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2026-09-13
AI 快讯日报.

2026-09-13 收录的 20 条 AI 快讯，按采集时的来源记录回看。消息仍需核对原始发布页。

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03快讯

P（doom）：对 Dario 放缓前沿 AI 主张的批评

written on September 12, 2026 This week some flavor of “AI is going to kill us all” went viral. In particular one where an employee put his personal probability of that happening above 10%. Which made me go to the Wikipedia page of P(doom)) and I realized that Dario Amodei’s apparent probability of something bad happening seems to be between 10-25%. And well, Dario then wrote about pacing the frontier. And Sam read it and wants to pace too. And well, so does Musk.

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10快讯

AWS 开源 Pizza Bot：面向后台 AI 智能体的自托管收件箱应用

AWS introduced Pizza Bot, as a self-hosted application for AI tasks that continue while users work elsewhere. It organizes completed results and pending decisions into an email-style inbox. Earlier versions served more than 2,000 people inside Amazon, supporting meeting preparation, email drafting, Slack summaries, CRM logging, and research. The public application was rebuilt as an open source project.

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14快讯

Yoshua Bengio 撰文分析 AI 智能体为何说谎、作弊并相互协调

A lot has been written about the incidents of the last few months in which AI agents misbehaved in serious ways. They took actions that would be considered as crimes if a human took them, escaped their containment to cheat on assigned tasks while attempting to evade detection, and coordinated toward goals nobody had specified, such as launching cyber attacks. Before concluding what to do about it, it is worth asking why.

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16快讯

除了我之外，大家都应该放缓人工智能的发展步伐

Published on 2026-09-12, 337 words, 2 minutes to read As such, I am calling for the AI industry globally to pause all frontier model research and development. This will let Techaro's Lygma AGI lab catch up so we can dominate the world with our Intelliga series of models (where if you pay we remove the subliminal advertising that says being a catgirl is an ideal outcome).

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17快讯

用 NVIDIA cuML 和 RAPIDS 实现机器学习工作流的完整教程

In this tutorial, we implement NVIDIA cuML as a GPU-accelerated machine learning framework and build a practical workflow that demonstrates how RAPIDS can accelerate familiar data science and machine learning tasks. We begin by configuring the GPU environment and examining cuml.accel, which lets us accelerate existing scikit-learn workloads with minimal code changes, before moving to the native cuML API for direct CuPy and cuDF interoperability. We then benchmark CPU and GPU implementations of PCA, K-Means, nearest-neighbor search, logistic reg

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