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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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ist and chief research officer at OpenAI; Dario Amodei, cofounder and CEO of Anthropic; Shengjia Zhao, chief scientist at Meta AI; Shane Legg, cofounder and chief AGI scientist at Google DeepMind; and John Schulman, the chief scientist at Thinking Machines. Looking at the METR report, it seems clear that AI-model capabilities have already advanced beyond our ability to understand and control them. Some of the signatories of “Pacing the Frontier” have noticed. And the challenge of aligning AI age

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cal data science, machine learning, bioinformatics, and digital health reviewed blinded reports from the Biomarker Discovery Framework and three contemporary AI research systems ( Google DeepMind’s AI co-scientist , Biomni , and Google ADK’s Data Science Agent ). Biomarker Discovery Framework, Biomni, and the Data Science Agent were scored together in 21 sessions, and Biomarker Discovery Framework was scored in a separate 13-session set using the same evaluation instrument. In the blinded evalua

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ting, we’ve seen no impact of watermarking on the content, level of creativity, or readability of Claude’s text. In the SynthID-Text paper , which introduced the technique we use, Google DeepMind tested this impact by serving a model that used watermarking to a portion of their Gemini traffic and comparing thumbs-up and thumbs-down ratings. They found no statistically significant differences from the unwatermarked model. And in a controlled study, human raters comparing watermarked and unwaterma

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r, an Anthropic staff member (and non-mathematician), prompted Claude to “take a real stab” at the hypothesis itself, leaving the mathematical choices from there up to the model.” Google DeepMind’s AlphaEvolve paper says: “in contrast to [traditional computational or theoretical methods performed by human experts], we have found that AlphaEvolve can be readily scaled up to study large classes of problems at a time, without requiring extensive expert supervision for each new problem.” Mythos Prev

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ms that could one day augment care by engaging with the sensory complexity of clinical practice. The research described here is joint work across many teams at Google Research and Google Deepmind. We are grateful to all our co-authors - Mahvish Nagda, Jihyeon Lee, Matthew Thompson, CJ Park, Tim Strother, Valentin Liévin, Roma Ruparel, Akshay Goel, Teya Bergamaschi, Suhana Bedi, Meet Shah, Pavel Dubov, Liviu Panait, Toshiyuki Fukuzawa, Sam Schmidgall, Craig Schiff, Joseph Xu, Aliya Rysbek, Yana L

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c tasks as coding, research, and file management – illustrate that Lightning is up to 30 percent faster than comparable Qwen models, and faster than other frontier AI models, like Google DeepMind’s Gemma. Check it out: The speed is important, she said, because “always-on” agents make thousands of models calls and latency matters. Lightning leverages Nvidia’s hybrid Mamba transformer architecture and multi-token prediction technology. According to the company, Lightning outperformed other open an

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T PhD student in electrical engineering and computer science (EECS); Kaiming He, a principal investigator in the lab, associate professor of EECS, and a distinguished scientist at Google DeepMind; and senior author Wojciech Matusik, the Joan and Irwin M. (1957) Jacobs Professor of EECS and a CSAIL principal investigator. Tsinghua University Associate Professor Mingsheng Long was also a co-author. The team presented the paper at the International Conference on Machine Learning in July. The resear

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r, Scott Reed, Jingwei Zhang, Konrad Zolna, Jeff Clune, Nando de Freitas, Satinder Singh, Tim Rocktaschel. Genie: Generative Interactive Environments . arXiv:2402.15391, 2024. [2] Google DeepMind. Genie 2: A Large-Scale Foundation World Model . Technical report / blog post, 2024. [3] David Ha, Jurgen Schmidhuber. World Models . arXiv:1803.10122, 2018. [4] Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap. Mastering Diverse Domains through World Models . arXiv:2301.04104, 2023. [5] Ma

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