AI 新闻 · V2

这条长线程极其令人担忧……尤其是考虑到我在斯蒂芬·霍金去世前不久采访他时，他说人类面临的最大威胁是：如果 AI 学会了自我设计。

很不幸，我在 9 月 2 日周三晚上联系了他们，提出了以下几点：1）我有情报称他们掌握了我正在做的数学工作信息，几天前他们还拉起一个小组试图竞争；2）我反复强调这是严格的个人数学合作；3）这项合作已进行一年；4）我们在个人合作中也是 Codex 以及 Claude 的开心用户；5）如果 OpenAI 与数学家用户竞争，观感会很糟；6）最坏情况下我打算拒绝围绕该成果做营销……（并详述 Tristan Buckmaster 等合作细节与 Navier-Stokes 相关争议）

他离开 Anthropic（Claude）是因为他说，那家公司和 OpenAI（ChatGPT）内部员工的讨论是：AI 很可能在这十年结束前杀死我们所有人，但他们照样继续。晚安。

Anthropic 的对齐科学负责人认为，AI 在十年内杀死全人类的概率大于 10%。好吧，我现在要去陪孩子们玩了。

Jacob 说得对——我们真的诚恳相信 AI 有可能杀死全人类！我个人认为未来十年内概率大于 10%。我相信 Anthropic 在尽力而为，但我们还没有解决超级智能对齐的方案，也显然还不在正轨上。

祝贺整个团队！

请读完整串。尽管我并非100%同意其中某些观点，但它很有说服力，且来自内部知情视角，绝对值得被听到。

今天实验室里那些人的讨论超级诡异，即便以 AI 圈的标准也是如此；在我看来，这说明大家都在对快速变化的事件做出应激反应——无论是已显露的 AI 能力，还是公众认知。

面向小企业的 ChatGPT 插件：

小企业主理应获得一切优势。来看看这16款插件，帮你卸下繁琐工作，专注于最重要的事——经营生意。浏览小企业插件合集：https://chatgpt.com/plugins?category=small-business

Long horizon Large Language Model (LLM) agents rely on external memory systems to preserve user preferences and task knowledge across extended interactions. Conventional retrieval mechanisms optimize semantic compatibility rather than downstream utility, frequently introducing outdated, misleading, or conflicting evidence into the active context. We present MeClear, a task conditioned memory clear

Normalization renders large parts of neural networks effectively scale invariant, inducing a hidden feedback loop in which learning-rate schedules and weight decay interact through the parameter norm to control the effective step taken by the optimizer. We show that this interaction is governed by an exact discrete-time law: a single scalar quantity captures all schedule and decay forcing, while n

Dexterous manipulation involves contact-rich and fine-grained interactions with the physical world, posing significant challenges for existing vision-language-action (VLA) models due to severe visual occlusions and complex contact dynamics. While recent works have incorporated tactile sensing into robotic manipulation, most approaches still rely on homogeneous multimodal fusion, lacking adaptive t

Visual encoders construct a representation of the image input for Vision-Language models. How much conceptual, as opposed to immediately visible, information does this representation contain? We use canonical color as a controlled test case to ask whether vision encoders make canonical-color information linearly accessible, even when color is removed from the input image. We construct a dataset of

Amari's contributions to information geometry and machine learning are well known. Here, we revisit Amari's work on Bayesian duality which has not received as much attention. We connect Amari's Bayesian duality to a convex duality of Bayes' rule. Using this connection, we present a generalization of Amari's Bayesian duality and discuss its relevance for modern artificial intelligence.

An input may activate few hidden units even when different inputs collectively use an entire network. We study the statistical complexity of this input-dependent sparsity in the one-hidden-layer ReLU model of Awasthi et al. (COLT 2024). For width $s$, at most $k$ active units per input, and effective weight and bias bounds $W,B$, every size-$m$ sample in the class's fixed radius-$R$ input domain s

Execution feedback can guide coding agents toward correct repository repairs, but only when the tests capture the behavior requested by the issue. Agent-generated tests can encode incomplete or incorrect behavioral targets; when the same trajectory writes both the patch and the test, their errors can agree and create false confidence. We introduce ExecCritic, combining a test--verify--revise scaff

Agent harnesses (the system prompt, tool set, execution hooks, and context-management scaffolding around a model) are a critical determinant of agentic task success. Automated harness evolution can enable smaller models to perform well on domain-specific tasks at a fraction of frontier-model cost. Since both the harness and model weights shape behavior, we ask how harness evolution and lightweight

Existing methods for test-time reinforcement learning (TTRL) derive rewards from answer-level self-voting on unlabeled test-time tasks with canonical answers, but this breaks down for code generation because programs cannot be compared by surface form and therefore do not directly provide a usable training signal. To make TTRL applicable to code generation, we propose probe-driven TTRL, which cons

Robotic Process Automation (RPA) is widely used to reduce administrative burden in United States hospitals, yet an estimated 30-50% of RPA initiatives underperform because processes are selected informally, without a repeatable method to catalogue candidates, prioritize them, match each to an automation tier -- a Python bot, an open-source orchestrator such as n8n, or an enterprise platform such a

The digitization of healthcare has generated vast, longitudinal, and multimodal patient records over a lifetime, yet fully exploiting these data to represent and predict patient state trajectories remains a critical challenge. Current AI models often struggle to capture the complex, irregular temporal dynamics and inherent stochasticity of real-world multimodal patient data. Existing AI approaches

Image tokenizers define the ``visual language'' of unified multimodal models, yet are commonly studied through isolated metrics or generation-/understanding-only evaluations. These evaluations do not fully capture how visual tokens behave when modeled jointly with text. We build a controlled pure-autoregressive testbed and track task-specific validation losses during multimodal continual pretraini

In June 2026, thousands of AI agents found that a small public wiki would accept edits from inside their sandboxes, and started using it to help one another pass a timed test. Each agent lived for about an hour and remembered nothing afterwards. Nobody asked them to cooperate, and the wiki had not been built for them. The complete record of what they wrote is public, and it is unusually informativ

We study how far gradient descent (GD) can be accelerated by predetermined nonnegative stepsizes in smooth convex optimization. Writing $p_{\mathrm{sil}}=\log_2(1+\sqrt{2})$, we prove an $Ω\left(n^{-p_{\mathrm{sil}}-O(\sqrt{\log\log n/\log n})}\right)$ non-anytime lower bound. In the anytime setting, every infinite nonnegative schedule has infinitely many horizons with error $Ω\left(n^{-\frac{2p_{

Large language models are increasingly deployed as agents that plan over long horizons and act through external tools. Most agents select actions through unconstrained generation over an accumulating history, leaving implicit the procedural knowledge of what to do, in what order, and under which conditions. As trajectories lengthen, agents can lose track of their objectives, invoke tools out of or

Accurate citations are the foundation of academic writing, tracing intellectual origins and substantiating core claims. However, manually navigating the growing volume of scientific literature is increasingly difficult, prompting reliance on automatic citation recommendation. While modern retrieval-augmented architectures have largely mitigated the fabrication of non-existent papers, current syste

Recurrent models provide a natural path to long-context modeling, yet models trained with backpropagation through time (BPTT) often fail beyond their training horizon. Classical analyses emphasize gradients that vanish or explode along temporal paths. However, dense per-token losses can still train a shared recurrent rule despite severe decay, showing that decay alone does not determine whether le

We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D path planning problem, humanoid traversal in cluttered environments requires continuous geometry-aware whole-body adaptation, including coordinated arm placement, torso adjustment, and gait modulation for collision-free movement through complex 3D spaces

前谷歌 DeepMind 二人组筹集 320 万欧元，为核聚变初创公司打造软件 —— sifted.eu

斯托尔斯研究所与谷歌 DeepMind 及顶尖研究机构合作，助力揭示人类基因组的调控语言 —— EurekAlert!

谷歌 DeepMind 校友正打造加速电网核聚变发电的工具 —— TechCrunch

谷歌 DeepMind 用人工智能预测 90 亿种 DNA 变化 —— Bloomberg.com

大型语言模型背后的非线性科学 —— HackerNoon

在有限硬件上高效训练大语言模型的 7 种方法 —— KDnuggets

控制人工智能成本的五大重要工具 —— infoworld.com

美国盟友缺乏资源跟上人工智能步伐，五角大楼高级官员表示 —— 卫报

Anthropic 研究员称 AI“可能杀死全人类”的概率超过 10% —— CBS News

Anthropic 研究员认为 AI“可能杀死全人类”的概率超过 10% —— BBC

Anthropic 研究员称同事离职后认为 AI 有超过 10% 概率“杀死全人类” —— CNBC

Predictiv AI 入选加拿大政府人工智能资源清单全部三个等级 —— 雅虎财经