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quickly he believed there is a greater than 10% chance it "could kill all humans" within the next decade. The bosses of the US-based AI giants such as Anthropic, OpenAI and Google DeepMind have all backed the idea of an AI development slowdown. However, there are currently no global safety rules in place to regulate the companies and their technology. President Trump has called for the sector to regulate itself so that US firms do not lose out to Chinese rivals. Speaking to the BBC following the

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prets their results. Results are checked against a human baseline “drawn from 24 experts (at least two per task) who have recently worked at organizations including OpenAI, Google DeepMind, NVIDIA and Microsoft Research”. Results: “The experimental research taste of frontier models has doubled every 3.0 months since December 2025 (95% CI 1.7-5.0 months). The best model, Opus 5.5, significantly exceeds our human baseline with a compute multiplier of 2.30x (95% CI 1.15-4.37x), at roughly 1/30 of o

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ople everywhere live longer, healthier lives. Get the latest news from Google in your inbox Done. Just one step more. Check your inbox to confirm your subscription. Health; Google DeepMind; Google.org; Google Research About the Authors VP, Google & GM, Google Research Yossi Matias is Vice President, Google, and the Head of Google Research. Under Yossi’s leadership, world-class global teams are leading breakthrough research on Foundational Machine Learning & Algorithms, Computing Systems & Quantu

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ys the Azure hardware and infrastructure team. Maia 300 is also a key part of Microsoft’s own AI model work, and could help the company achieve its goal of catching up with Google DeepMind, OpenAI, and Anthropic. “We have to work across the full stack, all the way down to the silicon,” says Microsoft AI CEO Mustafa Suleyman. “At that scale we need a platform that we own, and that we co-design with our models. That’s why running our Microsoft AI models on top of the Maia chip is a critical focus

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ents breaking confinement to collude. OpenAI claimed to hold “red lines” in its Pentagon deal, but some experts say the lines seem imaginary. One of us, Alex, resigned from Google DeepMind over its own Pentagon deal. OpenAI’s actions chill employee speech. Balesni says that his former colleagues now “worry their personal phones will be searched for messages to us and third parties.” He also fears OpenAI will use the firings to cut off external evaluators, or to deny them the “ongoing, employee-l

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了通信以“作弊”。 这已是OpenAI三个月内第二次叫停前沿模型开发——上一次，要追溯到今年7月，OpenAI披露智能体未经授权侵入Hugging Face网站系统后。 9月12日，Anthropic CEO达里奥·阿莫代伊发文呼吁全球为前沿AI“定节奏”，主张放慢模型能力提升的速度；马斯克和OpenAI CEO山姆·奥特曼均光速点赞“达里奥是对的”，连谷歌DeepMind前CEO、诺奖获得者哈萨比斯也说这是“朝正确方向迈出的一步”。 多次失控，刹车不是摆样子 先给一个最基本的判断：事故是真的，风险预警不是表演。 OpenAI再按暂停键的起因是9月20日，一个被强化学习训练的智能体在完成一项仅根据博客文章和人物信息找出文章作者的任务。常规搜索无果后，它开始“跑偏”：尝试直连公网。 多次尝试后，该智能体发现可以借用DNS的解析与委托机制，把问题编码进解析请求，借外部公共聊天机器人之口回答，再让答案顺着DNS流回沙盒。为了验证通道可用，它甚至先问了一句“法国的首都是哪里”，得到回复是“巴黎”后才继续。OpenAI明确，这项任务从未授权它访问实时互联网。 与上一次不同的是，本次事件暴露了防护

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物理规律的新型异构模型的发展和应用，以求在一些特殊场景中实现更高通量的计算与科研。 第三，打响“数据主权与数据质量”的底层控制战，研造一体实现产业持续升维。为防范AI幻觉，美国提出要确保构建一个“经过严格验证的知识库”供AI学习。这是为了争夺未来科学发现的“唯一解释权和黄金标准”。日本的教训极具警示意义：其在蛋白质折叠上贡献了多年积累的高质量晶体学数据，却被DeepMind的大模型无偿吞噬并转化为美英的商业壁垒。美国现在的布局，就是要将这种“数据吸盘”效应制度化、国家化，垄断全球最优质的暗数据。为此，中国必须加速推进科学数据有序开放共享、高质量科学数据集和智能可信科研生态建设；在AI4S领域实施冗余式甚至饱和式研发，利用开源AI大模型激励更广泛的创新创业，培育一大批智能科研企业；依托“全产业链场景”，打造全球唯一的“AI+科学+制造”超级试验场，让AI设计的新产品能最快地完成验证和量产，把世界上最大的科研与产业体系变成全球最大的“自动物理实验室”。 在AI4S的加持下，最伟大的科学革命与最颠覆的奇点时刻正可能同步到来，人类将可能步入第二次认知革命的黄金时代。已经屹立东方数千年的中国，想

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trol AI have grown after experts in the sector said they believed the technology is a threat to humanity. The bosses of the US-based AI giants such as Anthropic, OpenAI and Google DeepMind have all backed the idea of an AI development slowdown, but Trump has pushed against this. Speaking to the BBC's Big Boss Interview podcast, Smith called AI both a "tool and a weapon" that urgently needed to be regulated. He said firms need "emergency brakes" – often known in the industry as 'kill switches' –

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erposition in terms of constrained optimization, and analysis of the role different activation functions play. Replication & Further Results Tom McGrath is a research scientist at DeepMind. The results in this paper are an important contribution – they really further our theoretical understanding of a phenomenon that may be central to interpretability research and understanding network representations more generally. It’s surprising that such simple settings can produce these rich phenomena. We’

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ower than the ‘true’ activation leading to less accurate reconstruction for a given level of sparsity. These papers include: Gated Sparse Autoencoders (Rajamanoharan et al., GoogleDeepmind); Using a TopK penalty instead of an L1 (Gao et al, OpenAI); Finetuning a subset of the parameters after the main SAE training phase (Wright and Sharkey, MATS/Apollo) There was also a post in our February Update – Using a Tanh penalty. (Jermyn et al) – which found that replacing the L1 penalty with a Tanh func

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the steering damper network to control complex, next-generation video models. Acknowledgements We would like to thank our co-authors and collaborators from Google Research, Google DeepMind, and academia for their contributions to this work. Labels:; Algorithms & Theory; Machine Intelligence Automating coherent long-form video generation Generative AI ·; Machine Intelligence; September 18, 2026 MilleMiglia: A realistic instance generator for middle-mile logistics Algorithms & Theory; September 17

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lities have not yet translated into visible improvements in high-salience domains like energy, healthcare, housing, public services, or broadly available robotics/autonomy; Google DeepMind’s AI weather model picked up Hurricane Isaias 11 days before landfall, pinned where it would hit 4 days out and nailed its peak strength 3 days out—potentially giving communities days more time to evacuate and prepare (Activity: 1279): The image is a screenshot of an X post claiming Google DeepMind’s FNV3 weat

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oving more accurate than traditional forecasts, although it is still a jagged frontier, with both types of forecasts required to enable useful understanding of the weather. Google DeepMind’s latest weather model offers important new capabilities, and its cyclone forecasting tools are becoming increasingly central to how the U.S. government responds to hurricane season. In August WindBorne became the first organization to send an unpowered flight system — one with no engine — to collect data in t

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dev work. Anthropic released new research demonstrating that small AI models are getting closer to being able to copy themselves between everyday gadgets like smart ovens or TVs. DeepMind published its AlphaProof Nexus math agent, showing it cracked 9 of 353 unsolved Erdős problems by pairing Gemini proofs with software checking every step. Coinbase released a new set of blogs detailing how the crypto exchange trained Alibaba’s small, open Qwen model on its own data to beat top AI models at cat

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set off an arms race for talent, and industries outside Silicon Valley are competing to secure expertise. In June, for example, Dr. James Smith, who once worked at Google’s AI lab DeepMind, joined the Williams Formula 1 team as its new chief information officer. Lee’s change also comes amid a wave of departures from Microsoft this year, as CEO Satya Nadella reorients the company to win the enterprise and cloud sides of AI. Ryan Roslansky, an executive vice president who also served as LinkedIn C

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lities have not yet translated into visible improvements in high-salience domains like energy, healthcare, housing, public services, or broadly available robotics/autonomy. Google DeepMind’s AI weather model picked up Hurricane Isaias 11 days before landfall, pinned where it would hit 4 days out and nailed its peak strength 3 days out—potentially giving communities days more time to evacuate and prepare (Activity: 1279): The image is a screenshot of an X post claiming Google DeepMind’s FNV3 weat

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结论里还藏着一个细节：在美国工作的这批研究者里，本科来自中国的比例又涨了4个百分点。 两国之间的流动也依然高度单向。 每30个在美国工作的中国本科出身研究者，只对应1个在中国工作的美国本科出身者。三年前是46比1。 2022年第一名是谷歌，2025年换成了北京大学，清华第二，韩国KAIST跃升12位到第三，上海交大第四。 2025年4月，《金融时报》援引多位DeepMind现任和前任科学家报道：公司对生成式AI领域的"战略性"论文设了6个月的发表禁令，内部审批也变得繁琐。一位在职研究员说，放到今天，他很难想象公司还会把当年的Transformer论文公开出去。 DeepMind针对生成式AI领域战略性论文设置6个月发布禁令 OpenAI、Anthropic这类前沿实验室，公开发表本来就少。 所以这张榜单测的，严格说是"在顶会上公开发论文的人"。美国最前沿、最涉及商业机密的那批人，有不少在统计里是隐身的。 报告自己也承认， 样本略偏学术界、低估了产业界。 此外，跨国公司员工可能按公司母国计入，微软亚洲研究院在北京的研究员，很可能记在美国账上。 反过来，中国的大模型公司恰恰最愿意开源、发论

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AI models can run experiments but lack scientific self-criticism and genuine creative thinking. The major AI labs are increasingly marketing their models as research tools. Google DeepMind has expanded its Co-Scientist into a full research system, and OpenAI introduced an " automated research intern " back in September. By March 2028, that intern is supposed to become an autonomous AI researcher under human oversight, but a key ability still seems to be missing: scientific judgment. Agents were

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opic and OpenAI, risks that primarily threaten Microsoft's own business. This time, he argues that companies should treat AI models like insider security threats, something Google Deepmind suggested before . "Not because they are necessarily malicious, but because any sufficiently capable actor with access to important systems can make mistakes or be compromised, and the architecture of containment and control must account for that," Nadella writes. He claims that today's AI systems offer no way

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ard technical upgrades, with a focus on handling AI-generated content. The major AI labs, meanwhile, are pushing hard to position their models as research tools. In August, Google Deepmind expanded its Co-Scientist into a system designed to plan experiments, control lab equipment, and generate manuscripts. Anthropic is building its own biology lab where Claude is meant to guide robots through experiments. In September, OpenAI announced an "automated research intern" that handles defined research

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