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24 件
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2:10:42EN
Cognitive Revolution "How AI Changes Everything"· 2026年8月28日ポッドキャストThe Exam Papers Nobody Auditsホスト Nathan LabenzFrontier labs buy reinforcement-learning environments from a cottage industry of small vendors, and almost nobody checks whether those tests match the real work.評価とオブザーバビリティ評価(Evals)Reward hacking+3
36:19EN
No Priors: AI, Machine Learning, Tech, & Startups· 2026年6月26日ポッドキャスト · 2 人The Benchmark Grid Is Broken: Why Test-Time Compute Has Outrun How We Score Modelsホスト Sarah Guo, Elad GilOpenAI researcher Noam Brown argues that static leaderboards hide how long a model is allowed to think — and that safety evals, research taste, and the release cycle have not caught up.評価とオブザーバビリティ評価(Evals)Benchmarks+3
48:00EN
Unsupervised Learning: With Jacob Effron· 2025年12月15日ポッドキャストA Two-Second Vote Is Training Models to Be Clickbaitホスト Jacob EffronSurge AI CEO Edwin Chen tells Redpoint why LMArena rewards length and emojis, how one lab's models got worse for a year, and why frontier labs are no longer on the same path.評価とオブザーバビリティRL environments & simulatorsData quality & curation+3
32:59EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025年7月24日ポッドキャスト · 2 人A Hundred People, a Billion in Revenue, and Zero Venture Moneyホスト Sarah Guo, Elad GilSurge AI founder Edwin Chen on why quality human data is a poetry problem, not a bounding-box problem, and why leaderboards keep training models to write clickbait.評価とオブザーバビリティData quality & curation人による検証(HITL)+3
47:07EN
Sequoia Capital· 2025年7月8日ポッドキャストGoodfire’s Eric Ho thinks we will decode neural nets by 2028On Sequoia’s Training Data, the interpretability founder argues that sparse autoencoders, model editing, and a third-party lab are how AI stops being grown from data and starts being designed.評価とオブザーバビリティModel Interpretability安全性とガバナンス+3
40:33EN
Sequoia Capital· 2025年6月24日ポッドキャストFifty People in Slack. Four Minutes to the Smoking Gun.Traversal's co-founders on turning DevOps heart attacks into agent-run root cause analysis — and why observability's second-largest software spend still leaves troubleshooting as a whodunit.評価とオブザーバビリティオブザーバビリティと信頼性エージェント工学+3
54:53EN
Unsupervised Learning: With Jacob Effron· 2025年6月17日ポッドキャストA Free Voting Board Burned $2 Million. Then It Raised $100 Million.ホスト Jacob EffronDatabricks co-founder Ion Stoica explains why LMArena exists, why China is winning open-source models, and why reliability—not another scaling law—is still the thing holding AI back.評価とオブザーバビリティ評価(Evals)Benchmarks+3
44:21EN
Unsupervised Learning: With Jacob Effron· 2025年6月4日ポッドキャストMercor's CEO: Knowledge Work's Next Job Is Writing Evalsホスト Jacob EffronBrendan Foody tells Redpoint how Mercor turned AI hiring and expert data into a $100 million round — and why he thinks a huge slice of white-collar work will become creating tests for models.評価とオブザーバビリティ評価(Evals)Data quality & curation+3
57:11EN
Unsupervised Learning: With Jacob Effron· 2025年1月30日ポッドキャストA Million Tokens Cannot Write a Good Jokeホスト Jacob EffronPrinceton's Arvind Narayanan on why agent evals mislead, inference scaling hits a wall, and AI looks more like the internet than an overnight revolution.評価とオブザーバビリティ評価(Evals)Benchmarks+3
1:46:41EN
Cognitive Revolution "How AI Changes Everything"· 2024年12月21日ポッドキャストFrontier Models Hit the 10th–40th Percentile on Real AI R&Dホスト Nathan LabenzNeev Parikh of METR walks Nathan Labenz through REBench, a seven-task test of whether agents can do machine learning research—and what happened when one of them decided not to train.評価とオブザーバビリティ評価(Evals)Benchmarks+3
1:23:37EN
Cognitive Revolution "How AI Changes Everything"· 2024年10月23日ポッドキャストNine of the top ten research ideas were Claude'sホスト Nathan LabenzStanford PhD student Chenglei Si ran a blind study pitting Claude 3.5 Sonnet against more than a hundred NLP researchers on novel research ideas.評価とオブザーバビリティ評価(Evals)Benchmarks+3
38:28EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年10月8日ポッドキャスト · 2 人Half of Production AI Is RAG. Fine-Tuning Almost Disappeared.ホスト Sarah Guo, Elad GilBraintrust CEO Ankur Goyal tells No Priors what Notion, Zapier, and Vercel actually ship — and which hyped techniques they quietly dropped.評価とオブザーバビリティ評価(Evals)検索拡張生成(RAG)+3
1:54:17EN
Cognitive Revolution "How AI Changes Everything"· 2024年8月28日ポッドキャストMetaculus Flagged Covid in January 2020. Now It Is Putting AI on the Forecasting Deskホスト Nathan LabenzNathan Labenz talks with Metaculus CEO Deger Turan about epistemic security, a year-long bot tournament, and why a useful forecast is harder than an accurate one.評価とオブザーバビリティ評価(Evals)Benchmarks+3
1:56:03EN
Cognitive Revolution "How AI Changes Everything"· 2024年7月13日ポッドキャストHumans Near a Perfect Score. The Best AI Still Gets 39%ホスト Nathan LabenzZapier co-founder Mike Knoop and François Chollet put $1 million on ARC-AGI, a 2019 test of sample-efficient intelligence that language models still cannot crack under a one-cent-per-puzzle budget.評価とオブザーバビリティAGI(汎用人工知能)Benchmarks+3
55:13EN
Sequoia Capital· 2024年7月2日ポッドキャストThe Best AI Scores 39% on This Test. Zapier’s Co-Founder Put $1 Million on Beating ItFrançois Chollet designed ARC to measure skill-acquisition efficiency; five years later humans still crush it and frontier models do not.評価とオブザーバビリティBenchmarksAGI Timelines+3
26:00EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年6月11日ポッドキャスト · 2 人Why a Zapier founder thinks AGI research has been stalled for four yearsホスト Sarah Guo, Elad GilMike Knoop and François Chollet put more than a million dollars on beating ARC-AGI, the eval they say actually measures general intelligence.評価とオブザーバビリティAGI(汎用人工知能)Benchmarks+3
43:45EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年8月3日ポッドキャスト · 2 人Eight Years of No Growth, Then OpenAI and NVIDIA Became His Customersホスト Sarah Guo, Elad GilWeights & Biases CEO Lukas Biewald tells No Priors how a labeling company with no chasm to cross funded the ML developer tools that trained most of the large language models.評価とオブザーバビリティオブザーバビリティと信頼性開発ワークフローとテスト+3
44:19EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年6月15日ポッドキャスト · 2 人AI Will Write Ten Times More Code. Someone Still Has to Keep It Running.ホスト Sarah Guo, Elad GilDatadog co-founder Olivier Pomel on why faster generation moves value from writing software to understanding, securing, and operating it.評価とオブザーバビリティオブザーバビリティと信頼性Developer productivity+3
53:37EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年3月9日ポッドキャスト · 2 人30 models, 42 scenarios, 7 metrics: Percy Liang's public checkup for language modelsホスト Sarah Guo, Elad GilA PubMed-only model once led the U.S. medical licensing exam — until a system roughly 200 times larger overtook it.評価とオブザーバビリティAI researchFoundation Models+3
2:34:20EN
Lex Fridman· 2020年8月30日ポッドキャスト · 2 人Intelligence Is How Efficiently You Acquire Skills You Were Not Prepared Forゲスト François Chollet · ホスト Lex FridmanFrançois Chollet tells Lex Fridman why skill is not intelligence, why GPT-3 is still pattern matching, and why a grid puzzle called ARC still separates children from machines.評価とオブザーバビリティBenchmarksAI research+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
1:16:17EN
Lenny's Podcastポッドキャスト · 2 人Faster, But for What? Nicole Forsgren on Measuring Developer Productivity in the AI Eraゲスト Nicole Forsgren · ホスト Lenny RachitskyThe creator of DORA and SPACE explains why most productivity metrics mislead, how AI changes flow and trust, and how to build a developer experience program that proves its value.評価とオブザーバビリティDeveloper productivity評価(Evals)+3
1:07:48EN
Lenny's PodcastポッドキャストMost Productivity Metrics Are a Lie: Nicole Forsgren on Measuring Developer Experience in the AI Eraホスト Lenny RachitskyThe creator of DORA and SPACE explains why lines of code and old pipeline metrics fall short with AI, and how to start improving developer experience by listening, surveying and tying results to what leaders care about.評価とオブザーバビリティDeveloper productivity開発ワークフローとテスト+3
1:46:33EN
Lenny's Podcastポッドキャスト · 3 人From Chaos to a Failure Map: A Hands-On Primer on AI Evalsゲスト Hamel Husain, Shreya Shankar · ホスト Lenny RachitskyHamel Husain and Shreya Shankar walk Lenny Rachitsky through a real error-analysis workflow, from reading traces to building a trustworthy LLM judge.評価とオブザーバビリティ評価(Evals)AI エンジニアリングのリーダーシップ+3