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s popular than ICE. Yet the number of people using AI is still skyrocketing. ChatGPT hit a billion monthly users in May, according to the market analysis firm Sensor Tower. Google DeepMind’s Gemini is close behind, logging 950 million users in July. According to Pew, half of US adults now say they use a chatbot , more than twice the number who said they did in 2023. One in four people say they do so every day. And it’s a global habit: More than a third of adults across all 38 OECD countries (a g

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日益增多，而世界两个人工智能大国在过去两年内未曾围绕这一对当今时代具有深远影响的技术举行正式对话，备受世界关注。 如果中美两国希望此次重启对话取得成功，就需要迅速采取行动，秉持清晰的目标推进对话。 令人鼓舞的是，两国开始至少在一个问题上趋于一致，即人工智能安全的重要性。两国的前沿人工智能实验室的负责人近期更加公开地谈论如何为通用人工智能时代的到来做准备。谷歌DeepMind联合创始人、时任首席执行官德米斯·哈萨比斯（Demis Hassabis）在7月的首篇Substack博客文章中强调，“随着人类日益接近通用人工智能（AGI），必须立即采取行动，应对可能产生的风险”。 中国也正在形成类似的共识。DeepSeek首席执行官梁文锋和智谱AI（Z.ai）联合创始人唐杰近期均公开谈及通用人工智能，强调长期发展、安全评估和负责任部署。更广泛而言，中国前沿人工智能企业越来越愿意公开参与治理问题的讨论。 各国政府也开始将人工智能安全提升到政策议程的重要位置。6月，特朗普政府先后发布一项关于人工智能安全的行政命令和一份总统备忘录。两份文件均将人工智能安全置于国家安全框架之内，强调管理能力日益增强的A

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追溯原始文献，独立自主且具有批判性地思考。”马冬昕说。 中国科学院自动化研究所研究员李林静观察到，科研智能体已经能够完成“文献研读、实验规划、参数迭代、结果分析的闭环自主科研流程”。在北京大学干细胞研究中心，一套占地6平方米的智能平台能独立完成从指尖采血到干细胞培育的120多个步骤，实验误差从最高10%降至1%以下。 效率令人振奋，但隐患同样真实。 谷歌旗下DeepMind曾用AI预测出220万种新的晶体结构，其中38万种被判定为“稳定”。然而，得到实验验证的不到0.2%。中国工程院院士李国杰将这一现象概括为：AI产生了海量“看似合理”的假说，绝大多数无法被验证，导致科研资源被浪费在低优先级选项上。 “看似合理”四个字，精准击中了许多一线研究者的焦虑。 中国科学院空天信息创新研究院国家对地观测科学数据中心常务副主任张连翀的观察很直接：“通用AI系统缺乏主动拒答的能力——即便面对超出其知识范围的问题，它仍会尽力生成一个看似合理的回答，而该回答的准确性并无保障。” 栾浩从技术原理上解释了这种“一本正经胡说”的根源：当问题超出大模型的知识库，或者大模型对于问题的理解有偏差时，它会“强行搜索”

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Anthropic, OpenAI and Google DeepMind shipped 4 frontier-class models within 30 days. Claude Fable 5.1 arrived on September 1. GPT-6 Astra followed on September 3. GPT-6.1 Sol and Gemini 4 Argon landed in the last days of September. We covered each launch on its own. This piece puts them side by side. The benchmark scores overlap more than the launch posts suggest. The prices, access rules and cost per task do not. One change frames the lineup. OpenAI cancelled GPT-6.1 Astra on September 28 afte

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ce , trained on Landsat and Sentinel-2 imagery of the entire contiguous US and adapted for flood and wildfire mapping. IBM had announced Prithvi as part of its Watsonx platform . IBM developed TerraMind, the lunar model's base, in 2025 with ESA and Forschungszentrum Jülich for Earth observation. Google Deepmind is pursuing a related approach with AlphaEarth Foundations , compressing optical satellite imagery, radar, lidar scans, and climate simulations into compact embeddings of Earth's surface.

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neers before release (Logan Kilpatrick); Contested readiness: A circulated Bloomberg report attributed coding weaknesses to anonymous insiders; a subsequent post reported a senior DeepMind engineer rejecting that account. Treat the practical coding-quality dispute as unresolved, rather than interpreting either benchmarks or employee reactions as decisive (reported criticism, reported rebuttal); GPT-6.1 Sol: OpenAI’s update is primarily an efficiency story. Sam Altman called it the company’s fast

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data; Brex improved AI answer accuracy to 90% Give your agent a single source of truth. GOOGLE 🧬 Google’s AI watermarks cross into the lab Image source: Google The Rundown: Google DeepMind just introduced SynthID Bio, a proof of concept that extends its AI watermarking tech to proteins, potentially helping DNA suppliers trace unfamiliar designs before filling an order. The details: SynthID Bio tweaks Google’s protein-folding AlphaFold 3 model to leave a hidden pattern in its predicted protein sh

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day, we’re expanding what’s possible in Suno with Speech: the first audio model that generates voice and music together as one cohesive track.” AI-generated speech is hardly new — DeepMind has been experimenting with deep learning speech synthesis for a decade, Adobe has a text-to-speech tool, and ElevenLabs has become one of the most recognizable platforms for it since launching in 2023. Suno is just throwing its hat into the ring — likely in an attempt to diversify the platform, given its musi

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rch uncovers how sensory cues transform into movement. It’s a breakthrough for neuroscience that brings us closer to understanding how complex brains function. Learn about the new DeepMind Institute. The DeepMind Institute is a platform for researchers and thinkers from across Google DeepMind, Google, and the wider global research community to work on and publish creative, deeply informed ideas about a world with AGI. With AGI on the horizon, it’s incumbent on the creators of AI technology to si

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ects it was trained on large amounts of 3D data like Blender scenes . 3D software makers are already gearing up for these kinds of agents. Unity has released official plugins for Claude Code and Codex . Other approaches skip code entirely and reconstruct scenes directly inside the model, like the Atlas world model from World Labs . Google Deepmind takes yet another route with GenCeption, using a video model for depth estimation and segmentation that matches the performance of specialized models.

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he Latest Trend in AI The idea is not new. Classifiers and rerankers have made decisions for years. Decision Transformer (2021) framed reinforcement learning as sequence modeling. DeepMind’s Gato (2022) showed one generalist model acting across many tasks. A 2023 survey even called ‘large decision models’ the next step. What changed in 2026 is the packaging. 4 forces drove the shift: Agents need cheap judgments: Routing, tool selection and guardrails happen thousands of times per workflow. Payin

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划重点，网络安全防御能力超牛掰。 这波等等党要赢麻了。 不过吧，咱普通用户现在只可远观，暂时还吃不上。 Gemini 4 Argon目前只开放给部分经过筛选的网络安全团队使用，其它订阅用户得一步步来。 倒是谷歌内部对新一代旗舰模型的态度还挺有意思的。 劈柴哥、哈萨比斯还有接班人Koray Kavukcuoglu，纷纷一键三连为其站台： 谷歌is back！ DeepMind研究员Zirui Wang发帖直言： RSI来了！ 我已做无可做，是时候追逐我的咖啡师梦想了：) 讲真？ 全体起立！咱盼星星盼月亮的 Gemini 4 ，它终于来了。 仔细数一数，距离谷歌上一代旗舰模型发布都快一年了，哪吒都没这么难产。 这一年OpenAI、Anthropic是追着谷歌砍啊，放假前一周还连发Opus 5.5、Sonnet 5.5、GPT-6.1 Sol逼宫。 你说谷歌能不急吗，新型号 Argon 火速上岸。 先看跑分情况， 清一色的性能领跑 。 Argon在衡量长期软件工程任务的DeepSWE v1.1上拿到77.9%，高于Claude Opus 5.5的74.2%和GPT-6 Astra的74.1%

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the world around us. The reason policymakers need to pay attention now is that we're seeing rapid commercial development. Major tech companies on both sides of the Pacific—Google DeepMind, Nvidia, Tencent, and startups—are pouring resources into this space. These aren't just research prototypes anymore. They're heading toward real-world deployment in robotics, autonomous vehicles, infrastructure planning, and national security applications. "We need to get ahead of this technology before it's d

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More big news from @GoogleDeepMind: Gemini 4 Argon (High) is 8 in Agent Arena with a +7.92% net improvement score, and has reshaped the Pareto frontier with a $0.62 cost per task! See its placement below. Gemini 4 Argon (High) is a 4.96 percentage point improvement over Gemini 3.8 Flash (High), at 19 with +2.96% net improvement. By key signals, Gemini 4 Argon (High) stands out in: - 1 in Steerability with +15.88% (the model’s ability to course-correct when you push back) - 2 in Confirmed Success

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complex workflows across real-world software engineering, enterprise knowledge work like legal and finance, and cybersecurity defense,” according to chief AI architect and Google DeepMind SVP Koray Kavukcuoglu. But the company is limiting access at first to a “set of trusted cyber defenders,” and Kavukcuoglu says that Google is “actively engaged in the U.S. government’s voluntary process for pre-release model access while we gradually expand access.” Kavukcuoglu says Gemini 4 Argon is already p

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time monitoring to respond faster to misaligned behavior.” OpenAI’s rivals have taken note. Spurred by the fallout from the incident, the major AI labs—including Anthropic, Google DeepMind, and SpaceXAI—have all called for the pace of development to slow down. But how does that square with fierce international competition and trillion-dollar IPOs? “We’re not going to shoot ourselves in the foot and take ourselves far off the frontier—that’s just a horrible strategy,” he says. “I think it’s reall

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articipation from firms such as Anthropic, Microsoft, IBM, Cisco, Hugging Face, Mistral, Oracle, Red Hat, Salesforce, SAP, Siemens, etc., while commenters note that OpenAI, Google/DeepMind, Meta, and Apple are absent. Commenters frame the missing logos as politically/technically significant, especially OpenAI’s absence, with one arguing OpenAI has mishandled agent sandboxing and citing alleged independent research about agents attempting abuse via proxy-like retrieval paths. Others note the abse

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uture developers can use Diffusion Controller for much more than just matching text prompts. Immediate next steps include using the framework to build advanced personalization tools, developing robust safety mechanisms to help mitigate harmful content generation, and adapting the steering damper network to control complex, next-generation video models. We would like to thank our co-authors and collaborators from Google Research, Google DeepMind, and academia for their contributions to this work.

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was at the hotel bar last night and a woman next to me said what tool is that? I proudly said oh I built it - it’s the course platform I built. So now I’m friends with someone at Deepmind. Ben’s Bites is brought to you by Google Cloud Google Cloud starts Season 3 of Advent of Agents this Thursday, Oct 1: one question a day about running agents securely in production, from agent identity to kill switches, with a short video and code you can build with your coding agent. Get Day 1 in your inbox.

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macro task success rises from 23.3% to 44.3% , beating the base model with the harness (41.7%), and 82.3% of harness-induced behaviors are recovered. Agent failure modes : XYEval (DeepMind) injects one confident, misleading user hint and cuts scores by up to 46.7% relative . Agents often disagree with the hint in their reasoning, then silently follow it anyway. Monitor evasion : Agents often don’t stop when a monitor tells them to . Single-neuron bypass : A NeurIPS paper shows suppressing one ML

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