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gate in 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

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ance in 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 al

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e real-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’

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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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cent fund raise of $315 million in February . Its first release, GWM Worlds, was launched last December. Alongside Runway, there are several other notable projects in this domain: Google DeepMind’s Genie 3 (which also generates at 720p and 24 fps), Odyssey-2 Pro , and World Labs’ RTFM (Real-Time Frame Model). We’ve summarized their differences in the following table: Given the complexity and massive latency demands of real-time video and audio generation (which we’ll get into below), all of the

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th distinct accents and characteristics. 🔵 Gemini 3.8 Flash-Lite TTS: Built for efficiency and scale, choose from your created styles or our expansive… pic.twitter.com/iiZwtRBxgc— Google DeepMind (@GoogleDeepMind) September 23, 2026 Gemini 3.8 Flash TTSは自然言語プロンプトを使用して、100以上の言語と方言でキャラクターやアクセント、声の特徴をカスタマイズし、新しい音声をゼロから作成できる。メキシコスペイン語、ケベックフランス語、スコットランド英語などの地域的なバリエーションを含む、幅広い言語に対応した2,000種類以上の音声ライブラリを持っており、30秒の音声サンプルがあれば、一貫性のある音声プロファイルを再現する。 また近日公開予定の機能として、プロンプトを使って音声ライブラリから選んだ声の音色、ピッチ、ペース、アクセントを微調整して

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清晰，从互联网到新能源，再从新能源到具身智能。 2015年前后，最聪明的产品经理和算法工程师涌向造车新势力；2025年之后，最顶尖的AI人才和算法人才，开始不约而同地奔向机器人公司。 如果把近半年的高管变动列成一张表，会发现一个规律，走得最密集的，恰恰是那些被寄予厚望的“一号位”。 罗剑岚的履历十分华丽——博士毕业于加州大学伯克利分校，曾在Google X、Google DeepMind担任研究科学家。2025年4月罗剑岚高调加盟智元机器人，牵头组建智元具身智能研究中心，构建了超100万条真机操作数据的AgiBot World数据集，次年9月进入合伙人团队。然后，在智元官宣赴港上市仅10天后，他从合伙人名单中消失了。 智元方面回应称：“系内部岗位调整，没有离职。”不过，当被问及调整后罗剑岚的职务以及负责工作内容时，智元方面未透露更多信息。 丁琰的轨迹则是另一种版本。从上海AI Lab到一星机器人，再到鹿明机器人，他在14个月内换了三份工作。在一星机器人只待了4个月，公司就因吉利战略收缩而解散。辗转33天后，2025年11月他带着十余名骨干集体加入鹿明。结果不到280天，又走了。 一个值

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ity, labor restructuring, and scientific discovery Anu Madgavkar and Daniel Rock will lead Google's AI & Economy Research Program alongside Alex Imas, Director of AGI Economics at Google DeepMind, and Zanna Iscenko, AI & Economy Lead, in Google's Chief Economist's Office. Shaping future research and collective impact Maximizing AI’s economic opportunity while mitigating disruption requires sustained partnership. This expanded team of experts will serve as a scientific bridge, directly informing

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short the one thing it requires: everyone. In today’s AI rundown: Zuckerberg breaks ranks on the AI slowdown Nate's Notebook: Same-hole thinking A beginner’s guide to ChatGPT Work Google DeepMind starts an AGI think tank LATEST DEVELOPMENTS Image source: Mark Zuckerberg (@finkd on X) / The Rundown The Rundown: Meta CEO Mark Zuckerberg just pushed back on the coordinated AI slowdown, arguing each lab already has its own “responsibility and incentive” for pacing itself safely, in a seemingly veile

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ons across all target languages, preserving strict error hierarchies and streaming behavior, and integrating the generator directly into our internal monorepo and build system. At Google DeepMind, while we use AI across our development workflows, we believe in choosing the right tool for each layer of the stack. Transforming formal API specifications into multi-language SDKs demands determinism and strict type safety. With Speakeasy, we pair a fast, deterministic generator at the core with Antig

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ey. OpenAI CEO Sam Altman said that embedding third-party evaluators is a solid step. SpaceX and Tesla founder Elon Musk echoed that oversight is long overdue. Demis Hassabis from Google DeepMind called the essay a step in the right direction. Microsoft CEO Satya Nadella emphasized that pursuing AI without keeping human benefit at the center isn’t worth doing. Tech investor Gavin Baker posted a great write-up below summarizing these industry responses Will Chinese labs follow the suit? Some soci

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s is central to Andon’s business proposition: providing AI developers with evaluations grounded in situations that arose outside the lab. The company says it works with Anthropic, Google DeepMind, OpenAI, and SpaceXAI on research and evaluations. Andon’s physical businesses themselves remain decidedly less successful. When Andon Market’s Carson spoke with IEEE Spectrum , he was about an hour into his shift. Two customers had come in. Neither bought anything, although both left with free pins and

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s considering a $5B loan to AI cloud startup Fluidstack to strengthen U.S. manufacturing and supply chains for data-center components, WSJ reported. At the BRICS Summit in New Delhi, China proposed an open-source AI community to support shared AI development, training, and industrial use across member nations. Anthropic and Google DeepMind’s AI safety researchers Joe Benton and Josh Engels resigned to join METR Evals, with both warning that AI capabilities are outpacing safety. Sponsored Listing

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could see a scenario with substantial growth and modestly higher unemployment, or crazy growth and crazy unemployment. What people are saying: LSE economics professor Ben Moll and Google DeepMind’s director of AGI economics Alex Imas wrote an essay on Substack dismissing the predictions of double-digit GDP growth that Amodei and others have made. The economists think that such extreme forecasts would assume too much: Double-digit growth rests on several shaky assumptions, they write, including“m

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., Singh, S., Bartoldson, B. R., et al. (2025). Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach. arXiv:2502.05171. https://arxiv.org/abs/2502.05171; Google DeepMind (2026). DiffusionGemma. https://deepmind.google/models/gemma/diffusiongemma/; Gould, D., Ward, F. R., Woodruff, A. C., Arike, R., Hills, J., Serrano, A., et al. (2026). Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models. arXiv:2606.07157. https://arxiv.org/abs/2606.07157;

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ent – AI is reshaping the industry fast. Discover insights from 300 senior leaders on intelligent portfolio orchestration and what you need to succeed. Read Tempo's report today . Google DeepMind launched AlphaGenome Atlas, a free, searchable map generated by its AlphaGenome AI that predicts what all 9B possible one-letter DNA mutations do. Devin maker Cognition announced a new $2B funding round at a $48B valuation, with the company’s annualized revenue nearly doubling to $900M since May. Incept

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ine of AI commerce “default legal, not default illegal,” though it wasn’t clear what he thought was illegal that shouldn’t be. Safety testing for models came up mostly in passing. Google DeepMind’s Demis Hassabis said the opportunity had to be pursued “in a responsible way,” stopping short of detailing the industry-funded testing body he has pushed for all year. Sam Altman called the administration’s voluntary pre-release review of OpenAI’s forthcoming Astra model a “productive process.” There w

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so you can act on what really matters for your organization. With Gartner AI insights, you can: Access 4,000+ proven AI use cases Get objective, analyst-led guidance The Rundown: Google DeepMind introduced WeatherNext 3, its new weather model that uses live satellite images to refresh its forecasts every hour, taking the top spot on scoreboards against both rival models and the U.S. and European weather services. The details: WeatherNext 3’s improvement is about 5x in detail for temperature dow

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ve a big advantage. Before we explore the strategies companies use to generate real-world training data, it’s worth asking why we need real-world data at all. Nearly a decade ago, Google DeepMind trained an AI to play Go entirely by self-play. After playing millions of games against itself, the model became better at Go than the top humans. Could we do something similar for robots? Instead of training physical robots in the real world, maybe we could have virtual robots “teach themselves” to per

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ftware. VMware, through its VCF 9, also is expanding to more than 150 the open and commercial AI models that can run on AI Factory can run, adding support for Nvidia’s Nemotron 3, Google DeepMind’s Gemma 4, Alibaba’s Qwen 3.7-Max, NEC’s cotomi, and GLM 5.2 from Z.ai. VMware is introducing new AI services, including the ability to share AI models between tenants and lines of business via isolated nameplates, ensuring security and eliminating the need to deploy the same models, AI Gateway for ensu

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