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月14日 YouGov 一项民调显示，67%的美国人认为AI发展得太快。而且这也不是一个特别带有民主党色彩的问题：选举专家 内特·西尔弗看看民调，最近 写道 “我们并不常看到这种两党共识。” 美国正处于一个奇怪的境地。AI不断做出令人担忧的举动，这或许表明应该对AI进行一些监管。人们希望监管AI。三大AI实验室（Anthropic、OpenAI 以及 谷歌 DeepMind）都说他们想要AI监管。但科技界几位富有且有影响力的人士并不这么想。而且，至少在这个月，那才是真正重要的意见！ 既然这场国宴即将举行，我不禁想到一个 大胆声明 萨姆·奥特曼上周在他的AI安全新闻巡讲中说过：“我认为，如果特朗普总统和习近平主席能在一件本应容易达成一致的事情上达成一致，他们会一起获得诺贝尔和平奖，那将非常美好。”也许这件事终究没那么简单。 人们是这样说的： 塞思·森特他于2023年至2025年负责国务院的人工智能外交工作，曾撰文谈及美中人工智能讨论 在 《纽约时报》 今天。 森特描述了2024年关于人工智能的会谈中，中国官员对人工智能安全合作并不太感兴趣。“中国官员在数小时里一直把会谈引离人工智能系统的

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bor restructuring, and scientific discovery Anu Madgavkar and Daniel Rock will lead Google's AI & Economy Research Program alongside Alex Imas, Director of AGI Economics at Google DeepMind, and Zanna Iscenko, AI & Economy Lead, in Google's Chief Economist's Office. Shaping future research and collective impact Maximizing AI’s economic opportunity while mitigating disruption requires sustained partnership. This expanded team of experts will serve as a scientific bridge, directly informing future

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he one thing it requires: everyone. In today’s AI rundown: Zuckerberg breaks ranks on the AI slowdown Nate's Notebook: Same-hole thinking A beginner’s guide to ChatGPT Work Google DeepMind starts an AGI think tank LATEST DEVELOPMENTS Image source: Mark Zuckerberg (@finkd on X) / The Rundown The Rundown: Meta CEO Mark Zuckerberg just pushed back on the coordinated AI slowdown, arguing each lab already has its own “responsibility and incentive” for pacing itself safely, in a seemingly veiled shot

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仿。 “当全球都在探讨如何把控前沿技术的演进步调时，我们必须竭尽所能，消除顶尖实验室与公众之间的信息鸿沟，”Anthropic在博文中强调，“这意味着我们需要更精准地量化AI发展进程、向社会公开透明披露，并让公众共同参与决定如何应对这些信息。” 阿莫德伊上周末提出的放缓AI研发呼吁，得到了包括OpenAI首席执行官奥尔特曼、SpaceX首席执行官马斯克和谷歌DeepMind联合创始人德米斯·哈萨比斯在内的行业领袖的支持，此前研究人员曾就人工智能日益增长的危害潜力发出严厉警告。 阿莫德伊表示，他的计划旨在控制模型能力提升的速度，同时“不牺牲商业优势或美国在人工智能领域的领先地位”。 Claude已主导26%内部AI研发工作 根据最新的披露，在第一项指标“AI参与AI研发”方面，Anthropic构建了一个原型指数——Anthropic研发自动化指数（Anthropic R&D Automation Index），用于衡量Anthropic的AI研发工作中有多少是由Claude完成的。 其构建方式是：对公司内部开展的各类AI研发工作进行编目，评估每项任务当前的自动化程度，最后将这些评分汇总

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oss all target languages, preserving strict error hierarchies and streaming behavior, and integrating the generator directly into our internal monorepo and build system. At Google DeepMind, while we use AI across our development workflows, we believe in choosing the right tool for each layer of the stack. Transforming formal API specifications into multi-language SDKs demands determinism and strict type safety. With Speakeasy, we pair a fast, deterministic generator at the core with Antigravity

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文本识别，即通过分析字符出现的概率规律提高识别正确率，但面对更加复杂、严重损毁或书写风格差异大的文本，统计方法仍难以有效处理。 2000年后，文献解读技术迎来质的飞跃。随着深度学习技术在古文字研究中的广泛运用，研究人员不再需要手动设计文本特征，而是通过训练深度学习模型，使其自动从图像中提取文字形态特征，并结合上下文完成语境判断与归属分析。2021年，谷歌旗下DeepMind AI研究实验室与英国牛津大学等多家研究机构合作开发了“伊萨卡”（Ithaca）模型。该模型基于变换器（transformer）架构，能够同时完成残缺文本修复、年代预测与地理归属判断等任务，这是该技术进入古文字研究的里程碑式探索。2022年，该研究成果刊发在《自然》（Nature）上，文章进一步探索了史学家与人工智能合作的潜力，按“伊萨卡”模型独自修复文本，其准确率为62%，但若有史学家参与，在智能工具协助下辨认准确率可以达到72%，其效果远高于史学家与模型的各自单独识别。此外，“伊萨卡”模型提出的关于书写年代和地点的推论，也为历史研究提供了新视角，如该系统预测，其中一组法令的写作时间比传统观点晚约30年，从而引起史

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名研究员声称，如果不加以控制，人工智能可能会在 2020 年代末毁灭所有人类。 这些说法引发的热议，近日促使外界对人工智能开发加强审视。作为回应，Anthropic 首席执行官达里奥·阿莫代伊（Dario Amodei）呼吁所有人工智能开发放缓步伐，并敦促各国政府对该行业加以监管。 End of 热读 这篇文章获得了阿尔特曼的赞赏，谷歌（Google）旗下 DeepMind 联合创始人德米斯·哈萨比斯（Demis Hassabis），以及社交媒体平台 X 和人工智能助手 Grok 的所有者埃隆·马斯克（Elon Musk）也对此表示赞赏。 到周二为止，越来越多人工智能行业领袖表态支持行业自我监管，而非由政府介入。 阿尔特曼表示，他认为像自己公司这样的人工智能企业，基本上有能力进行自我监管。 阿尔特曼说：“我们会把这件事做好，我对我们公司和整个行业有能力安全地做到这一点非常有信心。”他还表示，他确信这些公司会“让对齐与安全远远领先于能力发展”，如果做不到，他们就会“放慢速度或停下来”。 图像来源， Reuters 在阿尔特曼发言之后，Meta 首席执行官扎克伯格（Mark Zuckerb

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me images and reason over them, and models that do that well are inherently large. Boston Dynamics would not give an exact parameter count, pointing instead to Gemini Robotics ER, DeepMind’s embodied reasoning model built on Gemini 3.5 Flash, as the right mental picture. Google publishes no parameter counts, but we estimate that System 2 could be somewhere in the hundreds of billions of parameters if not 1T. A model that size will not fit on a robot, which is why Boston Dynamics splits the stack

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nAI CEO Sam Altman said that embedding third-party evaluators is a solid step. SpaceX and Tesla founder Elon Musk echoed that oversight is long overdue. Demis Hassabis from Google DeepMind called the essay a step in the right direction. Microsoft CEO Satya Nadella emphasized that pursuing AI without keeping human benefit at the center isn’t worth doing. Tech investor Gavin Baker posted a great write-up below summarizing these industry responses Will Chinese labs follow the suit? Some social medi

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ntral to Andon’s business proposition: providing AI developers with evaluations grounded in situations that arose outside the lab. The company says it works with Anthropic, Google DeepMind, OpenAI, and SpaceXAI on research and evaluations. Andon’s physical businesses themselves remain decidedly less successful. When Andon Market’s Carson spoke with IEEE Spectrum , he was about an hour into his shift. Two customers had come in. Neither bought anything, although both left with free pins and sticke

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s considering a $5B loan to AI cloud startup Fluidstack to strengthen U.S. manufacturing and supply chains for data-center components, WSJ reported. At the BRICS Summit in New Delhi, China proposed an open-source AI community to support shared AI development, training, and industrial use across member nations. Anthropic and Google DeepMind’s AI safety researchers Joe Benton and Josh Engels resigned to join METR Evals, with both warning that AI capabilities are outpacing safety. Sponsored Listing

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ompared to coding the experiment. Whenever we talk about research, they propose a bunch of miscellaneous things which are very, very tiny steps. Charlie O’Neill Or even going from DeepMind’s approach of, “We’re going to solve intelligence by learning to play games at a superhuman level,” to one random researcher like Radford being, “I’m going to try and just predict the next token of a very wide swath of data”… Even once Radford had discovered that, it took a while before people decided to scale

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ee a scenario with substantial growth and modestly higher unemployment, or crazy growth and crazy unemployment. What people are saying: LSE economics professor Ben Moll and Google DeepMind’s director of AGI economics Alex Imas wrote an essay on Substack dismissing the predictions of double-digit GDP growth that Amodei and others have made. The economists think that such extreme forecasts would assume too much: Double-digit growth rests on several shaky assumptions, they write, including“machines

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h, S., Bartoldson, B. R., et al. (2025). Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach. arXiv:2502.05171. https://arxiv.org/abs/2502.05171; Google DeepMind (2026). DiffusionGemma. https://deepmind.google/models/gemma/diffusiongemma/; Gould, D., Ward, F. R., Woodruff, A. C., Arike, R., Hills, J., Serrano, A., et al. (2026). Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models. arXiv:2606.07157. https://arxiv.org/abs/2606.07157; Gu, A.,

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o which he contributes. Looking at Tibo’s career since, it seems like a well-timed lesson! 2. Google had a “ChatGPT-like” project a year before OpenAI. At the start of the decade, DeepMind was largely focused on games and reinforcement learning, but some members, including Tibo, reckoned that scaling language models might lead to artificial general intelligence (AGI). Their “chat with an LLM” project spread like wildfire internally, but for some reason the product was never launched. Who knows w

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I is reshaping the industry fast. Discover insights from 300 senior leaders on intelligent portfolio orchestration and what you need to succeed. Read Tempo's report today . Google DeepMind launched AlphaGenome Atlas, a free, searchable map generated by its AlphaGenome AI that predicts what all 9B possible one-letter DNA mutations do. Devin maker Cognition announced a new $2B funding round at a $48B valuation, with the company’s annualized revenue nearly doubling to $900M since May. Inception Lab

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scrutiny or as much debate about what their performance and economics would look like outside the company that designed them. Anthropic being the biggest user of TPUs, surpassing Deepmind’s own use by 2029. Google’s internal success was never the question. The question was how much of that advantage the rest of the industry could actually get. Could you take an open-weight model, serve it through a familiar inference engine, and beat NVIDIA on the economics that matter to your business? Today,

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AI commerce “default legal, not default illegal,” though it wasn’t clear what he thought was illegal that shouldn’t be. Safety testing for models came up mostly in passing. Google DeepMind’s Demis Hassabis said the opportunity had to be pursued “in a responsible way,” stopping short of detailing the industry-funded testing body he has pushed for all year. Sam Altman called the administration’s voluntary pre-release review of OpenAI’s forthcoming Astra model a “productive process.” There was litt

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can act on what really matters for your organization. With Gartner AI insights, you can: Access 4,000+ proven AI use cases Get objective, analyst-led guidance The Rundown: Google DeepMind introduced WeatherNext 3, its new weather model that uses live satellite images to refresh its forecasts every hour, taking the top spot on scoreboards against both rival models and the U.S. and European weather services. The details: WeatherNext 3’s improvement is about 5x in detail for temperature down to 5

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g advantage. Before we explore the strategies companies use to generate real-world training data, it’s worth asking why we need real-world data at all. Nearly a decade ago, Google DeepMind trained an AI to play Go entirely by self-play. After playing millions of games against itself, the model became better at Go than the top humans. Could we do something similar for robots? Instead of training physical robots in the real world, maybe we could have virtual robots “teach themselves” to perform ta

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