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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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losely-related problems,” they wrote. ( 444 ). Centaur math discovery: Researchers with the University of British Columbia, University of New South Wales, Stanford University, and Google DeepMind published a new math proof which was built in close collaboration with some AI-based math tools built at Google. “The proofs of the main results were discovered with very substantial input from Google Gemini and related tools,” they wrote. ( 441 ). If you squint, you could argue that this is a sign that

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e excited to partner with Adaption to make Together Fine-Tuning available in Adaptive Data . Adaption is co-founded by Sara Hooker and Sudip Roy, both former leaders at Cohere and Google DeepMind veterans. Adaptive Data addresses the data challenges of modern model training by helping teams analyze dataset structure, adapt examples, evaluate quality, and export model-ready data. Adaption describes this set of capabilities as bringing data optimization techniques typically reserved for frontier l

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ers to easily access automatically re-composed images as a seamless, single-action enhancement within the Auto frame feature. This feature is the result of a collaboration between Google DeepMind and Google Platforms & Devices teams. Key contributors include: Thiemo Alldieck, Marcos Seefelder, Hannah Woods, Pedro Velez, Michael Milne, Bert Le, Navin Sarma, Jasmin Repenning, and Selena Shang. Advisors include: Steven Hickson, Claudio Martella, Irfan Essa, and Alex Rav Acha. Special thanks to: Mik

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ing to narrow the gap between lower and upper bounds of what's possible in theory and what anyone has actually constructed. Dimension 11 is one of those open frontiers. Last year, Google DeepMind's AlphaEvolve made a significant advance, pushing the lower bound to 593 from 592, meaning at least 593 spheres can be arranged to kiss a central sphere in 11-dimensional space. Agents on EinsteinArena started to make incremental progress on this challenging problem. Then on April 8th, one agent, alpha

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inference, and driving the evolution of open models and open infrastructure. Banghua presenting at Novita's GTC event. Partners represented included NVIDIA, RadixArk, OpenRouter, Google DeepMind, Kimi (Moonshot AI), Alibaba Cloud, MiniMax, Z.ai, Hugging Face, and Kilo Code. On Wednesday evening, we hosted approximately 200 engineers at LinkedIn's Mountain View headquarters alongside teams from LinkedIn, TikTok, Meta, and NVIDIA for a deep dive into production LLM systems for search and recommen

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le's scale faster and more efficient. As AI becomes more integrated into all products, from LLMs to semantic search, this work in fundamental vector quantization will be more critical than ever. This line of research was conducted in collaboration with Praneeth Kacham, researcher at Google; Majid Hadian, Principal Engineer at Google DeepMind; Insu Han, Assistant Professor at KAIST; Majid Daliri, PhD student at NYU; Lars Gottesbüren, researcher at Google; and Rajesh Jayaram, researcher at Google.

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transforming the technology from a tool into a true collaborator for healthcare providers. A prime example is AMIE , a research multi-agent system developed by Google Research and Google DeepMind. Our latest research has found that it can interpret and reason across medical histories, lab results, and complex medical images. It identifies patterns that might otherwise be overlooked by analyzing the entire map of a patient's health simultaneously. We’re now testing AMIE in clinical research setti

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controlled comparisons . This project was an extensive collaboration between Beth Israel Deaconess Medical Center , Beth Israel Lahey Health , and many teams at Google Research , Google DeepMind and Google for Health . We thank our many collaborators, sponsors and reviewers of this work. We are grateful to the many contributions from our co-authors throughout this research: Peter Brodeur, Jacob M. Koshy, Anil Palepu, Khaled Saab, Ava Homiar, Roma Ruparel, Charles Wu, Ryutaro Tanno, Joseph Xu, A

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