OpenAI 如何在欧盟规则下进行文本水印处理。了解水印适用的范围、检测是如何工作的，以及为什么访问权限从研究人员开始。
How OpenAI is approaching text watermarking under EU rules. Learn where watermarks apply, how detection works, and why access starts with researchers.
独立研究人员发现了一种代理群，似乎运行在腾讯的基础设施上，并针对阿里巴巴的地图服务“高德地图”。
Independent researchers discovered an agent swarm that seems to be running on Tencent's infrastructure and targeting Alibaba's map service, Amap.
今天，我正在与来自加利福尼亚州的民主党参议员亚当·席夫进行交谈。席夫参与多个对科技和人工智能具有监督权的委员会工作：知识产权、反垄断、隐私和科技——所有内容都在其中。我真的很想问他，我们现在如何对与科技行业相关的事物进行监管……
Today, I’m talking with Sen. Adam Schiff, a Democrat from California. Schiff sits on a number of committees with oversight into tech and AI: intellectual property, antitrust, privacy and technology — it’s all there. I really wanted to ask him about how we might regulate anything related to the tech industry at this moment in […]
参加 TechCrunch Disrupt 2026 演讲比赛的最终五位评委，他们将决定谁将赢得演讲比赛。现在就购买入场券，最多可节省 100 美元，第二张门票可享受五折优惠。
Meet the final five Startup Battlefield judges who'll decide who wins the pitch competition at TechCrunch Disrupt 2026. Get your pass now to save up to $100, and get a second at 50% off.
Aleph Alpha发布了Kolibri，这是一个德英混合的专家模型，拥有780亿个参数，每个标记中有约30亿个参数处于活动状态。超过21%的训练数据是德语。该模型在德国和芬兰的768块B200 GPU上进行训练，权重可自由使用，采用Apache 2.0许可证。文章《Aleph Alpha发布Kolibri，一个开放权重的模型，为欧洲人工智能主权辩护》首先发表在The Decoder上。
Aleph Alpha has released Kolibri, a German-English mixture-of-experts model with 78 billion parameters, about three billion of which are active per token. Over 21 percent of the training data is German. The model was trained on 768 B200 GPUs in Germany and Finland, and the weights are freely available under the Apache 2.0 license. The article Aleph Alpha releases Kolibri, an open-weight model that makes the case for European AI sovereignty appeared first on The Decoder .
完整的TechCrunch Disrupt Stage演讲者名单已经公布，包括Max Hodak、Mark Wahlberg、Benchmark合伙人等。现在立即注册，您的通行证可节省高达100美元，并享受第二张门票5折优惠。
The full TechCrunch Disrupt Stage lineup revealed, featuring Max Hodak, Mark Wahlberg, Benchmark partners, and more. Register now to save up to $100 on your pass and get a second at 50% off.
获得一些额外的存储空间对于你的Mac来说，可能就像使用一个名为RemoveMacAI的新开源命令行工具删除苹果的AI功能一样简单。以前有一个单独的设置开关可以禁用苹果智能，但在macOS 27中这个开关被移除了。现在，这些模型仍然保留在你的磁盘上，这些功能……
Getting some extra storage space on your Mac could be as easy as deleting Apple's AI features with a new open-source command line tool called RemoveMacAI. There used to be a single Settings toggle for disabling Apple Intelligence, but that was removed in macOS 27. Now, the models stay on your disk, and the features […]
挪威希望获得时间来制定针对能够记录旁观者的眼镜的永久性规则。
Norway wants time to write permanent rules for glasses that can record bystanders.
2026年，企业人工智能的问题不再是预测模型是否能优于统计预测——这个争论已经解决了。现在最大的问题是，如何让预测系统根据自己的结论自主行动，而不会偏离业务意图。前沿已经从预测转向了自主决策，而……之间的差距……
In 2026, the question for enterprise AI is no longer whether predictive models can outperform statistical forecasts—that argument is settled. The big question now is how to enable predictive systems to act on their own conclusions without drifting from business intent. The frontier has moved from prediction to autonomous decision making, and the gap between…
OpenAI最新的广告形式将在你的屏幕上展示赞助产品和服务的图片。根据周一的一份声明，OpenAI将在本月晚些时候开始在美国进行测试，这些广告“最初”会在你使用ChatGPT生成图片时出现。该公司最初于2月将广告引入ChatGPT。截至目前，ChatGPT的[…]
OpenAI's latest ad format will put images of sponsored products and services on your screen. The ads, which OpenAI will begin testing in the US later this month, will "initially" appear when you generate images with ChatGPT, according to an announcement on Monday. The company first brought ads to ChatGPT in February. Until now, ChatGPT's […]
乳腺癌是美国女性中最常被诊断的癌症——然而治疗上的差距仍然很大。大多数40岁以上的女性都会跳过推荐的年度筛查。放射科医生在同事更少的情况下需要阅读更多的乳腺X光片。而当诊断结果出现时，用于指导治疗的检测结果可能需要数周才能出来。[…]
Breast cancer is the most commonly diagnosed cancer among American women — yet the gaps in care are wide. A majority of women over age 40 skip the recommended annual screening. Radiologists are reading more mammograms with fewer colleagues. And when a diagnosis arrives, the tests that inform treatment can take weeks to return results. […]
美国的ChatGPT用户很快将在聊天机器人为他们生成图片时看到展示广告。文章《ChatGPT的新广告形式在图片生成加载屏幕中加入产品轮播图》最早发表于The Decoder。
ChatGPT users in the US will soon see display ads while the chatbot generates an image for them. The article ChatGPT's new ad format fills the image generation loading screen with product carousels appeared first on The Decoder .
谁来决定人工智能能做什么？
Who chooses what AI gets to do?
Safeworld正在打造数字人类，以确保机器人不会伤害真正的普通人。
Safeworld is building digital humans to make sure robots don't hurt the real ones.
一个麻省理工学院的专家委员会警告称，人工智能正在侵蚀大学体验中的关键部分：办公时间、学习小组以及该校旗舰的本科生研究项目都在逐渐消失。师生之间的信任正在崩溃，一些教授甚至开始考虑用人工智能代理代替学生作为研究助理。与人工智能起源故事联系最紧密的这所大学，现在正呼吁对高等教育进行全面改革。文章《人工智能正在侵蚀办公时间、学习小组以及师生之间的信任，MIT报告发现》最先出现在The Decoder网站。
An MIT expert committee warns that AI is eroding key parts of the college experience: office hours, study groups, and the school's flagship research program for undergraduates are all fading. Trust between faculty and students is breaking down, and some professors are even considering AI agents instead of students as research assistants. The university most closely tied to AI's origin story is now calling for a complete overhaul of higher education. The article AI is eroding office hours, study groups, and the trust between faculty and students, MIT report finds appeared first on The Decoder .
OpenAI 在 ChatGPT 中引入了一种新的视觉广告格式，并扩展了广告客户的测量工具、归因合作伙伴和品牌适宜性。
OpenAI introduces a new visual ad format in ChatGPT and expands measurement tools, attribution partnerships, and brand suitability for advertisers.
控制论之父诺伯特·维纳曾说过：“每个时代的思维都反映在它的技术中。”在过去的一个世纪里，我们的思维反映在我们的计算机中，包括人工智能行业的人们。谷歌的德米斯·哈萨比斯称大脑“是图灵机的一种生物近似”。埃隆·马斯克则更直白地说，[…]
Norbert Wiener, godfather of cybernetics, once said, "The thought of every age is reflected in its technique." For the past century, our thought has been reflected in our computers, including by those in the AI industry. Google's Demis Hassabis calls the brain "a biological approximation to a Turing machine." Elon Musk puts it more bluntly, […]
整个夏天，我与Springboards的首席执行官进行了交谈。Springboards是一家初创公司，正在打造一款大型语言模型，该模型旨在比主流竞争对手产生更广泛多样的回应。通话刚开始时，他说了一句话，从那时起就一直萦绕在我的脑海中：“我们经常说，我们是一个自憎的AI……”
Over the summer I talked to the CEO of Springboards, a startup building an LLM that’s designed to come up with a wider variety of responses than its mainstream rivals do. At the start of the call, he said something that’s been stuck in my head since: “We often say that we’re a self-loathing AI…
Yossi Matias，谷歌研究院副总裁兼负责人，探讨了人工智能如何开始重塑生物学、基础设施、制造业和科学领域，以及为什么其最大的影响可能来自于与其他领域的交叉融合。跟随我们的《麻省理工科技评论》编辑走进新闻编辑室，获取团队研究的尖锐分析和未公开的见解……
Yossi Matias, Vice President & Head of Google Research, explores how AI is beginning to reshape biology, infrastructure, manufacturing, and science, and why its greatest impact may come when it intersects with other fields. Step inside the newsroom with our MIT Technology Review editors for sharp analysis and unpublished insights from the team that researches…
研究：Qwen3.8 27B 以文字形式进行加法运算的实验。Colin Frasier 在 Bluesky 上分享了他两年前使用 GPT-4o 进行的一项实验，目的是测试它在面对越来越大的数字时，能否“计算总和但以文字形式返回答案”。他分享了这些结果的图表：我确信 GPT-4o 没有作弊使用计算器，尤其是因为它在很多计算中都出错了，但我受到启发，在本地硬件（DGX Spark）上重新运行了这个实验，以在完全受控的环境中探索这一现象。我将他的图片粘贴到一个 Codex Remote 会话（GPT-6 Astra）中，并让它使用 Qwen3.8-27B-Q4_K_M.gguf 运行相同的实验。以下是每种组合进行 30 次尝试的结果，且禁用了推理功能：然后我再次运行了实验，但这次启用了推理功能。由于每对数据的处理时间更长，我并没有为每个组合运行 30 次样本，而是只运行了一次——这导致热力图的视觉效果要差很多，因为每个方块要么是 100%，要么是 0%：它在 169 次尝试中答对了 167 次，由于这些是一次性测试，我确信第二次运行会得到不同的结果。这是包含推理追踪的报告版本：
Research: Qwen3.8 27B addition in words Colin Frasier posted on Bluesky about an experiment he ran over two years ago using GPT-4o to see how well it could "compute the sum but return the answer in words" across increasingly large numbers. Here's the chart he shared of those results: I'm confident GPT-4o didn't cheat and use a calculator, especially since it got so many of the calculations wrong, but I was inspired to run the experiment again on local hardware (a DGX Spark) to explore the effect in a fully controlled environment. I pasted his image into a Codex Remote session (GPT-6 Astra) and had it run the same experiment using Qwen3.8-27B-Q4_K_M.gguf . Here's the result for a run of 30 attempts per combination with reasoning disabled: Then I ran it again with reasoning enabled. This took a lot longer per pair, so instead of running 30 samples per square I ran just one - which results in a much less visually appealing heatmap since each square is either 100% or 0%: It got the right answer in 167 out of 169 attempts, and since these were one-shot I'm confident a second run would produce different results here. Here's a version of the report that includes the reasoning traces from