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59:46EN
Jane Street· 2025년 3월 12일In Young Cho on Trading Research and Finding Signal in Financial DataYaron Minsky, In Young Cho · Jane StreetIn Young Cho and Ron Minsky discuss the blurred lines between trading, research, and engineering, and why machine learning in markets is difficult.Machine LearningFintechSoftware engineering+2
32:46EN
Sequoia Capital· 2025년 2월 25일팟캐스트Don't draw the flowchart: OpenAI's Deep Research team on training agents end to endIsa Fulford and Josh Tobin explain why Deep Research was trained with reinforcement learning on hard browsing tasks instead of a human-written graph of model calls.에이전트에이전트 엔지니어링Reinforcement Learning+3
57:41EN
No Priors: AI, Machine Learning, Tech, & Startups· 2025년 2월 25일팟캐스트 · 2명A Hundred Million Cells, Four People, Three Days: Biology's ImageNet Moment진행자 Sarah Guo, Elad GilVevo Therapeutics and the Arc Institute open-sourced Tahoe-100M and a 230-million-cell atlas in the same week — and argued that virtual cells, not just protein folders, are the next language model of biology.모델과 학습AI for scienceData quality & curation+3
1:40:22EN
Cognitive Revolution "How AI Changes Everything"· 2025년 1월 22일팟캐스트Twenty-atom crystals taught an AI the physics of a heartbeat진행자 Nathan LabenzOrbital Materials' Jonathan Godwin and Tim Duignan tell Nathan Labenz how a neural network trained on tiny crystals simulated a potassium ion channel on a single 2017 GPU.모델과 학습AI for science머신러닝 기초+3
1:27:33EN
Cognitive Revolution "How AI Changes Everything"· 2024년 12월 3일팟캐스트A 70B Model Booked Dinner 20% of the Time. MultiOn Took It to 95% in a Day진행자 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.에이전트에이전트 엔지니어링Computer use+3
1:49:41EN
Cognitive Revolution "How AI Changes Everything"· 2024년 11월 21일팟캐스트A Team of Fifteen Matched Meta’s Post-Training on Meta’s Own Base진행자 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.모델과 학습Post-trainingData quality & curation+3
56:10EN
Unsupervised Learning: With Jacob Effron· 2024년 10월 29일팟캐스트DeepL Beat Google at Translation by Refusing to Be a General Model진행자 Jacob EffronOnly 18 percent of the world speaks English—and a Cologne startup turned that fact into a $2 billion language-AI company.기업과 전략Data quality & curation평가(Evals)+3
1:13:23EN
Cognitive Revolution "How AI Changes Everything"· 2024년 10월 12일팟캐스트An AI Found a GABA-Cancer Link. The Next Step Is Talking to the Cells.진행자 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· 2024년 10월 8일팟캐스트GPT-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.기업과 전략엔터프라이즈 AIAI Strategy+3
슬라이드 5장19:38EN
Matthew Berman· 2024년 9월 9일Reflection 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 models평가(Evals)+2
슬라이드 2장35:30EN
Matthew Berman· 2024년 9월 6일Reflection 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.모델과 학습Data quality & curation평가(Evals)+2
50:33EN
Sequoia Capital· 2024년 8월 20일팟캐스트The 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.인프라와 배포Reinforcement LearningData quality & curation+3
1:16:11EN
Cognitive Revolution "How AI Changes Everything"· 2024년 7월 11일팟캐스트Scooping Food Into Boxes Is 70 Percent of Plant Labor진행자 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· 2024년 7월 9일팟캐스트Scaling 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.기업과 전략Scaling LawsInference+3
1:06:04EN
20VC with Harry Stebbings· 2024년 6월 12일팟캐스트 · 2명Alexandr Wang on AI Data, Model Development and Building Scale게스트 Alexandr Wang · 진행자 Harry StebbingsScale’s founder discusses his data-bottleneck thesis, enterprise adoption, AI security and the challenges of growing a team.Data quality & curation모델과 학습AI Strategy+2
39:00EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 5월 22일팟캐스트 · 2명JPMorgan Has 150 Petabytes. GPT-4 Trained on Less Than One.진행자 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.인프라와 배포Data quality & curationPost-training+3
1:01:39EN
Cognitive Revolution "How AI Changes Everything"· 2024년 4월 13일팟캐스트GPT-5 Wants 100 Trillion Tokens. Does Earth Have Enough?진행자 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.모델과 학습Scaling LawsSynthetic data+3
38:48EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 1월 31일팟캐스트 · 2명Shopify Built a Copilot, Not an Autopilot, for Every Merchant진행자 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.제품과 UXAI Product ManagementProduct Strategy+3
40:58EN
No Priors: AI, Machine Learning, Tech, & Startups· 2024년 1월 25일팟캐스트 · 2명99% of Factory Robots Are Dumb. Covariant Wants the Smart Ones Working in Warehouses First진행자 Sarah Guo, Elad GilFormer OpenAI researcher Peter Chen explains why robot foundation models have to be trained on production floors, not in labs.모델과 학습RoboticsFoundation Models+3
27:14EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023년 12월 7일팟캐스트 · 2명Seven People Did the Work of 700. Salesforce's AI Chief Says Enterprise AI Is Still in the Second Inning진행자 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.기업과 전략엔터프라이즈 AIAI Strategy+3
1:21:50EN
Cognitive Revolution "How AI Changes Everything"· 2023년 11월 14일팟캐스트A 16K Context Window Holds About 350 Rows. That's Why Tables Need an Agent.진행자 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.모델과 학습Synthetic dataData quality & curation+3
1:32:40EN
Cognitive Revolution "How AI Changes Everything"· 2023년 10월 31일팟캐스트The Historian Who Taught GPT-3.5 to Beat GPT-4진행자 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.RAG와 지식검색 증강 생성(RAG)파인튜닝+3
37:01EN
No Priors: AI, Machine Learning, Tech, & Startups· 2023년 9월 28일팟캐스트 · 2명DNA Is Code, but You Compile It with a Pipette진행자 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.모델과 학습AI for scienceBiosecurity+3
46:37EN
20VC with Harry Stebbings· 2023년 9월 22일팟캐스트 · 2명Christian Kleinerman on AI Models, Enterprise Data and Product Decisions게스트 Christian Kleinerman · 진행자 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
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