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今日 64 条
9月15日周二
9月14日周一
  1. Gary Marcus40

    Gary Marcus:特朗普与 AI 的命运之约?

    Gary Marcus 在《经济学人》撰文提出,特朗普最重大的总统决策可能是是否与中国就 AI 达成协议,他主张策略重点不在芯片交易,而在 AI 向善的合作。特朗普与习近平 9 月 24 日通话,AI 已列入议程。文中还提到 AI 股票下跌、公众反 AI 情绪升温,以及 Steve Bannon 转而与 Sanders 联手反对。

  2. a16z News67

    Josh Elman 谈产品管理的核心仍是讲故事:AI 时代从写 spec 转向先建原型

    前 LinkedIn、Twitter 产品负责人 Josh Elman 撰文指出,AI 把开发成本降到极低后,产品开发循环从先写 spec 再构建反转为先快速原型再设计,spec 不再是交付物,但判断成本没有下降,决定做什么才是产品经理的整个工作。

    推荐理由:作者结合 LinkedIn 和 Twitter 的一线产品经历,说明 AI 如何把产品开发从写规格文档改为先做原型再做判断,方法论可直接迁移。

  3. Mustafa Suleyman40

    这是一个非常直白且符合常识的观点:技术的目的是服务人类,加速人类繁荣。 任何无法实现这一目标的技术都是失败的,应当被拒绝。 我们还没有到那一步。但开始为这种可能性做准备是正确的。

    引用Satya Nadella@satyanadella

    Any pursuit of superintelligence has to be grounded in the core principle that if the AI we build is not helping humanity and under human control, it's not worth pursuing. We also need to accelerate and spread the benefits of AI, such that they are diffused broadly across countries, communities, and companies. This requires a frontier ecosystem in which both closed and open-source models can thrive. And for firms, it’s imperative that they retain full control over their unique and tacit knowledge. Every organization should be able to build its own continuous learning loop/hill climbing machine, without becoming dependent on any one model provider, and have the ability to embed its own knowledge into models and weights they control. So, in this context, we welcome the research, focus, and deliberate pacing needed to get alignment right as the design goal. We also welcome ideas like "embedded evaluators" and the broader efforts to develop the mechanisms to make this more than just talk. The key is that this cannot be controlled by a handful of entities, but must have broad representation across the ecosystem, countries, and fields, including academia. This is the approach we are taking: broad access and choice at every layer of the AI stack; enterprise control of learning loops and models; and the “Code of Conduct” that underlies our own first party MAI models that we’ll publish tomorrow for public consultation.

  4. elsewhere articles38

    当具身智能走到十字路口:苏度、蚂蚁灵波、自变量、破壳谈四种一线判断

    苏度科技韩铮、蚂蚁灵波沈宇军、自变量王潜、破壳许华哲在 2026 Inclusion 外滩大会圆桌中,围绕具身智能的数据来源、模型路线与落地场景展开了一场未收敛的路线级分歧讨论。对话聚焦 GPT-6 Astra 的能力边界及其对具身行业的冲击,并探讨高成功率与泛化性、客户持续付费等跨越泡沫的指标。嘉宾还就五年后被高估与低估的领域给出各自判断。

9月13日周日
  1. Demis Hassabis62

    Demis Hassabis 发文表示 Dario Amodei 新文《We Must Pace the Frontier》指出了正确的前进方向,细节仍需完善,但方向对应对这一关键时刻是正确的。他还附上自己此前提出的前沿 AI 行业标准机构提案链接;Dario 原文宣布 Anthropic 将为第三方评估者提供永久员工级系统访问权限。

    引用Dario Amodei@DarioAmodei

    We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: https://darioamodei.com/post/we-must-pace-the-frontier

  2. Peter McCrory69

    Dario Amodei 发表文章《We Must Pace the Frontier》,主张 AI 行业应放慢速度并给出三部分计划,Anthropic 单方面承诺执行其中第一步。该步骤是向第三方评估者提供永久的员工级系统访问权限,用于核验安全措施落实、报告事故并评估训练中模型的对齐情况。全文见 https://darioamodei.com/post/we-must-pace-the-frontier,作者 McCrory 认为嵌入式评估者是合理的第一步。

    引用Dario Amodei@DarioAmodei

    We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: https://darioamodei.com/post/we-must-pace-the-frontier

    推荐理由:Anthropic 首席经济学家推荐 Dario Amodei 新文,提出给第三方评估者永久员工级访问权以核验安全措施,可了解行业自律的具体动作。

  3. Aidan Gomez47

    卡特尔这边有些“好主意”: - 你们得给我们员工级别的权限,访问你们整个运营体系 - 如果我们觉得你们不够“安全”,抱歉,为了“安全”我们得把你们关停 - 中国不会遵守,但其他所有人都得遵守!不然就没芯片! 真是绝了。

    引用Sam Altman@sama

    I agree with Dario that we need to pace the frontier. This has been a primary topic of discussions we've had at OpenAI in recent weeks. Committing to having independent evaluators with employee-like access is a great idea, and we will do the same. We'll have more to share soon.

9月12日周六
  1. Peter McCrory46

    这是该模型的一个重要局限。我们聚焦于 AI 转型的供给侧(AI 能做什么、扩散多快、劳动者转岗多快)。 价格是灵活的,总需求等于经济体的产出能力。 更多思考见 🧵

    引用modest proposal@modestproposal1

    Anthropic's economic scenario analysis is interesting. But this is not something you can ignore, this is the most important consideration! "the model cannot generate the negative feedback in which disruption depresses demand and amplifies its own labor-market consequences"

  2. Dwarkesh Patel56

    Dwarkesh 对谈 John Schulman、Beren Millidge 与 Charlie O'Neill:递归自我改进还有多远

    Dwarkesh Patel 与 Zyphra CTO Beren Millidge、Thinking Machines 首席科学家 John Schulman、Baseten 模型训练负责人 Charlie O'Neill 长篇对谈,逐段讨论递归自我改进(RSI)最可能失败的技术原因、中国实验室的追赶路径、自动化 AI 研究者的训练方式以及长时程 RL 能否带来 AGI。

9月11日周五
  1. Nathan Lambert: Interconnects61

    Nathan Lambert 整理开源 AI 与开放模型必读书单

    Interconnects 作者 Nathan Lambert 公布一份开放模型研究书单,覆盖开放模型战略、中美竞争、技术细节与蒸馏争议等主题,并承诺持续更新。书单收录 Mark Zuckerberg、Irene Solaiman、SemiAnalysis、Kevin Xu 等人的文章与论文,并指出当前开源与闭源模型的差距约为 4-6 个月,2024 年前后领先的开放模型均来自中国实验室。

  2. a16z News32

    a16z:雇主医保市场迎来替代性健康计划(AHP)创业窗口

    a16z 指出,随着保费每年上涨 10% 以上,多数雇主开始寻找替代方案,或转向低成本健康计划,或彻底放弃传统医疗保险。AI 正在降低搭建和运营健康计划的固定成本门槛,催生一批 AI 原生且抗 AI 冲击的挑战者健康计划、PBM 和基础设施平台。这一覆盖 150M+ 美国人、规模达 $1T 的商业医保市场,正出现数十年来首次代际替换机会。

9月10日周四
  1. jietang24

    你确定吗?找到最优模型规模很棘手:数据量、激活参数量、环境数量,以及目标推理成本。模型性能还取决于许多其他因素,每个因素都会带来自身的变数。

    引用Charlie O'Neill@oneill_c

    Fable is probably ~2-2.5T parameters, not 10T. Kimi K3 is 2.8T params, trained on maybe 20–30k Blackwell-equivalents. It lands within spitting distance of Fable 5 in terms of capabilities (5, not 5.1). Anthropic has far more compute than Moonshot, better rl environments, better architecture and better optimizers and all of that adds to capability per parameter. So if Fable is only slightly ahead of K3 with this in mind, it's almost certainly a smaller model. GPT-5.5 and 5.6 are smaller still (I'll say more on that later)

9月9日周三
  1. Nathan Lambert: Interconnects41

    普通人何时才能感受到 AI 的影响?

    Nathan Lambert 认为 AI 对普通人日常生活的直接触达仍然边缘化,家庭、饮食、交通和娱乐等核心领域几乎感受不到影响,AI 目前主要是服务精英的知识工作工具。他警告若 AI 只带动半个社会,可能重演英国 1790 至 1840 年工资停滞而人均 GDP 快速扩张的"恩格斯停顿",进而拖累 AI 发展。

  2. Eric70

    OpenAI 宣布给出纳维-斯托克斯千禧年大奖难题的一个解,证明由一组智能体使用比 GPT-6 Astra 能力更强的 OpenAI 下一代模型产出。该问题关注纳维-斯托克斯方程描述的光滑三维流体运动是否会崩溃,约 90 年来未获解决。转发作者以在 OpenAI 工作的口吻感叹又是一个疯狂的日子。

    引用OpenAI@OpenAI

    We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.

    推荐理由:OpenAI 官方宣布用下一代模型的智能体群产出纳维-斯托克斯千年问题证明,读者可以关注智能体做数学研究的这一路径。

9月8日周二
9月7日周一
9月6日周日
9月4日周五
9月3日周四
  1. elsewhere articles46

    对卷卷的3小时访谈:从抖音到AI 3D、成为制造业OS的野心、基础模型不会吞噬一切

    数美万物创始人兼CEO任利锋(卷卷)在近3小时访谈中回顾了从0到1孵化抖音的经历,并介绍了公司最新发布的Hi3D 3.0 2048³模型。他将公司目标从Maker OS推向制造业OS,认为基础模型不会吞噬一切,实体制造仍需能产出生产级3D资产的模型,难点在于拆件、连接结构、材料设备适配与按时交付。

9月2日周三
9月1日周二
8月31日周一
  1. Jensen Huang46

    黄仁勋称 AI 正把制造业带回美国、推动再工业化,并带动老化电网与可持续能源投资,由市场力量而非补贴驱动。他表示 AI 正在能源厂、芯片厂和数据中心创造建筑与制造岗位,过去六个月 AI 初创公司获投 4000 亿美元。他呼吁建设者与社区合作、赢得信任并创造本地收益。

    引用Gavin Baker@GavinSBaker

    Regret the tone of my post on data centers yesterday. What I should have said: There were reasonable concerns about data centers 18ish months ago: water, taxes, jobs, electricity prices, the environment and what they would do to small towns. Well-structured data center projects have largely addressed these concerns today and we should be celebrating this. On balance, data centers are awesome for America in every way. On water: U.S. data centers use a fraction of what golf courses use. A lot of the numbers from 18 months ago were off by over 1000x. Newer data centers use closed-loop systems or recycled water. Should be required by every town approving a data center project. On taxes: looking only at sales-tax exemptions, as Ronan Farrow did, is the wrong way to evaluate this. Data centers pay significant property taxes. Loudoun County, which is the wealthiest county in America, now collects on the order of $1 billion a year from data centers. In Quincy, WA, data centers are more than half the property-tax roll. Over time, property taxes can go to zero while government spending increases in these towns. On jobs: this has been unambiguously awesome for blue collar Americans. Demand for electricians, plumbers, welders, HVAC techs, and contractors has gone vertical, and it is not a one-time construction job. These buildings get upgraded and expanded over time. That is why the building trades are fighting for them, and why some unions are now treating opposition to data centers as a reason not to endorse politicians. On power: the original fear was that households would pay for the incremental electricity demand in the form of higher prices. That is why the ratepayer-protection deals and the new large-load tariffs exist. The right structure is: the data center brings or pays for new generation and signs a contract long enough that existing customers are protected. Where that is happening, utilities are cutting or freezing residential rates and saying so on the record. Where it is not, people are right to object. Electricity prices are going down *today* in a number of large states because of data centers. 
On the environment: data centers overwhelming use natural gas today, which is the cleanest power source outside of nuclear, solar and wind. And the companies that are building the data centers are committed to carbon neutrality such that an equivalent amount of solar will likely be built. Maybe more importantly, the data centers need batteries to function effectively and these batteries can also sell energy back into the grid (which recently prevented blackouts in Texas). Over time, data centers will run on solar plus batteries. On the towns: Poverty in Quincy, WA fell from 29% to 6%. Data center taxes paid for a new high school, a hospital, a library, police and fire stations. This is happening in many left for dead former mill and farm towns that had no other bidder for the land. Data centers are actually reindustrializing parts of America and creating the kind of working-class jobs both parties have spent decades claiming to support. That should not be a partisan issue. Data centers can and should be awesome for America and they increasingly, overwhelmingly are. Supporting the outsourcing of data centers to China will likely age just as well as support for the outsourcing of high quality, blue collar manufacturing jobs to China has aged. When the facts change, I change my mind. I hope that reasonable people who had good faith reasons to oppose data centers at least consider updating their beliefs given the change in the facts over the last 18 months. This really matters for America. I will say I also think the idea of making data centers beautiful is a good one that has yet to be implemented. Data centers should be just as beautiful as Grand Central Station. We can learn a lot from the railroad buildout. Neoclassical revival ftw. Might write up open-weight AI tomorrow as this is equally essential to America.