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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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to similar platforms like Health Data Nexus as examples of PhysioNet’s legacy. Although there are now more resources out there hosting similar electronic health data, according to Google DeepMind researcher Vivek Natarajan, both PhysioNet and MIMIC “set the standard,” he says, “and it’s still the standard right now.” That standard, according to those who use the platform, changed how research is conducted. Access to data should not be the determinant for which ideas are possible, according to Zi

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tomAI’s end-to-end real-world performance through DDx accuracy on a population sample, and show how SymptomAI diagnoses can enable analysis of population-scale signals like wearable biosignals for identifying associations in physiological signals with reported illness. This work is the result of equal contributions from Joe Breda, Jake Sunshine and Daniel McDuff. We would like to thank our co-authors and collaborators from Google Research and Google DeepMind for their contributions to this work.

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and employing more advanced machine learning methods, we hope to unlock significant additional improvements. Our work thus enables a new paradigm: a quantum computer that learns from its errors and doesn’t stop computing. We thank our co-authors for their contributions, including building and maintaining the hardware, software, cryogenics and electronics infrastructure. This work was made possible by the Google Quantum AI team at Google Research, in collaboration with teams from Google DeepMind.

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聚变等离子体的复杂动态，到探索新材料的广阔搜索空间，再到理解从全球最先进的实验设施中涌出的数艾字节数据。对现代研究的要求前所未有。前沿 AI 可以帮助应对这些挑战，同时加速突破性的科学发现。 去年 12 月，我们分享了对白宫“创世纪任务”（Genesis Mission）的承诺——这是一项国家努力，旨在利用 AI 在十年内将美国科学发现的速度提高一倍。此后，Google DeepMind（GDM）宣布了一项早期访问计划，向全部 17 个美国能源部（DOE）国家实验室提供 AI 科学工具，Google Public Sector 也分享了 Gemini for Government 如何作为 DOE 的 AI 骨干。 今天，在 DOE Genesis Mission Summit 2026 上，我们进一步扩大这一承诺，为支持 Genesis Mission 的研究人员提供 4000 万美元的 AI tokens 和云 credits。 在这一扩大后的承诺下，我们将首先向 DOE 的 Genesis Mission 获奖者提供 GDM 前沿 AI 科学产品组合的实物访问权限，包括： 其次，

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ock Designs: I. The Method of Paired Comparisons. Biometrika 39(3/4):324–345, 1952. JSTOR 2334029; [11] Qwen Team. Qwen3 Technical Report. arXiv:2505.09388, 2025; [12] Gemma Team, Google DeepMind. Gemma 4. Model card, 2026. ai.google.dev/gemma · HF: google/gemma-4-31B-it; [13] Preferred Networks & NICT. PLaMo 3. Our reward models are built on an internal PLaMo-3 checkpoint, post-trained in-house from the publicly released base models plamo-3-nict-8b-base and plamo-3-nict-31b-base. 2026. HF: 8B b

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y, and at scale. By learning from over a trillion minutes of unlabeled sensor data, it generalizes across cardiovascular, metabolic, sleep, and mental health; supports label-efficient adaptation and robust daily-metric estimation; can be specialized automatically by an agentic classroom; and can ground a Personal Health Agent in a person’s own signals. 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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Risks of Preventive Attack in the Race for Advanced AI | RAND; Frontier lab safety policies/commitments Responsible Scaling Policy | Anthropic; OpenAI — Preparedness Framework v2; Google DeepMind — Frontier Safety Framework v3.0; xAI — Risk Management Framework (RMF); Meta — Frontier AI Framework; Lab governance scrunity Anthropic is Quietly Backpedalling on its Safety Commitments — LessWrong; Holden Karnofsky on dozens of amazing opportunities to make AI safer — and all his AGI takes | 80,000 H

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s of covariate shift in training LM agents on long-horizon tasks, as well as evaluating safety risks posed by LM agents in multi-agent settings. What's next: Research Scientist at Google Deepmind Qiyang Li Email: qcli@berkeley.edu Website: https://colinqiyangli.github.io/ Advisor(s): Sergey Levine Research Blurb: Recent progress in robotic manipulation policy learning has been largely driven by (1) the increasing availability of large-scale prior datasets and (2) the success of action chunking,

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n-device LLM efficiency, with Filippo Galgani, Omri Homburger, Pooja Consul, Matthew Markwell, and Vivek Kumar. Certain elements were built on developments from the Gemini team in Google DeepMind: Tal Schuster, Ziwei ji, Ivan Korotkov, and Ganesh Jawahar. We’d also like to extend a big thank you for reviews and valuable feedback and support to Nadav Bar, Utku Evci, Nir Shabat, Joe Zou, and teams in Google Research, Google Deepmind, and Platforms & Devices. Compared to Pixel 9 and Pixel 10 phones

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(AGI) and the future of robotics” as a source of bonus points for prospective hires; For the sake of this post, “leading foundation model labs” include OpenAI, Anthropic, xAI and Google DeepMind. This is based on previous work looking at AI job postings; These numbers assume zero years of experience, if no experience requirement is stated. But the US-China gap in required prior experience persists even if we restrict ourselves to postings that give an explicit figure — 5.5 years for US labs com

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for Science is a suite of experimental tools designed to expand the scale and precision of scientific exploration — developed in close collaboration with teams from Google Cloud, Google DeepMind and Google Labs. One of the new tools in Gemini for Science, Computational Discovery , is an agentic research engine built with ERA and AlphaEvolve . The Computational Discovery prototype generates and scores thousands of code variations in parallel, enabling scientists to rapidly test multiple hypothes

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m to dynamically generate entirely new visuals in real time. This is possible, she explained, because of "a novel real-time code generation system we built in partnership with the Google DeepMind team" that runs on Gemini 3.5 Flash. Generative UI capabilities will roll out to everyone this summer, free of charge. The redesign also opens the door to what Google calls " information agents " — AI agents that users can configure directly within search to monitor the web 24/7 for specific conditions

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