筛选结果
36 篇排序
1:29:02ENFrom 50 Designs a Day to 1,500: How AI Changed What Jaguar Land Rover Can Explore主持 Nathan LabenzNeural Concept’s Thomas von Tschammer on specialist physics models, engineering copilots, and why Formula 1 already runs thousands of designs overnight.
1:48:43EN → 简体中文奖励从 3 涨到 180,Hacker News 上的标题全是谷歌裁员主持 Nathan LabenzCoreWeave 的 Kyle Corbitt 带 Nathan Labenz 讲 GRPO、评分标准(rubric)、强化学习环境,以及为什么大多数奖励作弊(reward hacking)只要知道去找,就很容易发现。
2:25:10EN → 简体中文微调没有死,却把一个乐于助人的模型变得邪恶了主持 Nathan LabenzNathan Labenz 谈为什么大多数工作改由提示词完成而不再微调,涌现式错位(emergent misalignment)到底揭示了什么,以及他如何看待工作、全民基本收入(UBI)和通用人工智能(AGI)。
1:32:38ENPolish Is 1% of Frontier Training Data, So Poland Built Its Own Model主持 Nathan LabenzMarek Kozlowski of Poland's National Information Processing Institute explains how PLLuM uses language adaptation, organic instructions, and on-prem small models to keep a mid-size country in control of its AI.
1:55:12EN → 简体中文28,000 份文献、一个 30 亿参数的模型、22 个人:天主教 AI 的技术账本主持 Nathan LabenzLongbeard 首席执行官 Matthew Harvey Sanders 在 The Cognitive Revolution 节目中讲述,一支使命驱动的团队如何让模型与天主教训导对齐,以及为什么微调还不够用。
38:10ENPlus Five for Format, Plus Ten if It Compiles主持 Nathan LabenzPredibase CEO Dev Rishi explains how reinforcement fine-tuning, production feedback, and narrow specialist models turn one-off training into systems that keep learning.
1:28:48ENDoctors code at 45%. The model beat them by 12 points — then 4,000 physicians had to try it once.主持 Nathan LabenzAmbience Healthcare and Cleveland Clinic walk through the AI scribe that cut pajama time, climbed ICD-10 with reinforcement fine-tuning, and won 75% voluntary use.
1:42:06EN → 简体中文每家前沿实验室每年在数据上花十亿美元,钱究竟花在哪里主持 Nathan LabenzLabelbox 首席执行官 Manu Sharma 谈 150 亿美元的 Scale 交易、强化学习(RL)“训练场”、年收入 25 万美元的 AI 训练师,以及为什么后训练(post-training)如今占去了过去属于预训练(pre-training)的预算。
47:00ENAn Eval Lasts Three Months — and a Frozen Frontier Would Still Feed Startups for a Decade主持 Jacob EffronOpenAI’s GPT-4.1 post-training lead Michelle Pokrass tells Unsupervised Learning how the team spent three months on evals before training, why agents fail on wiring more than intelligence, and when to reach for RFT.
3:53:49EN → 简体中文数据只有十二分之一,一个开源视觉模型击败了 GPT-4o主持 Nathan LabenzVeratai 的 Will Hardman 带 Nathan Labenz 梳理了两年来的视觉语言模型(VLM),从 ViT 和 CLIP 讲到 InternVL、BLINK 和 Transfusion。
55:50ENThere will not be one model. Fireworks is betting on hundreds of small experts主持 Jacob EffronPyTorch veteran Lin Qiao argues that inference is a compound system hyperscalers cannot buy their way through, and that the products with real traction still keep a human in the loop.
1:24:48EN → 简体中文Canva 的 AI 功能被使用了 100 亿次:打法不是一个聊天框主持 Nathan LabenzCanva 生成式 AI 产品负责人 John Milinovich 向 Nathan Labenz 讲解自动化与增强的区别、概念层面的界面、缺少“设计竞技场”时怎么做评测,以及应用层公司为什么仍有十年的发展空间。
1:04:54ENHe bet company equity that Transformer would last — then drew an AI map for 12,000 companies主持 Jacob EffronDatabricks chief AI scientist Jonathan Frankle tells enterprises when to prompt, fine-tune, or pre-train, and why he still refuses to forecast the next architecture.
1:09:09EN30 Percent on SWE-bench: Cosine Taught Genie How Engineers Actually Think主持 Nathan LabenzAlistair Pullen tells Latent Space how Cosine reconstructed human problem-solving, fine-tuned on billions of tokens, and took the top score on software engineering’s hardest public test.
1:51:00ENStart With Ten Gold Examples: A Three-Step Playbook for AI Automation主持 Nathan LabenzThe day after OpenAI opened GPT-4o fine-tuning, Nathan Labenz unpacks a three-year method for choosing work, extracting tacit judgment, and raising model performance.
1:10:07ENSame Intelligence, 1% of the Price: Logan Kilpatrick on Gemini Flash主持 Nathan LabenzA former OpenAI developer-relations lead, now the product manager for Google AI Studio, walks through what Gemini 1.5 Flash actually changes for builders.
1:42:17ENThe Cheapest Inference Provider Is the Most Dangerous主持 Nathan LabenzFireworks AI cofounders Lin Qiao and Dmytro Ivchenko explain why subsidized GPU bills vanish, how LoRA serving can cut cost a hundredfold, and why OpenAI is the rival that actually matters.
1:34:11ENFrom forty hours in a scanner to one: reconstructing what you see from a shared brain space主持 Nathan LabenzPaul Scotti explains how MindEye2 maps a new person's fMRI into CLIP with an hour of data, and why a linear layer plus better image generators changed the cost of reading minds.
1:27:01ENThree Million Emails, Ten LLM Calls, Six Models: Shortwave's RAG Playbook主持 Nathan LabenzFirebase founder Andrew Lee walks Nathan Labenz through the retrieval stack that turned a Gmail inbox into a working AI assistant.
1:43:54ENFifteen Examples Can Poison GPT-4: Today's AI Systems Are Exploitable by Default主持 Nathan LabenzFAR AI founder Adam Gleave walks through cheap jailbreaks in GPT-4's APIs and amateur-level wins against superhuman Go engines, and why robustness is not arriving on its own.
1:06:39ENOpenAI's First DevRel Writes 90% of His Code with ChatGPT — and Says Web Agents Still Need Years主持 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.
1:22:43ENFive billion minutes of calls: why Dialpad trained its own business LLM主持 Nathan LabenzDan O’Connell on real-time speech, verticalizing the stack, and why he and Nathan Labenz disagree about when AI employees show up.
1:21:50ENA 16K Context Window Holds About 350 Rows. That's Why Tables Need an Agent.主持 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.
1:14:48EN → 简体中文首届开发者大会(DevDay)或许催生了数千家创业公司,OpenAI 的 Logan Kilpatrick 这样说主持 Nathan LabenzOpenAI 首届开发者大会次日,开发者关系负责人 Logan Kilpatrick 对 Nathan Labenz 讲了:为什么薄薄一层 API 封装从来不算公司,为什么智能体(Agent)没有被做成完全自主的,以及 200 万美元起的定制模型到底有什么用。
加载更多已显示 24 篇,共 36 篇









