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Google DeepMind published an announcement titled "Introducing Gemini 3.7 Flash." No article text or further details were provided in the source, so the model's capabilities, release timing, and availability cannot be confirmed from this item.
Google, via DeepMind, has committed $40 million in AI tokens and compute credits to support the Genesis Mission, an effort framed as accelerating scientific discovery using AI. The announcement comes from Google DeepMind and specifies the contribution as tokens and credits rather than direct cash grants.
Google DeepMind introduced Gemini 3.5 Flash Cyber, described as a lightweight cybersecurity model intended to find and patch software vulnerabilities.
Google, via its DeepMind unit, and the Atal Innovation Mission (AIM) launched ATL Saathi, a Gemini-powered AI tool intended to empower educators working in Indian robotics labs.
The Trump administration released a 28-page AI Action Plan and an executive order titled “Preventing Woke AI in the Federal Government” that urges federal procurement to prioritize AI systems described as "truthful" and calls for reviewing Biden-era AI rules to remove references to misinformation, Diversity, Equity, and Inclusion, and climate change. Media commentary warns the directive could pressure companies to align model behavior with the administration’s political definitions of truth; so far major AI firms have not publicly objected, with some offering positive or neutral responses.
Google researchers report that all next-word-prediction neural LMs in Gboard are now trained with federated learning and formal differential privacy guarantees, with over 30 on-device models across 7+ languages and 15+ countries. The deployed models satisfy (ε,δ)-DP with δ=1e-10 and ε ranging from 0.994 to 13.69; the post describes practical techniques used (DP-FTRL, secure aggregation, adaptive clipping, pretraining on C4) and states this is, to their knowledge, the largest production deployment of user-level DP and the first announced ε<1 for models trained directly on user data.
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