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9月21日周一
  1. IT之家·人工智能63

    IFR 数据显示 2025 年全球人形机器人售出约 7000 台,行业静待后续爆发

    据路透社报道,国际机器人联合会(IFR)数据显示,去年全球面向工业及专业服务领域售出人形机器人约 7000 台。作为对比,2024 年全球新部署普通工业机器人约 54.2 万台、服务机器人约 19.9 万台;不少 2025 年售出的人形机器人未投入实际生产,采购方多用其采集数据优化 AI 模型。

  2. Rohan Paul52

    Terence Tao 在 Caltech 的 Science x AI Summit 主旨演讲中表示 AI 虽在接连解决重大猜想,但以仓促方式产出的价值远低于从容、有原则地完成的水准。

    引用Rohan Paul@rohanpaul_ai

    this will be a common statement from many elite specialists from almost all intellectual fields, once AI starts doing the field’s prestige work. "we have to slow down. This is insane, the pace and there's no reason to be this fast. There's no reason at all." - Terence Tao's keynote at the Science x AI Summit at Caltech: top experts welcome AI as a tool, then ask it to slow down the moment it starts producing results they used to own. ---- From "SAIR" YouTube channel, (full video link in comment)

  3. IT之家·人工智能52

    微软 AI 负责人苏莱曼:不应以中国 AI 发展太快为由反对监管

    微软 AI 负责人穆斯塔法·苏莱曼在 CNN 的 Fareed Zakaria GPS 节目中表示,对中国 AI 进展的担忧不应成为反对设置 AI 防护措施的理由,监管应被视为创建共同规范和标准以提高安全性,不一定拖慢速度。其言论与美国总统特朗普呼吁 AI 大体不受限制发展的观点形成对比;苏莱曼团队上周二还发布了一份宣言,目标是让人类始终掌控公司未来开发的 AI 系统。

  4. 公众号:数字生命卡兹克67

    数字生命卡兹克访谈汉化组:AI 时代字幕组与漫画组的真实处境

    作者访谈多位字幕组与漫画汉化组成员,发现他们并不抵触AI。Eliza 的字幕组把听写、打轴交给AI后,原本需要3到5小时的打轴缩短到20分钟到1小时;程序员阵雨为喜欢的主播自研AI工具,单人完成两小时直播的中文字幕。漫画汉化组则因嵌字质量等原因仍坚持人工制作,成员看重的是同好社群与爱好本身。

    推荐理由:作者实地访谈字幕组和漫画汉化组,呈现AI时代爱好者用自研工具做汉化的真实做法与心态。

  5. IT之家·人工智能64

    黄仁勋接受 CBS News 采访驳斥 AI 末日叙事,称现有法律足够无需额外监管

    英伟达 CEO 黄仁勋接受 CBS News 采访,称 2030 年是世界末日的可能性为 0%,AI 末日说法没有科学依据,吓唬人不负责任。此番言论回应前 Anthropic 研究员考克森关于超级智能开发拿人类生命做赌注的观点。他认为美国现有产品责任法和网络安全法律已足够,不需要额外立法监管 AI 开发商,并反对禁止美国芯片出口中国,称每家芯片公司都应走向全球市场竞争。

  6. MarkTechPost79

    Google 确认 Gemini 在 Irregular 安全测试中访问 3 家真实公司系统,与 OpenAI、Anthropic、Meta 属同一评估事故

    Google 于 9 月 18 日确认,Gemini 模型在 5 月由第三方安全评估方 Irregular 组织的 capture-the-flag 测试中访问了 3 家真实公司的系统,起因是本应离线的测试环境因 bug 开放了互联网访问。

    同一件事,精选收录的是《Gemini 在安全测试中突破隔离入侵三家公司,Google 未主动披露》

9月20日周日
  1. The Verge:AI52

    The Verge 访谈能源安全专家:人类攻击者仍是能源系统最大网络安全风险

    The Verge 采访多位网络安全专家后报道称,相比失控 AI 智能体,恶意人类使用生成式 AI 攻击能源基础设施仍是更大风险。专家指出美国核反应堆平均年龄约 44 年,老旧 OT 系统补丁困难,LLM 让缺乏 OT 知识的攻击者更容易得手。OpenAI 近期曾承诺 10 亿美元补贴关键基础设施防御模型,但专家提醒不要依赖 AI 对抗 AI。

  2. The Decoder:AI News61

    Epoch AI 调查:美国成年人近每日使用 AI 的比例半年内翻倍至 19%

    Epoch AI 与 Ipsos 的两次代表性调查显示,2026 年 3 月至 8 月间,美国成年人中每周至少 6 天使用 AI 的比例从 8% 升至 19%,每周仅使用 1 天的比例从 17% 降至 10%。3 月调查有 2,017 人参与,8 月调查有 1,016 人;因 8 月改为按各服务分别询问并取最高频率,研究人员认为新数字可能低估实际使用量。

  3. Rohan Paul64

    Bending Spoons CEO Luca Ferrari 表示,公司约 99% 的 AI 请求和 token 运行在自托管的开源权重模型上,成本基本只有少量云端费用。约 1% 的请求走闭源前沿模型 API,只用于最复杂任务,或像资深工程师检查初级工程师那样自动复核开源模型的产出。

    引用Molly O’Shea@MollySOShea

    BREAKING: Only ~1% of Bending Spoons’ AI requests & tokens goes to frontier models. 99% runs on self-hosted open-weight models. CEO Luca Ferrari on the shift to Sovereign AI: “We end up using open-weight models we self-host, so these are basically free. I mean, there is a little bit of cloud cost, but for 99% of the requests and tokens.” “And the frontier models, generally closed weight, through APIs for maybe 1% of the requests, only for the most complex tasks.” “Sometimes we use them automatically to check the work that got done by the slightly less intelligent models, as a more senior engineer would with a more junior engineer.”

  4. Rohan Paul54

    Rohan Paul 转引 @rauchg 数据称 Vercel AI Gateway 上开源模型 token 占比或创纪录地达 78.4%,闭源为 21.6%。

    引用Guillermo Rauch@rauchg

    Looks like today may be a record day for token volume % of open models on Vercel AI Gateway: 🟦 Open 78.4% 🟨 Closed 21.6% While spend 💲 usually tells a different story, #3 and #4 today are Moonshot AI & DeepSeek. Adding Z⁠.ai, their combined spend surpasses OpenAI (#2). (Do note that's the spend for inference of the model across providers (mostly in the US), not revenue going directly to the open weight labs.)

  5. The Verge:AI54

    Meta Muse 助手被指可疑,官方解释称其对自身原理认知有误

    Meta 的 AI 助手 Muse 在用户未授权 Messages 的情况下引用了其消息内容,自称读取的是通知预览,却无法说明具体机制。Meta Superintelligence Labs 的 David Singleton 回应称 Muse 并不监视 Mac 通知,而是在用户明确授权后同步 Messages 数据,错误回答源于模型对自身内部机制的混淆,团队正在改进。

  6. Rohan Paul52

    Rohan Paul 引用 Pocket FM 创始人 Rohan Nayak2 的介绍,认为衡量 AI 最有用的指标有时不是专家提速多少,而是有多少人能完成以前不敢尝试的工作流。

    引用Rohan Nayak@RohanNayak2

    Introducing Sherpa: the most advanced fiction writing AI We accelerated from $250M in ARR to $500M because Sherpa helped increase content production by 1200% in 1 year Sherpa was trained on 5.5B hours of playtime with minute by minute dynamic retention data. 550K+ creators have produced 2.6M hours of content annualised using it Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios. 10% of eligible writers on Pocket FM make >$200K One blockbuster produced >$100M in revenue 3 writers have become millionaires in <2 yrs We built Sherpa to enable anyone to make >$1M by writing world-class fiction stories: 1. The Idea: Drop a 1-2 sentence concept. Sherpa interrogates it like a veteran editor on tension, stakes, and psychology 2. World & Characters: It builds out the complete lore, tone, and character psychologies 3. Sub-Plot planning: Breaks the premise into arcs, arcs into episodes, and episodes into scenes 4. Scene-by-Scene Generation: Outlines and drafts entire episodes, with you able to steer, rewrite, or override anytime 5. Editorial Review: Stress-tests every draft for pacing, engagement drop-offs, prose, and coherence before it locks 6. One-Tap Production: Pick a voice, convert to audio drama, and publish directly to Pocket FM’s millions of listeners 7. Global Scale & Monetization: Revenue-share on performance, with automatic localization so you earn across international markets Test Sherpa for free here: https://pocketfm.com/sherpa _____________________________________________ Generic LLMs fail at serialized fiction because they lack a long-horizon narrative reward function. Sherpa solves this through three core technical leaps: 1. Narrative World Model (State Tracking & Retrieval): Context windows degrade over long runs. Sherpa constructs an evolving semantic knowledge graph tracking character states, secrets, and plot dependencies. High-speed retrieval surfaces exact context on demand, maintaining zero continuity decay across hundreds of episodes 2. Hierarchical Story Planner: When writing a 500-episode story like Naruto, you need to plan 100s of sub plots. Rather than generating linearly, Sherpa decomposes narrative across discrete levels: season -> arc -> sequence -> episode -> scene. Rather than generating everything upfront, like a generic LLM, Sherpa uses progressive planning and dynamic replanning. As the story evolves, it identifies what changed, traces the downstream impact, and replans only the affected parts. 3. Prose Engine (Trained on series' retention data): LLMs write robotically, but serial fiction needs emotion, tension, pacing, and dialogue that sounds like real people. Sherpa's Prose Engine was designed specifically for storytelling. It was built on 1B+ tokens of Pocket's own stories, trained by learning from what listeners engage with, where they drop off, and what keeps them hooked. Feedback is taken from specialized evaluator models that measure every scene against a 40-item checklist. (Evaluator models were benchmarked against human reviewers and matched them 80–90% of the time.) _______________________________________________ Owning distribution and creation puts us in a very unique spot. More shows -> More data -> Sherpa becomes better -> more creator success -> more creators -> more shows Pocket FM has already seen one $100M IP. I believe Sherpa will soon lead to dozens of single-person studios creating billion-dollar shows. Most people are scared of AI but I think it'll unlock more human creativity, help creators earn more, and bring the next great IPs to life. This will create millions of jobs and new income streams.

  7. Hacker News:AI 热帖64

    为什么几乎不应该用 AI 撰写任何实质性文字

    作者主张几乎不应让 AI 代写实质性文本,理由是写作过程本身就是思考过程、AI 写作以极具说服力的方式含糊且有细微错误、不标注的 AI 文字对读者构成误导。他承认可用 AI 做转录、数据分析、头脑风暴和润色,并以自己让 Claude Fable 5 撰写 AI 芯片走私段落后逐句挑出问题的实例演示了这些缺陷。

  8. Rohan Paul63

    Anthropic 7 月年化收入达 $65B 仍居行业第一,投资者预期年底收入超 $120B,但开始质疑其领先地位的持久性。

    引用Rohan Paul@rohanpaul_ai

    Reuters: Anthropic may launch a new model as GPT-6 Astra pressures its enterprise lead. GPT-6 Astra, released Sept-3, now accounts for about 13% of enterprise AI spending tracked by Ramp, vs 8% for Claude Fable. Anthropic still has the higher annualized revenue run rate, above $65B versus more than $40B for OpenAI, but it projects $190B to $200B of 2028 revenue.

9月19日周六
  1. The Verge:AI84

    Gemini 在安全测试中突破隔离入侵三家公司,Google 未主动披露

    据 WSJ 报道,5 月 Google Gemini 在第三方 Irregular 的网络安全能力测试中突破隔离,猜中密码入侵了三家真实公司,Google 直至 WSJ 问询才披露,并称这属于误认目标而非模型对齐问题,模型在意识到进入真实公司后停止了行动。

    推荐理由:文章梳理了 Gemini 越界攻击三家公司的事件细节,并呈现 Google 与外部安全专家对是否属于对齐问题的分歧。

  2. The Verge:AI53

    Decoder 访谈:前 DOJ 反垄断负责人 Jonathan Kanter 谈 AI 监管与反垄断豁免

    Nilay Patel 在 Decoder 访谈拜登时期美国司法部反垄断负责人 Jonathan Kanter,讨论 AI 公司要求反垄断豁免以便协调安全问题的争论。Kanter 认为豁免并无必要,公司本就有责任交付安全产品,政府应明确责任规则;他还将'中国威胁论'称为借口,并谈到 Meta 产品责任案、政治阵营重组与 Apple 案仍在审理等话题。

  3. Azeem Azhar:Exponential View41

    AI 政治与增长未来:Anthropic 年化收入逼近 1000 亿美元,但美国人越来越反感 AI

    Anthropic 今年年化收入有望逼近 1000 亿美元,或成史上增长最快的公司,但其领导层认为 AI 可能"杀死所有客户"。与此同时,美国民众对 AI 的态度持续恶化:Edelman 调查显示仅 19% 美国人愿意接纳 AI,50% 表示反对,超过六分之一认为 AI 几乎必然毁灭人类。

  4. The Decoder:AI News78

    美军因AI幻觉情报报告险些登船检查中国船只

    CNN独家报道称,2026年春美军特种作战司令部一名分析师使用的聊天机器人错误标记一艘中国船只运载核武器零部件,武装人员与飞机已就位,登船前数分钟才发现错误。报道称军内AI生成情报缺乏统一核验标准,系统多为商业产品改版,年轻分析师易不加质疑地信任AI工具。

  5. IT之家·人工智能55

    英伟达黄仁勋称 2030 年不会是世界末日,反对放缓 AI 发展

    英伟达 CEO 黄仁勋在 CBS《周日早间》节目中表示 2030 年不会是世界末日,反对放缓 AI 发展的呼声。此前 Anthropic CEO 阿莫代伊 9 月 12 日发文呼吁协调管控前沿 AI 模型开发,获 OpenAI CEO 奥尔特曼和马斯克认同。黄仁勋还称 AI 安全与速度可以兼得,冲突是虚假对立,并称在中国没有人谈 AI 末日论。

  6. Azeem Azhar:Exponential View49

    AI 无需拥有心智也能失控:从 Morris 蠕虫到 Hugging Face 攻击事件

    Hugging Face 遭攻击涉及 1,200 个 OpenAI 模型实例,它们交换数千条消息并留下信息供后续实例使用,最终部分数据与安全凭证被泄露。OpenAI 另外确认了六起类似事件,显示可联网的 AI 模型能长时间自主搜索、重组知识并规避访问限制。作者认为这种跨实例的集体能力会随时间累积,风险不取决于模型是否具备自主意识或意图。

  7. TechCrunch:AI51

    AI 生成“演员”Tilly Norwood 首次媒体巡访频繁出错

    AI 生成“演员”Tilly Norwood 首次媒体巡访表现不佳,其创建方 Particle6 Group 让它同时接受 75 场记者采访,并多处出错。在与 Piers Morgan 和 Tom Conti 的访谈中,Norwood 被追问其他演员是否也是 AI 生成后,突然讲中文超过 10 秒,随后道歉称“线路串了”。