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170 件
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42:08EN
Sequoia Capital· 2024年7月30日ポッドキャストMeta Trained a 405B Teacher on 16,000 GPUs — Then Told the World to Distill ItTwo days after Llama 3.1 405B shipped, Meta's generative-AI product lead walks Sequoia through why the most expensive model in the family is free, and why he thinks the frontier itself is heading toward commodity.オープンソースとコミュニティOpen-weight modelsDistillation+3
スライド 3 枚13:40EN
MBMatthew Berman· 2024年7月29日Testing Llama 3.1 8B: Benchmark Gains and Mixed Practical ResultsMatthew Berman · Forward FutureMatthew Berman tests coding, reasoning and other prompts on the small model and finds several failures despite stronger reported benchmarks.Open-weight models評価(Evals)モデルと学習+1
25:56EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年7月11日ポッドキャスト · 2 人A Prompt Cannot Hold a Good Design: Six AI Practitioners at Midyear 2024ホスト Sarah Guo, Elad GilSarah Guo and Elad Gil stitch together clips from Stripe, Figma, Figure, OpenAI's Sora team, Cognition, and Scale into a half-year snapshot of what people actually shipping AI were willing to claim.企業と戦略AI Strategy評価(Evals)+3
1:16:22EN
Cognitive Revolution "How AI Changes Everything"· 2024年7月9日ポッドキャストHe Spent 20 Hours Crafting a Prompt. DSPy Beat Him Anywayホスト Nathan LabenzSander Schulhoff walks through The Prompt Report — a 78-page survey of what actually works when you prompt large language models.Prompting & in-context learningPrompt optimizationエージェント工学+3
59:11EN
Sequoia Capital· 2024年6月25日ポッドキャストDon't Train a Model. Factory's Founders Say Go Dig for GoldMatan Grinberg and Eno Reyes tell Sequoia's Training Data why they sell a fleet of job-specific droids on top of other people's models — and how that stack just took SWE-bench from 14% to 19%.コーディングエージェントエージェント工学評価(Evals)+3
1:37:07EN
Cognitive Revolution "How AI Changes Everything"· 2024年6月22日ポッドキャストA Text-Only AI Outdiagnosed GPs. Google Still Won't Ship It.ホスト Nathan LabenzGoogle researchers Vivek Natarajan and Khaled Saab walk Nathan Labenz through five medical-AI papers, from radiology reports to an AI doctor that still has to earn trust.モデルと学習AI in healthcareMultimodal AI+3
1:25:10EN
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
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
58:28EN
Cognitive Revolution "How AI Changes Everything"· 2024年5月22日ポッドキャストFrom Jailbreaking ChatGPT to Anthropic’s First Prompt Engineer: How Alex Albert Ships Claude 3ホスト Nathan LabenzA year after building jailbreakchat.com, Alex Albert walks Nathan Labenz through Claude 3’s writing, orchestration, character work, and the guardrails app developers still skip.モデルと学習安全性とガバナンス評価(Evals)+3
39:00EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年5月22日ポッドキャスト · 2 人JPMorgan Has 150 Petabytes. GPT-4 Trained on Less Than One.ホスト Sarah Guo, Elad GilScale AI founder Alexandr Wang tells No Priors why easy internet data is exhausted, why expert-plus-model teams still win, and why the path to AGI looks more like curing cancer than inventing a vaccine.インフラとデプロイData quality & curationPost-training+3
スライド 2 枚12:50EN
MBMatthew Berman· 2024年5月22日Testing Smaug: Hosted 70B Versus a Smaller Local ModelMatthew Berman · Forward FutureMatthew Berman compares two Smaug configurations on Snake, math and reasoning puzzles.Open-weight models評価(Evals)モデルと学習
54:25EN
Cognitive Revolution "How AI Changes Everything"· 2024年5月9日ポッドキャストJail Only for Perjury: Scott Wiener Walks Through California's Frontier AI Billホスト Nathan LabenzSB 1047 sets the same 10^26 FLOPs threshold as the Biden executive order, and the San Francisco senator wants testing without a licensing regime.セキュリティと安全性安全性とガバナンスAI Regulation+3
1:05:52EN
Cognitive Revolution "How AI Changes Everything"· 2024年5月3日ポッドキャストInfinite AI Interns Are Already on the Jobホスト Nathan LabenzNathan Labenz tells computer-science students how to stand out when models are beating doctors on routine work and firms are hiring fewer juniors.エージェントAI StrategyHuman-AI Collaboration+3
スライド 3 枚16:28EN
MBMatthew Berman· 2024年4月23日Testing Reka Core: Multimodal Benchmarks, Coding and ReasoningMatthew Berman · Forward FutureMatthew Berman reviews Reka’s reported model comparisons and tests Core on code, logic and image understanding.Multimodal AIBenchmarksモデルと学習+1
スライド 3 枚15:01EN
MBMatthew Berman· 2024年4月19日Testing Llama 3 at Launch: Code, Reasoning and Image GenerationMatthew Berman · Forward FutureMatthew Berman tries Meta AI’s new interface on a snake game, logic puzzles and rapid image generation.Open-weight models評価(Evals)モデルと学習+1
24:32EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年4月18日ポッドキャスト · 2 人Image Generation Is Still a Loot Box. Suhail Doshi Wants to Scale Pixelsホスト Sarah Guo, Elad GilThe Playground AI founder tells No Priors why he skipped language models, how a noise-sampling trick fixed SDXL's muddy brightness, and what a large vision model would actually do.マルチモーダルとメディアGenerative media画像と動画+3
1:23:09EN
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
スライド 3 枚14:23EN
MBMatthew Berman· 2024年3月31日Testing DBRX: Coding Strengths and Reasoning GapsMatthew Berman · Forward FutureMatthew Berman examines Databricks’ open-weight model and puts it through coding, arithmetic and reasoning prompts.Open-weight modelsモデルと学習評価(Evals)+1
27:32EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年3月28日ポッドキャスト · 2 人From a side project to 700 integrations: Harrison Chase on what still blocks agentsホスト Sarah Guo, Elad GilThe LangChain CEO walks through a two-year churn of patterns, and why memory, planning, and few-shot data still matter more than swapping models.エージェントエージェント工学Agent memory+3
1:28:37EN
Cognitive Revolution "How AI Changes Everything"· 2024年3月14日ポッドキャストTwelve Hours After a GPT-4 Demo, He Turned a Decade-Old Legal Startup Aroundホスト Nathan LabenzPablo Arredondo, co-founder of CaseText and now VP of CoCounsel at Thomson Reuters, traces legal research from 19th-century citators to a retrieval-anchored GPT-4 product that law firms treat more like a colleague than a search box.企業と戦略検索拡張生成(RAG)評価(Evals)+3
スライド 2 枚21:16EN
MBMatthew Berman· 2024年3月9日ArtPrompt: What a 2024 Study Found About Visual Text and Model SafetyMatthew Berman · Forward FutureMatthew Berman reviews an ASCII-art recognition benchmark and reports mixed results from his own informal tests.AI Safetyセキュリティと安全性評価(Evals)+1
スライド 3 枚23:00EN
MBMatthew Berman· 2024年2月22日Testing Google Gemma 7B at Launch: Setup MattersMatthew Berman · Forward FutureMatthew Berman compares local and hosted tests of Google’s early open-weight model.Open-weight models評価(Evals)モデルと学習
1:06:39EN
Unsupervised Learning: With Jacob Effron· 2024年1月9日ポッドキャストOpenAI's First DevRel Writes 90% of His Code with ChatGPT — and Says Web Agents Still Need Yearsホスト Jacob EffronLogan Kilpatrick on picking GPT-3.5 versus GPT-4, why plugins stalled, and why the public internet is not ready for autonomous agents.企業と戦略AI StrategyPrompting & in-context learning+3
1:22:59EN
Cognitive Revolution "How AI Changes Everything"· 2023年12月2日ポッドキャストIf Your Agent Is Right 80% of the Time, Ten Steps Leave You With 10%ホスト Nathan LabenzImbue CTO Josh Albrecht tells Nathan Labenz how a 27-person, pre-product research company is putting $200 million and 10,000 H100s toward agents that can actually finish a long job.エージェントエージェント工学Reasoning & test-time compute+3
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