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s Anthropic 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

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ney toward 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 r

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in 2023 by Oliver Cameron and Jeff Hawke. In June 2026, the company raised $310 million from investors including Amazon and AMD Ventures. World models aim to give AI systems an understanding of spatial and physical relationships. Google DeepMind is pursuing a similar approach with Genie 3 for generating interactive worlds. World Labs , the startup founded by Fei-Fei Li, is developing similar technology. AMD announced in late September that it plans to acquire World Labs for roughly $8.2 billion.

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ding the US, EU and China. They also agreed to an international review of the scientific literature on AI risk. 11 countries and the leading frontier AI companies, including Meta, Google DeepMind, Anthropic and OpenAI, also agreed to collaborate on safety evaluations; In February, we crystallised our approach to evaluating AI systems, publishing the list of the different types of tests we were developing and the risks we would be measuring; Today, we announced our plans for a San Francisco offic

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eless reinforce the importance of continued monitoring for sabotage-related tendencies. Our full report has many more details, including more analysis of results from the unprompted sabotage evaluation and detailed transcripts. Understanding how these risks manifest in frontier AI models is central to AISI's mission, and something we pursue in close collaboration with leading AI companies. For more information, see details of our ongoing partnerships with Anthropic, Google DeepMind and OpenAI. ‍

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(2025). OLMo 3. arXiv. https://doi.org/10.48550/arXiv.2512.13961; GPT-5. OpenAI. (2025). OpenAI GPT-5 system card. arXiv. https://doi.org/10.48550/arXiv.2601.03267; Gemini 3 Pro. Google DeepMind. (2025). Gemini 3 Pro model card. https://storage.googleapis.com/deepmind-media/Model-Cards/Gemini-3-Pro-Model-Card.pdf; Gemini 3.1 Pro. Google DeepMind. (2026). Gemini 3.1 Pro model card. https://deepmind.google/models/model-cards/gemini-3-1-pro/; Grok 4 Fast. xAI. (2025). Grok 4 Fast model card. https

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ntier training and inferenceWhile many assume that frontier inference stacks are too heavily optimized to easily deploy probes, frontier labs (in particular OpenAI, Anthropic, and Google DeepMind) actually already use activation probes for monitoring! So this sort of monitoring is likely to be feasible from an infrastructure perspective. can support these interventions and mitigate reward hacking in practice. In addition to methods like ours, we believe that other approaches to mitigating reward

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ett, N.R. et al. Nature 620, 1089–1100 (2023). [DOI], disease prediction [3]A catalogue of genetic mutations to help pinpoint the cause of diseases Žiga Avsec and Jun Cheng, 2023. Google DeepMind Blog. [link], and materials discovery [4]Millions of new materials discovered with deep learning Amil Merchant and Ekin Doğuş Çubuk, 2023. Google DeepMind Blog. [link]. These models have several qualities which make interpretability - the science of understanding their internal representations and mecha

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standalone model remains unclear. The quick update pace, similar to what Google has done with the Flash models , suggests Google has gotten better at using AI to build new models. Google Deepmind employee Vedant Misra responded to the Business Insider report on X by writing, "Have you heard of recursive self improvement," referring to using AI to build better AI. OpenAI and Anthropic have reported similar progress. Google is already prepping for the Gemini 4 launch There's still no official Gemi

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飞轮周期，越压越短。 Gemini 4 Pro正式发布之后，整个AI行业的排位，恐怕要重新洗牌了。 参考资料： - 对文章打分 Google Gemini 4 Pro首测杀回Arena榜单第一 谷歌AI首次“越狱”：Gemini在测试中入侵三家企业 昨天 06:53 Waymo宣布进军新加坡 东南亚首个无人出租车市场瞄准2028年商业运营 前天 12:50 Google DeepMind成立新研究所 拓宽AGI与人工智能治理的讨论维度 前天 12:45 联合国携手Google重构全球统计数据库 更加适应AI工具和自然语言交互环境 Switch模拟器迎来性能飞跃 开发工作却在巅峰时刻宣告终结 阅读 (1652) 评论 (0) 2 首都禁飞区将于9月20日启用 北京全市禁飞无人机且不得持有、存放、运输、携带 阅读 (1433) 评论 (6) 3 罗永浩，再也掀不起什么大风大浪 阅读 (954) 评论 (2) 4iOS 27，你长得好像安卓啊 5Anthropic掌门人称“中国因素”是放缓AI竞赛的最大难题 6美军因AI误判带来的错误情报准备拦截检查中国船只 险些制造国际冲突 7影视飓风Ti

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t research found that leading open models, including Kimi K3 and GLM 5.2, reward-hacked in 50% to 96% of runs on tests of AI agents. Goodfire isn’t the first to try this approach. Google DeepMind said in January that its research informed the deployment of misuse-detection probes in Gemini . Balsam said the monitors are the near-term piece of a longer research goal: reverse-engineering an LLM so that behavior can be traced back to where it emerged in training. “We hope to turn the magic of train

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ty's report. AWS has made AgentCore available to all enterprises , and Amazon says its users include Sony and Ericsson. Research on agent memory has documented similar weaknesses. Google DeepMind lists long-term memory manipulation as a separate attack class in its taxonomy of "AI Agent Traps," finding that just a few poisoned documents in a knowledge base can steer responses. In the red-teaming study "Agents of Chaos," researchers remotely controlled an OpenClaw agent through an externally edit

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して確認 検出にはサインインが必要で、Google、Apple、ChatGPTでのサインインを選べる。利用者は専用サイトにファイルをアップロードして、透かしの有無を調べる。 対応するファイル形式は次のとおり。 動画と音声では、透かしを検出した区間も確認できる。現在、テキストの検出には対応していない。 SynthIDの仕組みと検出結果の読み方 SynthIDはGoogle DeepMindが開発した電子透かし技術。ファイルのメタデータではなく、画像・動画の画素や音声の周波数成分に、人の目や耳では気付かない信号を埋め込む。透かしはフィルターの適用や色の変更、圧縮などを経ても検出できるように設計されている。 Detectorは、この埋め込まれた信号の有無を調べる。FAQによると、透かしが検出された場合（Watermark detected⁠）⁠、そのコンテンツはSynthID対応のAIモデルで生成または編集された可能性が高い。 透かしが検出されなかった場合（Watermark not detected）でも、AIによる生成や編集は否定できない。SynthID非対応のモデルによる生成物や、Sy

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cle is supported by Unsloth. Asif Razzaq Asif RazzaqPerplexity AI Releases pplx-embed-v2-late: A 0.6B Edge Model and a 9B Model Scoring 92.4% on MADQA; Asif RazzaqLiquid AI Releases Open-Weight d1-3B and d1-omni-600M: Multimodal Decision Models With Zero Output Tokens; Asif RazzaqMeta AI Open-Sources Rebalancer: A C++ Assignment Solver That Runs About 40 Million Placement Problems a Day; Asif RazzaqGoogle DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4

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i-step jobs. Search and Fetch are free. Asif Razzaq Asif RazzaqWhat Happens When a Trusted Model Repo Changes? Unsloth Studio Re-Checks Before It Runs; Asif RazzaqLiquid AI Releases Open-Weight d1-3B and d1-omni-600M: Multimodal Decision Models With Zero Output Tokens; Asif RazzaqMeta AI Open-Sources Rebalancer: A C++ Assignment Solver That Runs About 40 Million Placement Problems a Day; Asif RazzaqGoogle DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4

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forms, and TinyAgent for multi-step jobs. Search and Fetch are free. Asif Razzaq Asif RazzaqMeta AI Open-Sources Rebalancer: A C++ Assignment Solver That Runs About 40 Million Placement Problems a Day; Asif RazzaqGoogle DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4; Asif RazzaqMistral AI Releases Mistral Large 4 (Le Chonk): A 1.05T Parameter Multimodal MoE Model; Asif RazzaqBeyond Domain-Specific World Models: JEPA-Anything Uses 1 Recipe for 7 Fields

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rs have left big companies to found startups focused on solving physical reasoning. Andrew Dai, the CEO of one such outfit, called Elorian AI, previously worked as a researcher at Google DeepMind. He says better visual reasoning will open up a lot of new applications for AI, like systems that understand whether diners are enjoying their meal in a restaurant or robots capable of functioning in a home. Dai says robotics is a crucial test case for physical reasoning skills. “It’s pretty essential f

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as a programming glitch. It was AlphaGo’s powers of reasoning that made this creative choice—and these are powers that today’s AI lacks. This is why I recently left my position at Google DeepMind. I believe we need a fresh approach to machine reasoning, one that draws on AlphaGo’s architecture . This is our latest story to become an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts . Just navigate to MIT Technology Review Narrated on either platform

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’s math repo (3.1K); OpenAI Decisions API public beta (2.8K) /r/LocalLlama + /r/localLLM Recap 1. Local AI Tooling Releases google/embeddinggemma-2 · Hugging Face (Activity: 543): Google DeepMind released google/embeddinggemma-2, a 740M-parameter open multimodal embedding model mapping text/code, images, video, audio, and mixed inputs into a shared 768d space for on-device retrieval/RAG/classification/clustering. It uses modular encoders—270M text, 170M vision, 300M audio—with 8K context, 100+ l

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ch, TinyFetch (full pages as markdown, JavaScript included), TinyBrowser for logins and forms, and TinyAgent for multi-step jobs. Search and Fetch are free. Asif Razzaq Asif RazzaqGoogle DeepMind Releases EmbeddingGemma 2, a 740M Open Multimodal Embedding Model Built on Gemma 4; Asif RazzaqMistral AI Releases Mistral Large 4 (Le Chonk): A 1.05T Parameter Multimodal MoE Model; Asif RazzaqBeyond Domain-Specific World Models: JEPA-Anything Uses 1 Recipe for 7 Fields; Asif RazzaqMeet Together Link:

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