搜索“DeepMind”

ee 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“machines

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h, 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; Gu, A.,

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o which he contributes. Looking at Tibo’s career since, it seems like a well-timed lesson! 2. Google had a “ChatGPT-like” project a year before OpenAI. At the start of the decade, DeepMind was largely focused on games and reinforcement learning, but some members, including Tibo, reckoned that scaling language models might lead to artificial general intelligence (AGI). Their “chat with an LLM” project spread like wildfire internally, but for some reason the product was never launched. Who knows w

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I 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. Inception Lab

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scrutiny or as much debate about what their performance and economics would look like outside the company that designed them. Anthropic being the biggest user of TPUs, surpassing Deepmind’s own use by 2029. Google’s internal success was never the question. The question was how much of that advantage the rest of the industry could actually get. Could you take an open-weight model, serve it through a familiar inference engine, and beat NVIDIA on the economics that matter to your business? Today,

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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 was litt

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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 down to 5

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g 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 perform ta

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the weights for Muse Spark. Gemini 3.8 Flash keeps 3.7’s pricing ($0.75 / $3.75) with gains in coding, reasoning, and agentic tasks, slotting in at a 59 on the Intelligence Index. DeepMind’s Koray Kavukcuoglu admitted Gemini sits “a little below the frontier”, and said, “there’s nothing other than being at the frontier that’s important for us.” Why it matters: Meta continues to climb with an impressive blend of intelligence and cost (something Google used to own), making Watermelon a potential i

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o began his PhD at MIT in 2020 in the Department of Electrical Engineering and Computer Science (EECS). Hong has been captivated with reinforcement learning since discovering that DeepMind could play Atari and learn from raw screen pixels via feature engineering. During his graduate work with EECS Associate Professor Pulkit Agrawal, who is also a principal investigator with the lab, Hong sought to build on this: improving value function learning for reinforcement learning in video games, using “

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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 ensuring un

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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 agents wit

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bs and robots. The details: Generalist raised nearly $200M in fresh funding led by 8VC, extending June’s $400M Series B and bringing the round to a hefty $600M. Founded in 2024 by DeepMind and Boston Dynamics veterans, the startup is building intelligence designed to work across different robot form factors. Its GEN-1.5 model learns unfamiliar physical tasks from a single demo, with Generalist showing examples learned from 12s of data, without fine-tuning. Skild AI, valued above $14B, and Physic

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a 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 evaluation, t

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e’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 unwatermarked an

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nthropic 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 Preview’s a

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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 Lunts, J

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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 and propr

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tudent 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 researchers’

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t 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] Mahmoud A

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