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PLUS: How (and why) to set up Meta’s Muse Desktop Zach Mink Good morning, AI enthusiasts, and welcome to the 5,879 new readers who joined us yesterday. Google spent most of 2026 on the sidelines of the frontier, with a scrapped Gemini 3.5 Pro and a steady stream of Flash models filling a widening gap. Gemini 4 Argon is the company’s long-awaited answer, and the early leaderboards show significant progress. There’s just one catch — you probably can’t use it yet. In today’s AI rundown: Gemini 4 Ar

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家庭的投资全流程——从投研、规划到最终交易。 几十年前，普通散户必须致电经纪人，并为每笔交易支付昂贵佣金；而如今在平台高管描绘的蓝图中，智能体正成为华尔街的“自动驾驶汽车”：让投资者即便在完全移开注意力时，也能自动化参与股票、期权、比特币乃至预测市场合约的交易。 就在上周，嘉信理财、LPL Financial 及摩根大通等机构的股价集体下挫。市场普遍担忧，以Meta旗下Muse为代表的智能体工具，可能会自动把客户账上的闲置现金扫入收益率更高的竞品账户——这直接掐中了传统银行与券商高度依赖低成本现金吸储的盈利“命脉”。 今年早些时候，Robinhood已允许用户将Claude或OpenClaw等第三方AI助手连接到投资账户。其他经纪公司也推出了类似工具。一些精通技术的投资者构建了定制化的代理程序，这些程序就像一支微型资产管理团队，能如同对冲基金的分析师团队一样进行数据分析和市场监控。 散户接受度仍待观察 Robinhood表示，迄今已有超过15万名客户开设了智能体交易账户。但为了控制风险，智能体工具仅能调配这些独立专用账户中的资金，且用户可选择手动批准每笔交易。 Robinhood还表示

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lacement for every pending cluster, then act on it. This is the direction the rest of Modal’s scheduling systems are moving in too. On each iteration, the gang scheduler: Collects metadata for all multi-node workloads in the system; Assigns all workloads to nodes, grouping by AZ and network ID; Spins up any new capacity for workloads which do not currently fit; Sends schedule notifications to all nodes which are alive The gang scheduler pulls from the same capacity pool as everything else on Mod

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least for a while. It turns out that organizing work is just one more thing AI can learn to do. Which brings us to dots and Muse. The number one app in the App Store right now is Meta’s Muse, a personal agent that promises to do work for you. OpenAI has now released a competitor tool, called dots. They aren’t alone: SpaceX’s Grok Bot, Instinct, and Gemini Spark all do similar things, more or less. All of these agents draw inspiration from a phenomenon you might remember from earlier this year,

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该报告的主要作者、贝恩全球科技、媒体和电信业务主席David Crawford写道：“该行业需要的是一场远超移动和云计算所带来的变革的创新浪潮。 人工智能基础设施的建设远远领先于需求曲线，而要为其提供可持续的资金支持，就需要将全球年均GDP增长率提高约1%。 ” 贝恩公司的报告凸显了要使人工智能目前的研发速度可持续发展所面临的障碍。 以微软、谷歌、亚马逊、Meta Platforms和甲骨文为首的公司正在数据中心领域投入数万亿美元，以满足人工智能对计算资源的需求。数据中心的规模和成本大约每12到16个月翻一番，部分原因是英伟达和SK海力士等公司的芯片、网络设备和其他组件的价格飙升。 而这份报告发布之际，围绕人工智能服务提供商尚未实现的收益的争论愈演愈烈。批评人士担忧，技术制造商和人工智能开发商之间日益紧密的相互依存关系，助长了过高的期望，而这些期望反过来又需要更多的资金投入。 贝恩公司表示，虽然目前的讨论主要集中在员工生产力上，但人工智能基础设施的经济效益将需要数万亿美元的新收入，而不仅仅是生产力的提高。 该咨询公司预测，到2030年，数据中心支出将达到5万亿至6.5万亿美元，新增至

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项具有“道德约束力”的AI协议，通过“自我约束和行业互律”来约束各公司 。 特朗普在“真实社交”平台上发布了该协议的副本，这份文件呼吁每家参与公司实施四项不同的措施来保障AI技术的安全，并补充称，未来“将这些步骤编入法律或法规可能是有意义的”。 一些与会高管表示，该协议包含了一系列承诺，涉及检测新模型问题的技术手段、内部风险审查、外部审计以及其它相关措施。 Meta CEO马克·扎克伯格称该协议是“一个开端”，而其他人则暗示一些细节仍需进一步敲定。 Anthropic CEO达里奥·阿莫代伊表示：“关于应对这些风险的具体机制，目前仍在讨论之中”。他表示，在确保美国赢得AI竞赛以及安全地赢得竞赛方面已取得进展，但他同时强调，该技术仍然构成“真实的风险”。 此次会议正值美国近期接连曝出多起令人担忧的AI越界事件之际。据报道，OpenAI和Anthropic以及安全研究人员正在调查数万起AI安全事件。 而在佛罗里达州，州检察长于周一向一名法官提出申请，要求法院禁止 OpenAI 在没有外部监督的情况下开发新的人工智能模型。这项请求是该州起诉 OpenAI、指控其伤害未成年人相关诉讼的一部分。

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说道：“这个名称是由全世界最伟大、最聪明、最杰出的人们正式更改的。” 但这些科技公司高管似乎签署了一份单独的两页文件，特朗普在他的“真相社交”账号上分享了这份文件。该文件并未声明要更改名称，只是在其标题中提到了特朗普对人工智能的新称呼：“白宫超级智能协议”。 该协议副标题为“前沿责任联合承诺”，其中表示签署方“认为每家公司都有责任以安全且能建立信任的方式开发自己的技术”。该协议指出，这包括实施“健全的内部流程和控制，以确保其技术按预期运行”，并建议增加四层控制措施。 根据该文件，签署方同意“定期会面”以制定安全标准，文件还补充说，“随着时间的推移，将这些步骤编纂成法律或法规可能是有意义的”。 除了特朗普之外，上述帖子还显示，签署该协议的还有六位科技公司高管：谷歌CEO桑达尔·皮查伊、特斯拉以及SpaceX首席执行官埃隆·马斯克、Anthropic首席执行官达里奥·阿莫代伊（Dario Amodei）、Meta首席执行官马克·扎克伯格、OpenAI总裁兼联合创始人格雷格·布罗克曼以及英伟达CEO黄仁勋。 当被问及该文件是否具有任何约束力时，特朗普在白宫外表示，该文件“具有道德约束力” 。

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bout 5x Luna per task, because only 48% of its repeated context hits cache versus 99% for Luna ( @ArtificialAnlys ). Agent Research, Inference and Systems Context Language Models (Meta) : CLMs treat context as an editable file rather than an append-only log, with context-management policies learned in the weights and no external harness ( @RulinShao ). Result : They score 65% higher with the same compute on a 24-hour multi-repository agent-swarm task ( @arankomatsuzaki , @natolambert ). Adaptive

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满意。苹果的市盈率目前处于30多倍的高位，这远高于标普500指数的约22倍。 他警告称， 如果周四的苹果财报令人失望，尤其是如果半导体价格上涨挤压利润率，苹果可能会面临风险。今年内存芯片成本飙升，这一趋势已经已迫使整个手机市场提价。 “你不能因为他们没有在AI上花钱，就把世界上所有的钱都投到这一只股票上，”Niles指出，“在这样的估值水平下，这并不合理。” 华尔街机构目前预计，苹果二季度营收将接近1089亿美元，每股收益为1.89美元，高于去年同期的1.57美元。投资者将密切评估其Apple Intelligence能否在不推高资本支出、不侵蚀营业利润率的前提下顺利落地。 周四的财报发布也正值科技巨头密集公布财报的一周，Meta和亚马逊也将在同一周发布财报，同样面临着关于AI支出的类似审视。 值得注意的是，本周四的财报电话会议也将是蒂姆·库克作为苹果CEO主持业绩会议的“告别之秀”。 库克将于9月1日卸任CEO并转任执行董事长，正式将苹果的掌舵权移交给硬件工程资深高管John Ternus。无论如何，库克留给继任者的，不仅是一份创纪录的市值成绩单，更是这一轮AI狂潮中最独特的避风港。

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oop stays the same: curate, mix replay, choose an adaptation depth, bucket by duration, evaluate on independent target and regression sets. What changes per language is the corpus metadata, transcription conventions, normalization, tokenizer coverage, script handling, and capability metrics. For another language, start with a small audited manifest and test the base tokenizer on names, numerals, borrowed words, and mixed-script sentences. Replace the Arabic normalizer with one that preserves mea

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n" image, the model integrates a timeline, a line chart, a bar chart, and a realistic view of the station within a single frame. The information hierarchy is clear, and the visual metaphors are accurate, demonstrating the model's ability to spatially organize panoramic data. Prompt: A visual infographic chronicling scientific research at Antarctica's Qinling Station. Place the main Qinling Station building at the center. Surround it with a timeline of research station development, a bar chart co

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lt is an urgent and growing need for more fast inference across the industry. Cerebras is the market leader in high-speed AI inference, powering models from OpenAI, Cognition, and Meta at up to 3,000 tokens per second. Today's announcement brings that speed to AWS's global customer base. And through our unique collaboration on disaggregated inference, we'll be able to provide 5x more capacity for high-speed inference. Every time you ask AI a question, two distinct kinds of computation happen: pr

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cifically focuses on training foundation models and large language models on newer modalities such as structural biology and joint image-text pairings, and evaluating methods with meta-learning downstream task performance-in-the-loop. Most foundation model research has been in text or image data, and we plan on focusing our work in new multimodal and biological data types as well as evaluating downstream fine-tuned task performance using in-the-loop meta-learning techniques. School of Humanities

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ant-programs/hai-accelerator-for-learning-partnership-grant","children":["HAI与学习加速器合作资助",null]}]}],false,"$undefined","$undefined",false,false,["$","div",null,{"className":"ContentMeta root xqJXg ContentMeta isGrant 91KjO","children":["$undefined",null]}]]}],false,["$","div",null,{"className":"ContentItem imageContainer kCAMl","children":["$undefined","$undefined"]}],["$","div",null,{"className":"ContentItem content 1QYrh","children":[["$","div",null,{"className":"ContentItemDek root P1pHC Conte

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ally designed for mental health purposes, but are commonly used for mental health support Companion chatbots Examples: ChatGPT’s CounselorGPT, Character.ai’s Trauma Therapist, and Meta AI Studio’s My Therapist General-purpose LLMs that assume a particular persona or role, such as that of a therapist, friend, or trusted partner Wellness apps that use AI Examples: Wysa Applications that use AI to provide mental health support and promote emotional well-being more broadly but stop short of making m

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e administration is weighing whether to extend that kind of scrutiny to Chinese open models as well. The conversation has led to an American corporate response; Nvidia, Microsoft, Meta, and 20-plus other companies published an open letter warning Washington against “premature restrictions” on open-weight AI models, highlighting the need for competitiveness in a heated global market. Stanford Institute for Human-Centered AI Denning Director James Landay agrees with the instinct behind that letter

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provement is involved. The idea of an AI kill switch has gained appeal because it offers a seemingly concrete response to an uncertain threat. But the panelists cautioned that the metaphor can obscure more than it clarifies. Yang said a shutdown mechanism may be necessary, but only after answering a basic question: What exactly would it switch off? “AI” can refer to frontier foundation models, networks of autonomous agents or the many smaller systems already embedded in products and institutions

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stead of guessing or hard-coding coordinates, then query Fishing Events within that geometry, format the results with deep links back to the Skylight map, and attribute any vessel metadata drawn from Skylight partners like Global Fishing Watch or TMT. A single question posed to Shippy can hook into several skills at once. "Are there vessels operating near the Cordillera de Coiba MPA?" draws on the Skylight skill for data query, our partner ProtectedSeas’ database for MPA boundary context, and th

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dar, the available polarization channels may matter more. We rely on public STAC catalogs and open standards wherever possible. But a large inference job can generate thousands of metadata queries at once—far more than external services such as ESA’s or Microsoft Planetary Computer’s STAC APIs are designed to handle concurrently. To avoid overwhelming those services, the OlmoEarth Platform maintains its own metadata index, updated as new imagery is published. For datasets hosted through AWS Open

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e. 3 Over the years, METR has worked to gather evidence about these frontier AI agents with access provided 4 by America’s leading AI developers such as OpenAI, Anthropic, Google, Meta, SpaceXAI, and Amazon. The participation of these AI developers is voluntary, 5 and METR is not paid or funded 6 by them. Today, I am here to speak about one particular voluntary engagement, in which OpenAI allowed a handful of my colleagues 7 inside its walls to conduct a brief investigation into how its agents b

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