#数据/训练
Rohan Paul@rohanpaul_aiAI 评分5555Amazon ScienceAI 评分5555 Amazon Bio Discovery 发表三篇论文,用 AI 设计并实验验证抗体
Amazon Bio Discovery 团队发布三篇论文,分别针对抗体结合强度排序、可开发性预测和端到端抗体设计。
Hacker News:AI 热帖AI 评分7878 NYT 诉讼简报披露:Microsoft 高层称 AI 抓取是人类历史上最大规模的劳动窃取
《纽约时报》在诉 OpenAI 和 Microsoft 的版权案中提交法律简报,请求简易判决,其中引用被告内部文件称 AI 抓取是“人类历史上最大规模的劳动窃取”,OpenAI ChatGPT 负责人称 ChatGPT 对出版商是“生存威胁”。
Hacker News:AI 热帖AI 评分5959 编码智能体 Harness 设计的实证研究:176 个设置下消融规划、动作空间与上下文管理
arXiv 论文(arXiv:2609.20804)构建固定执行循环的轻量级 coding harness,在 Nemotron-3(30B/120B/550B)和 Mistral-Medium-3.5-128B 上、于 SWE-Bench Verified 和 Terminal-Bench 2.1 共评测 176 个设置,变化规划、动作空间和上下文管理三个组件。
SemiAnalysis 长文 RSSAI 评分5858 SemiAnalysis 实测 DeepSeek-V4.1-Flash 的 Engram:架构如何围绕 HBM 约束做 DRAM/SSD 卸载
SemiAnalysis 分析 Engram 架构(在标准 token 嵌入上扩展学习式多 token 查找)如何通过协同设计将嵌入表卸载到主存 DRAM 或 SSD,在同等质量下降低模型所需的 HBM 容量。
IT之家·人工智能AI 评分5959 北京发布“词元经济十条”,部署词元工厂建设与关键技术攻关
北京市经济和信息化局9月18日发布“词元经济十条”,即《北京市加快词元经济发展的行动方案(2026—2028年)》。方案提出高标准建设词元工厂,制定覆盖模型适配数量、词元吞吐速度、首字延迟、缓存命中率、PUE 等指标的分级评价标准;推动推理专用芯片、模型轻量化、边缘端部署等技术攻关;并部署词元质量评测体系、词元分发平台、通用与垂直领域智能体培育、算力与词元金融工具及词元工程师再技能化培训等措施。
IT之家·人工智能AI 评分5555 消息称长鑫存储计划进军 NAND 闪存市场,将与三星、SK 海力士、美光竞争
据路透社援引三位知情人士报道,长鑫存储计划在北京新工厂建设 NAND 闪存研发生产线,并已就 NAND 业务与客户接洽。TrendForce 数据显示,2026 年第二季度三星以 29.3% 营收份额居全球闪存市场首位;同期长鑫 DRAM 营收达 146.24 亿美元,环比增长 99.3%,市占率从 7.6% 升至 9.5%。
IT之家·人工智能AI 评分6161 美光高管称有意义的新增内存供应要到 2028 年才开始爬坡
美光 CEO 高级顾问苏米特·萨达纳在 Six Five Summit 2026 上表示,内存短缺已覆盖所有市场,真正有意义的新增供应要到 2028 年才开始爬坡,供需何时平衡看不到明确时间。
Rohan Paul@rohanpaul_aiAI 评分6969引用Anthropic@AnthropicAIAI systems are getting more powerful, and they're increasingly being used to build the next version of themselves. We want to illuminate that progress for the public. Today, we're sharing three measurements that help track AI development: 1. How much AI R&D is done by AI. 2. How well AI agents are overseen. 3. How compute is allocated. We provide a snapshot of these metrics from inside Anthropic. Any frontier developer could publish the same measures, and third parties could verify them. As the world considers pacing the frontier, we should do everything possible to minimize the gap between what frontier labs know and what the public knows. This means better measuring the development of AI, publishing our findings, and giving society an opportunity to decide how to use this information. Read the full post and methodology: anthropic.com/institute/measu…
TechCrunch:AIAI 评分6161 FAA 拟投入 8.75 亿美元部署 AI 空管软件 Smart
FAA 将推出名为 Smart(Strategic Management of Airspace, Routes and Trajectories)的 AI 空管软件,12 年合同金额 8.75 亿美元。
Rohan Paul@rohanpaul_aiAI 评分5555Figure 的 Helix 2.5 端到端机器人模型在 30 户未见家庭中将零样本家庭任务成功率提升逾六倍,从 9% 升至 56%,且要求整个任务完成、无部分得分。
引用Rohan Paul@rohanpaul_aiFigure just released this video. a beautiful robot future is indeed coming. Its Helix 2.5 model (Figure’s end-to-end robotics brain) lifted zero-shot household-task success more than sixfold across 30 unseen homes. Figure also reports success rising from 9% to 56%, with success requiring the entire task to be completed and no partial credit awarded.
Anthropic:The Institute精选AI 评分7373 Anthropic 发布前沿 AI 开发节奏测量工具与内部指标快照
Anthropic 发布一套测量前沿 AI 开发节奏的指标,覆盖 AI 主导研发、智能体监督和算力分配三方面。
推荐理由:Anthropic 公开测量自身 AI 研发自动化程度、智能体监督和算力分配的方法与数据,读者可以看到前沿实验室透明度报告的一种可复用范式。
Ars Technica:AIAI 评分6565 微软高管曾称 AI 抓取是"人类历史上最大规模的劳动窃取",新闻集团据此推进诉讼主张
新闻集团在诉讼动议中指责 Microsoft 将为 Bing 购买的数据集转售给 OpenAI 作训练数据,且 OpenAI 从受商业限制协议约束的第三方获取含 180 万篇文章的 NYT 数据集并用于训练。原告方引用微软内部文件承认生成式 AI 对训练数据生产者就业存在"真实风险",并主张法院若认定抓取新闻不属于合理使用,可打破各 AI 公司竞相免费搭车的"囚徒困境"。
Tomasz Tunguz 博客AI 评分5252 Tomer Tunguz 解析 harness 的毛利机会:同一模型可省 71% 成本
Tomer Tunguz 撰文分析 Berkeley 本周发表的 HarnessTax 研究:控制 AI Agent 的 harness 系统可在不损失准确率的情况下,将同一结果的成本降低 71%(GPT-5.6 Sol 从 Claude Code 换到 Pi,每解决一个任务成本从 $1.540 降至 $0.441)。
jason@jxnlcoAI 评分6161引用Exa@ExaAILabsWe built a time machine for the web. Introducing Exa Snapshot: an index of 400 billion historical snapshots of webpages that lets you search as if it's the past. Snapshot is already being used for backtesting prediction models, RL at labs, exploring the pre-AI web, and more.
IT之家·人工智能AI 评分5252 OpenAI 前 RLHF 研究者 Diogo Almeida 创办 TypeSafe AI,发布不生成文本的结构化决策模型 Jev
TypeSafe AI 于 9 月 16 日发布 System One 模型 Jev,不生成文本,仅输出 Choice、Score、Noul 三类结构化决策,输入词元定价每百万 0.042 美元,输出词元免费。
IT之家·人工智能AI 评分6464 华为轮值董事长汪涛:AI 战略核心是算力,坚持硬件变现
据财联社报道,华为副董事长、轮值董事长汪涛 9 月 17 日在上海华为全联接大会上介绍智能时代部署,称华为 AI 战略核心是算力、坚持硬件变现,并以超节点加集群打造算力底座。
IT之家·人工智能AI 评分5252 零跑发布自研世界模型智驾 4.0,2026 年冲刺智驾第一梯队
零跑在 9 月 16 日技术发布会上公布自研世界模型智能辅助驾驶,称达到全行业第一梯队水平,将覆盖 A 到 D 全系车型。此前内部对智驾 2.0、3.0、4.0 三条路线赛马,4.0 一段式端到端世界模型因综合接管率仅为头部车企三分之一而胜出;4.0 架构成本仅为 1.0 时期的 40%,明年起将面向 35 万辆激光雷达版车型免费升级,2024 款 C16/C11/C10 也可使用。
Hacker News:AI 热帖AI 评分5151 BITCOS 布局突破三值 LLM 的 1.58-bit 存储壁垒
论文提出 BITCOS,一种由存在位图加压缩符号向量组成的三值 LLM 权重布局,按零密度 z 计成本为 2−z bits per weight。作者实测 29 个三值模型发现零权重占比最高达 51.5%,其中 26 个模型的存储比 five-trit packing 更紧凑,最稀疏模型达 1.485 bits per weight。
MarkTechPostAI 评分6363 Knowledgator 发布 GLiFormer:575M 参数编码器不生成 token 实现 91.10 F1 嵌套 JSON 提取
Knowledgator Engineering 发布 schema 条件化编码器框架 GLiFormer,单个模型可在推理时按标签和 schema 完成 NER、分类、关系抽取、嵌套 JSON 结构化和文本嵌入。
蚂蚁百灵:Developer BlogAI 评分4444 蚂蚁百灵 OmniTable 如何把一次数据验证从 14 天缩短到 2.5 天
蚂蚁百灵为 PB 级大模型数据治理搭建的统一宽表系统 OmniTable,将一项真实 SFT 数据治理任务的处理周期从约 14 天缩短至约 2.5 天,缩短约 82.1%,人工操作步骤从约 45 个减至约 12 个。该系统目前管理超过 35 PB 数据、逾 3050 亿条记录,相关论文获 VLDB 2026 最佳工业论文奖。
IT之家·人工智能AI 评分5555 消息称苹果筹划重返服务器业务,高配版搭载 4 颗 M8 Ultra 芯片
据 The Information 援引知情人士报道,苹果筹划推出搭载自研芯片的企业级 AI 服务器,基础版配备 2 颗 M8 Ultra 芯片,高配版 4 颗,并考虑采用英伟达 NVLink Fusion 实现芯片互连。
IT之家·人工智能AI 评分5555 集邦咨询预计 2026 年全球数据中心用电需求同比增长 31% 至 161GW
TrendForce 集邦咨询发布 AI Server 产业研究,预估 2026 年全球数据中心电力需求容量达 161GW,同比增长约 31%,其中 AI Server 占 33.4%。报告预计 2027 年增速仍超 30%,2030 年需求达 490.7GW,而电网可供容量仅 222.6GW,账面缺口 268GW,其中包含表后自建发电未被计入及并网交付延期两部分因素。
IT之家·人工智能AI 评分6666 消息称字节跳动今年上半年利润降至 200 亿美元,因 AI 投入加大
《The Information》援引知情人士消息称,字节跳动今年上半年净利润降至 200 亿美元,降幅为个位数百分比,主因是 AI 投入增加;同期营收 1,200 亿美元,同比增长约 30%。公司还扩张 AI 云业务,近期从银行借入约 300 亿美元,创历来最大单笔贷款,或用于支持 AI 投入。
IT之家·人工智能精选AI 评分7777 404 Media 曝光 OpenAI 莉莉计划:人工审核 ChatGPT 聊天记录以优化模型
404 Media 披露 OpenAI 内部代号为莉莉计划(Project Lily)的项目,由时薪超 50 美元的提示词审核员查看匿名化后的真实用户聊天记录,评判回复是否切题、是否存在 AI 式话术和谄媚口吻。
推荐理由:报道披露了人工审核流程的运作细节和隐私边界,读者可以据此了解模型优化背后的人力环节与数据风险。
IT之家·人工智能AI 评分5353 国家网信办通报执法典型案例,涉网页篡改、数据泄露及 AI 标识与安全评估违规
国家网信办通过“网信中国”通报一批执法典型案例,涵盖网页篡改、恶意程序、数据泄露、个人信息违法出境等类别,属地网信办均已责令改正并处罚款或警告。其中两起涉及人工智能:四川某公司小程序因未落实 AI 生成合成内容显式和隐式标识被责令下线,江苏某公司以 API 中转站方式调用大模型接口提供服务但未按规定开展安全评估被警告。
IT之家·人工智能AI 评分6666 工信部与国家发改委印发电子信息制造业发展“十五五”规划,提高先进制程并突破高端芯片
工信部、国家发改委联合印发《电子信息制造业发展“十五五”规划》,提出到 2030 年规模以上企业营业收入突破 30 万亿元、研发投入强度达到 3.5%。规划部署 17 项任务和 9 个专栏,要求提高先进制程能力,突破高端手机核心芯片和 PC 高性能芯片,加强开源鸿蒙等国产操作系统搭载,并推进 RISC-V 研发与产业化。
Amazon ScienceAI 评分5353 Amazon Science 解读 ML 研究智能体为何不易过拟合:短提示即可复现策略
Amazon Science 介绍其论文《What fits (into few tokens) doesn't overfit》,用可重置的 LLM 研究智能体实验解释为什么机器学习研究社区长期在固定基准上迭代却很少过拟合。
IT之家·人工智能AI 评分5858 谷歌完成对 AI 编程创企 Mechanize 的人才收购
据《商业内幕》报道,谷歌完成对旧金山 AI 编程初创企业 Mechanize 的人才收购,联合创始人塔迈·贝西罗格鲁自 8 月起加入谷歌 DeepMind,另有十多名工程师一同转投。
Dwarkesh Patel:Podcast & BlogAI 评分5656 Dwarkesh 播客:John Schulman 等三位 AI 研究者讨论递归自我改进离我们有多远
Dwarkesh Patel 与 John Schulman、Beren Millidge、Charlie O'Neill 对谈递归自我改进(RSI)的最新一期播客。
Google Developers BlogAI 评分5454 Google 开源 autofinetune:用 AI Agent 在 TPU 上自动完成 LLM 后训练
Google 发布 autofinetune 项目,把 autoresearch 的自主研究循环应用到 LLM 后训练(SFT 和 GRPO),使用 Tunix、Gemma、Cloud TPU,由 Antigravity CLI 和 Gemini Flash 3.7 编排。
IT之家·人工智能AI 评分5252 Meta 重组应用 AI 部门,邀请部分员工重返管理岗
据《商业内幕》援引 4 名知情人士报道,Meta 调整应用 AI(AAI)部门人员安排,邀请部分非管理岗员工自愿重返管理岗位。AAI 由 Meta 今年设立,约 7000 名员工被调入,部分人调入后不再担任管理职务;此举部分逆转了 Meta 过去一年裁减中层管理人员的方向,此前 Meta 今年已裁员约 10%。
Rohan Paul@rohanpaul_aiAI 评分7272
Together AI 研究与产品博客精选AI 评分6363 Together AI 扩展微调服务:新增模型、实验跟踪与更细训练控制
Together AI 扩展 Together Fine-Tuning 服务,新增对 GLM-5.3、Kimi K2.7、Qwen 3.5 系列等最新开源权重模型的支持,并上线实时实验跟踪、数据集预览验证等能力。
推荐理由:原文给出新模型支持、Expert LoRA 实测对比、自动早停和降价幅度等具体细节,读者可据此评估微调工作流的实际改进。
Google Research:BlogAI 评分4949 Google 提出 ToolGrad:用文本“梯度”高效生成工具调用数据集
Google 在 ACL 2026 提出 ToolGrad,用文本“梯度”迭代构建 API 工作流,先产出真实工具调用链再反向标注用户提示词,实现近乎 100% 的数据生成通过率。
Rohan Paul@rohanpaul_aiAI 评分6666引用Rohan Nayak@RohanNayak2We grew from ~0 to $500M ARR, adding $250M last year alone while being EBITDA profitable. 1,300-word post on every growth tactic that worked for us: 1. What got us from 0-$400M ARR in the US works in every country. 2. Retention and Monetisation Hacks. 3. Localization > Translation. 4. Experiment with $1M internal seed checks. 5. Pivoting to only AI content production unlocked 100% ARR growth. Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios. 1. Acquisition Playbook for 0-$10M ARR in any country in 6 months We run a 90 sec video trailer of an audio drama as an Ad and ask users to download the app if they're interested in the rest of the story. Our core insight after spending >$100M on acquisition is: If the clickthrough rate (CTR) for an ad goes from 2% to 2.25%, our customer acquisition cost (CAC) decreases by ~ 30%. We remodelled our system around this insight and built an AI-first 2.25% CTR ad manufacturing machine that works in every country. For every new country, we take our hit shows -> use LLMs to extract and most intriguing moments -> Write a 5-min script combining all the best parts. The first 60 seconds has to have a hook every 5 seconds and needs to end with a crazy cliffhanger to force a download mid-scroll. If a marketing video is not hitting our benchmarks (2.5% CTR and 55% 3-second through-play), a creative director gets involved to change the hook or cliffhanger to get the numbers there. AI lets us make 1,000 Ads per show, and in total we do ~17.5k Ads per month. When a business creating scales from 1k to 10k Ads, the normal thing is for CAC to skyrocket. But with what I just shared, we 7-8x'd our User Acquisition budget without increasing our CAC materially. It took us 8 months to figure this out, but then the timeline from 0-$10M in every country got shorter and shorter: US revenue grew to $25M in 19 months. (US is now 78% of total) Germany to 21M in 11 months. France to $10M in 3 months. 2. Retention and Monetisation Hacks. We knew we wanted to create an audio entertainment platform, but there was no standard format. For the first 2-3 years, we tried 10 different formats before landing on the winner. After we got it right with audio drama (8-12min chapters, written for mobile fiction, serialized, episodes have strong hooks and end on cliffhangers), avg daily streaming time went from ~25mins to 150+. Audio drama made us realize that Pocket FM was creating a whole new medium. There was no playbook for anything that we were doing. Everything had to be thought & built from scratch. This is what we did for each major bullet: - Monetization: Users have some free daily minutes to listen; then it's pay per episode. We also added the option for users to unlock episodes by watching ads, which is doing extremely well. Ads scaled from zero to a ~$90M run rate in 12 months. - Engagement & production: You don't become obsessed with an app you open once a week. To have users engage daily, we make the next episode free every day. Also helps them build the habit. - Discovery. Huge problem because people consuming Pocket FM enter the app, tap the show, and lock the screen. We fixed it by doing "playlists" of episodes. Once users's free minutes on a show are done, we ask users if they want to pay. If they say no, we play a new show, one where they haven't used their free mins. And we stitch episodes of different shows together that way, creating natural discovery. 3. Localization > translation. 78% of our revenue is still concentrated in the US. Localisation is fixing this: You might write a show for a Spanish audience where language, jokes, folklore, have a certain flavor; if you merely translate the show for, say, a Norwegian audience, that color is lost and hurts the show in the Norway. Listeners would relate more if the show was written by a Norwegian. That's why, instead of translating, we localize shows to different regions. We use AI to take the spine of stories and adapt their whole cultural layer to the other country. The results: a US show that was localized for a German audience had 50% higher retention than the translated version. Localization + our user acquisition playbook led to: - $10M+ ARR in France within 3 months - $21M+ ARR in Germany within 11 months. And this expands writers' addressable market. We get messages of writers thrilled to have revenue coming from the US, India, EU, LatAm, without them doing much incremental work. 4. Experiment. We give $1M checks to new internal initiatives, and the team has 12-18 months to prove their thesis. If they prove it, we double down. This is how Pocket Saga came about, our AI video microdrama app. It's an AI video equivalent of Pocket FM. Same shows and structure, but as a vertical 2-min mobile video series. We launched it two months ago and it's at ~$15M ARR. Pocket's broader thesis is to help creators tell their stories to as many people as possible. Start with audio drama -> multiple languages or localize to diff countries -> microdrama -> more formats like movies, TV shows, and games. Best of all is that writers get revenue streams not only from countries they wouldn't have tapped into, but also from formats they wouldn't have thought possible. 5. Pivoting to only AI content production unlocked 100% ARR growth. In mid-2024, we pivoted to only AI content production. Our growth took a very direct hit. Pocket was already at $200M ARR, growing 50% YoY, and we flatlined for a whole semester. Six months later, the business exploded. After the switch, we went from ~25k hours of content produced per year to over 2.5M hours, which are also higher quality. Because AI orchestrates the writing and more data is fed into it, more blockbusters come out. Over 90 titles have $1M+ in lifetime earnings and 13 crossed $10M. Quality control is done with LLM as a judge, and LLMs are quite tough. Results are very encouraging: - 12-month revenue retention went from 44% to 76%. - In the past year, we added $250M in net new ARR. __________________________________ Pocket Entertainment (Pocket FM + Pocket Saga) has become the largest AI entertainment platform. We have the largest storytelling catalog with 770,000 titles, 550k creators, and 5.5B hours of playtime with minute by minute retention & engagement data. We are using all of this data to improve every aspect of our business. Our Bet: In the next 3 years we will be able to produce Naruto-like series for $1000. Netflix has to spend $17B to find 100 Blockbusters per year. What happens when you can produce 1M high quality shows for $1B? The streaming wars caused a $300B reallocation of market cap. The AI entertainment wars may be a $1T+ reshuffling of market cap. We are at a unique spot. Unlike other AI creator tools like Runway or Midjourney, we own both the supply and demand side of AI content. We've attracted over 550,000 writers who are producing an annualized 2.5M hours of content every year. This pairs with 137B+ minutes streamed. Because of this, growth is accelerating as we scale further. We have multiple S-curves inflecting at the same time: - AI produces better and more ads -> scale faster in new countries - Writers + AI produce more shows -> more blockbusters - Localisation -> more reach per blockbuster - Audio to video format expansion -> larger TAM -> more creators Every aspect of our business is designed to improve another, and everything is aligned so that the number of blockbusters continues to rise. If you're interested in building at the intersection of ai, tech and entertainment, DM me.
OpenAI:官网动态精选AI 评分6363 OpenAI 在 ChatGPT Work 中推出 Data agent
OpenAI 在 ChatGPT Work 中推出新的 Data agent,用户用自然语言即可连接公司数据、分析变化并生成可分享的交互式仪表盘。
推荐理由:官方公布了数据源和 BI 工具接入范围及权限管控方式,读者可据此评估它在自有数据工作流中的落地路径。
Hacker News:AI 热帖AI 评分7272 数学家要求 OpenAI 证明未使用其研究成果训练模型
数学家 Andreas Thom 指责 OpenAI 在非 sofic 群结果上行为不诚实,质疑他与 ChatGPT 的互动可能进入了训练数据。此前 NYU 教授 Tristan Buckmaster 也质疑 OpenAI 是否受益于其 unpublished 工作;OpenAI 否认访问过特定用户数据,但不排除去标识化数据的间接影响。多位研究者担心此类事件会促使数学界走向更保密。