストClinical Guidelines Are 11% Evidence. Curai’s Founder Still Wants GPT-4 in the Exam Roomホスト Nathan LabenzNeal Khosla of Curai Health argues that large language models already match median U.S. physicians on today’s tests — and that the real work is scaffolding, supervision, and a doctor’s signature.企業と戦略AI in healthcareProduct Strategy+3
58:24EN
Cognitive Revolution "How AI Changes Everything"· 2023年5月11日ポッドキャストAmong 100 Random Doctors, Zak Kohane Would Be Surprised If One Beat GPT-4ホスト Nathan LabenzHarvard's biomedical informatics chair on a rare newborn case, why drug-style trials will not certify a general medical model, and why the AI should still work for the patient.AI in healthcare評価(Evals)Human-AI Collaboration+3
1:21:06EN
Cognitive Revolution "How AI Changes Everything"· 2023年4月27日ポッドキャストEveryone Talks About RLHF. Almost Nobody Is Doing It.ホスト Nathan LabenzHumanloop CEO Raza Habib on why an LLM API is maybe five percent of a working product, and what actually fills the rest.プロダクトと UX評価(Evals)オブザーバビリティと信頼性+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
1:23:53EN
Cognitive Revolution "How AI Changes Everything"· 2023年3月17日ポッドキャストGPT-4, 24 Hours Later: No Safety Locks, a Captcha Hire, and a U.S.–China Deadlockホスト Nathan LabenzA day after OpenAI shipped GPT-4, ChinaTalk sat a red teamer, a pessimist, and a policy researcher down to argue over capability, safety, and a race nobody can brake.セキュリティと安全性Red Teaming安全性とガバナンス+3
1:13:06EN
Lex Fridman· 2019年8月19日ポッドキャスト · 2 人Lockheed Martin's CTO on Autonomy, the Moon, and the Culture of Not Getting It Wrongゲスト Keoki Jackson · ホスト Lex FridmanKeoki Jackson tells Lex Fridman why going back to the Moon is the easy part—and why proving a learning system will still do the right thing is not.セキュリティと安全性安全性とガバナンス人による検証(HITL)+3
1:45:49EN
Cognitive Revolution "How AI Changes Everything"ポッドキャストTrust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the Companyホスト Nathan LabenzAndreas Stuhlmüller and Jungwon Byun explain how Elicit keeps frontier models checkable for high-stakes science, and where they think AI reasoning is heading.エージェントWorld modelsReasoning & test-time compute+3
1:25:13EN
Lenny's PodcastポッドキャストCodex Is Not Just a Coding Tool: OpenAI's Plan for an Agent That Behaves Like a Teammateホスト Lenny RachitskyCodex product lead Alexander Embiricos explains the 20x growth, why every useful agent may need to write code, and why human review speed is the real bottleneck.コーディングエージェントCode review & verificationDeveloper productivity+3
1:33:50EN
Lenny's PodcastポッドキャストEvals Are the New PRDs: Inside Anthropic's Research Product Teamホスト Lenny RachitskyDianne Penn, Anthropic's head of product for research and Labs, explains how the company found its identity, how her team turns user feedback into evals, and why human judgment still matters.プロダクトと UXAI Product ManagementProduct Strategy+3
1:31:41EN
Lenny's Podcastポッドキャスト · 2 人The Worst Model You'll Ever Use: Kevin Weil on Building Products at OpenAI's Paceゲスト Kevin Weil · ホスト Lenny RachitskyOpenAI's chief product officer explains how evals, light planning, fine-tuned model ensembles and a small PM team shape the way the company ships.プロダクトと UXProduct LeadershipAI Product Management+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
1:07:08EN
Lenny's Podcastポッドキャスト · 2 人If the Model Is the Product, the Eval Is the PRD: Inside Mercor's Riseゲスト Brendan Foody · ホスト Lenny RachitskyMercor CEO Brendan Foody explains why AI labs will pay almost anything for expert-written evals, how his company grew from $1 million to a $400 million run rate in 16 months, and what that means for jobs and founders.評価とオブザーバビリティ評価(Evals)Data quality & curation+3
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