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96 件
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1:27:33EN
Cognitive Revolution "How AI Changes Everything"· 2024年12月3日ポッドキャストA 70B Model Booked Dinner 20% of the Time. MultiOn Took It to 95% in a Dayホスト Nathan LabenzMultiOn CEO Div Garg returns to The Cognitive Revolution to recap 18 months of agent hype, the AgentQ stack, and why 2025 might still be the year of vertical agents.エージェントエージェント工学Computer use+3
1:49:41EN
Cognitive Revolution "How AI Changes Everything"· 2024年11月21日ポッドキャストA Team of Fifteen Matched Meta’s Post-Training on Meta’s Own Baseホスト Nathan LabenzNathan Lambert walks through Tulu 3: the three-stage recipe, the 1 percent versus 14 percent lesson, and why open post-training now lives or dies on data and evals.モデルと学習Post-trainingData quality & curation+3
56:10EN
Unsupervised Learning: With Jacob Effron· 2024年10月29日ポッドキャストDeepL Beat Google at Translation by Refusing to Be a General Modelホスト Jacob EffronOnly 18 percent of the world speaks English—and a Cologne startup turned that fact into a $2 billion language-AI company.企業と戦略Data quality & curation評価(Evals)+3
1:13:23EN
Cognitive Revolution "How AI Changes Everything"· 2024年10月12日ポッドキャストAn AI Found a GABA-Cancer Link. The Next Step Is Talking to the Cells.ホスト Nathan LabenzMichael Levin and Leo Pio Lopez on a multilayer embedding that predicted melanoma, a cancer with no mutations, and why biology's real interface is cognitive.AI for scienceComputational BiologyAI research+3
59:30EN
Sequoia Capital· 2024年10月8日ポッドキャストGPT-4 Gets Half Your Data Questions Wrong. Snowflake's Answer Is Systems DesignUsing an off-the-shelf model on enterprise data yields about 45% reliability—one wrong answer out of every two.企業と戦略エンタープライズ AIAI Strategy+3
スライド 5 枚19:38EN
Matthew Berman· 2024年9月9日Reflection 70B: Conflicting Results and Unanswered QuestionsMatthew Berman · Forward FutureMatthew Berman reviews his early coverage, independent evaluations and competing claims about the September 2024 release.BenchmarksOpen-weight models評価(Evals)+2
スライド 2 枚35:30EN
Matthew Berman· 2024年9月6日Reflection 70B: Its Creators Explain the Proposed Self-Correction MethodMatthew Berman, Matt Shumer, Sahil Chaudhary · Forward FutureA launch-day discussion with Matt Shumer and Sahil Chaudhary covers synthetic data, selective reflection and evaluation limits.モデルと学習Data quality & curation評価(Evals)+2
19:46EN
Matthew Berman· 2024年8月28日Linus Torvalds and Dirk Hohndel on AI Coding, Bugs and HypeMatthew Berman, Linus Torvalds, Dirk Hohndel · Forward FutureMatthew Berman reacts to two conversations about AI-assisted programming, code review, automation and open data.コーディングエージェントSoftware engineeringOpen Source+2
50:33EN
Sequoia Capital· 2024年8月20日ポッドキャストThe 40% data-center save started with an email that ended in a question markPhaidra founder Jim Gao tells Sequoia how an AlphaGo-inspired bet at Google became reinforcement learning that runs billion-dollar plants.インフラとデプロイReinforcement LearningData quality & curation+3
1:16:11EN
Cognitive Revolution "How AI Changes Everything"· 2024年7月11日ポッドキャストScooping Food Into Boxes Is 70 Percent of Plant Laborホスト Nathan LabenzChef Robotics CEO Rajat Bhageria on 20 million plated meals, a foundation model for food, and why the kitchen may leave the house.RoboticsData quality & curationLocal inference+3
1:00:28EN
Sequoia Capital· 2024年7月9日ポッドキャストScaling Laws Aren't Done. Don't Lock Yourself Into Yesterday's Architecture.Microsoft CTO Kevin Scott on why the exponential is still compounding, why inference will dwarf training, and why a language model is the world's crappiest database.企業と戦略Scaling LawsInference+3
1:06:04EN
20VC with Harry Stebbings· 2024年6月12日ポッドキャスト · 2 人Alexandr Wang on AI Data, Model Development and Building Scaleゲスト Alexandr Wang · ホスト Harry StebbingsScale’s founder discusses his data-bottleneck thesis, enterprise adoption, AI security and the challenges of growing a team.Data quality & curationモデルと学習AI Strategy+2
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
1:01:39EN
Cognitive Revolution "How AI Changes Everything"· 2024年4月13日ポッドキャストGPT-5 Wants 100 Trillion Tokens. Does Earth Have Enough?ホスト Nathan LabenzTraining GPT-3 took about a trillion tokens; GPT-4 took about ten trillion. The next jump is a data question as much as a compute one.モデルと学習Scaling LawsSynthetic data+3
38:48EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年1月31日ポッドキャスト · 2 人Shopify Built a Copilot, Not an Autopilot, for Every Merchantホスト Sarah Guo, Elad GilShopify VP of Core Product Glen Coates on collapsing duplicate engines, shipping AI that proposes but never commits, and teaching storefront search to understand a sweater.プロダクトと UXAI Product ManagementProduct Strategy+3
40:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024年1月25日ポッドキャスト · 2 人99% of Factory Robots Are Dumb. Covariant Wants the Smart Ones Working in Warehouses Firstホスト Sarah Guo, Elad GilFormer OpenAI researcher Peter Chen explains why robot foundation models have to be trained on production floors, not in labs.モデルと学習RoboticsFoundation Models+3
スライド 5 枚19:37EN
Matthew Berman· 2023年12月13日How Rephrased Test Data Inflated a 13B Model’s Benchmark ScoresMatthew Berman · Forward FutureMatthew Berman examines a 2023 experiment showing how reworded benchmark questions can evade simple contamination checks and inflate model scores.Benchmarks評価(Evals)Data quality & curation+2
27:14EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年12月7日ポッドキャスト · 2 人Seven People Did the Work of 700. Salesforce's AI Chief Says Enterprise AI Is Still in the Second Inningホスト Sarah Guo, Elad GilClara Shih tells Sarah Guo and Elad Gil why Salesforce built a shared LLM platform, why data cleanup beats model shopping, and why AI is becoming the enterprise UI.企業と戦略エンタープライズ AIAI Strategy+3
1:21:50EN
Cognitive Revolution "How AI Changes Everything"· 2023年11月14日ポッドキャストA 16K Context Window Holds About 350 Rows. That's Why Tables Need an Agent.ホスト Nathan LabenzGretel AI founder Alex Watson tells Nathan Labenz why most company data is still a spreadsheet, why GPT-4 is the wrong tool for generating it, and how differential privacy lets a model learn a disease without learning a patient.モデルと学習Synthetic dataData quality & curation+3
1:32:40EN
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
37:01EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023年9月28日ポッドキャスト · 2 人DNA Is Code, but You Compile It with a Pipetteホスト Sarah Guo, Elad GilGinkgo Bioworks CEO Jason Kelly tells No Priors why cells are worth the magic, why biology has no AWS, and why a pandemic should look like cybersecurity.モデルと学習AI for scienceBiosecurity+3
46:37EN
20VC with Harry Stebbings· 2023年9月22日ポッドキャスト · 2 人Christian Kleinerman on AI Models, Enterprise Data and Product Decisionsゲスト Christian Kleinerman · ホスト Harry StebbingsSnowflake’s product leader discusses model choice, enterprise adoption and the trade-offs between speed, reliability and simplicity.AI Product ManagementData quality & curationProduct Leadership+1
2:15:11EN
Cognitive Revolution "How AI Changes Everything"· 2023年9月22日ポッドキャストTwenty Gibberish Tokens Jailbroke ChatGPT and Bardホスト Nathan LabenzCarnegie Mellon’s Zico Kolter, Andy Zou, and Asher Trockman walk through a universal jailbreak that transfers from open models to commercial APIs, and a training-free way to steal some of pretraining’s benefit.セキュリティと安全性AI security安全性とガバナンス+3
スライド 4 枚9:59EN
Matthew Berman· 2023年9月7日Open Interpreter: A 2023 Local Code-Execution WalkthroughMatthew Berman · Forward FutureMatthew Berman installs Open Interpreter, analyzes local files with GPT-4 and tries a local Code Llama setup, with mixed results.コーディングエージェントLocal inference開発ワークフローとテスト+2
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