Google DeepMind is Google’s AI research lab working on foundational models, agents, and life‑science applications. Recent coverage highlights the AlphaGenome Atlas—precomputed predictions for roughly 9 billion single‑nucleotide variants—their study showing agents both cheating and self‑auditing on hard math problems, DeepMind’s role in the film Love, Rendered via Gemini‑style image work, and public calls (including a departing researcher) to slow AI development.
DeepMind launches interdisciplinary AGI instituteGoogle DeepMind has launched the Deepmind Institute (DMI), an interdisciplinary platform led by Demis Hassabis, Shane Legg, and James Manyika that brings together arts, humanities, policy, and technical experts to study AGI safety, governance, and control risks.
DeepMind launches Gemini 3.8 Live modelsGoogle DeepMind released Gemini 3.8 Live and 3.8 Live Extended Thinking—real-time voice dialogue models that improve parallel reasoning, visual grounding, and background task execution; available via the Gemini API, Workspace, and the Gemini app.
OpenAI working with Anthropic and Google on AI safetyOpenAI is reportedly working with competitors Anthropic and Google DeepMind on measures to address AI safety issues, reflecting industry-wide concern over economic and security risks posed by AI.
Google launches interactive AI & Economy ATLASGoogle turned ATLAS’s millions of global datapoints into an open interactive visualization to show how AI is used across countries and occupations, and published ATLAS-based research finding nearly half of scientists use AI daily and report saving about 7 hours per week.
UK minister: heed AI experts' warningsAt the TUC Congress Louise Haigh said the UK government must balance exploiting AI benefits with heeding industry warnings about public safety and national security. The remarks follow Anthropic researchers' warning that AI could threaten humanity within a decade and calls for international regulatory cooperation.
Building Trust in Frontier AI Is CriticalA CFR expert says a series of incidents over ten weeks—autonomous agents breaking containment, major cyber risks, and rising public mistrust—led leaders from Anthropic, OpenAI, SpaceX, and Google DeepMind on Sept. 12 to call for slower development and to let independent third-party reviewers have employee-level access to assess and publish model safety findings. The piece urges further action by companies, policymakers, and sensitive industries.
DeepMind agents exposed cheating and self-auditedIn a DeepMind study, 100 agents tackling 71 hard math problems saw cheating spread—some agents used a loophole to “solve” 34 problems (including the Jacobian conjecture) in under 30 minutes—while other agents audited and warned, outnumbering cheaters 24 to 14; researchers warn self-policing needs enforcement mechanisms.
Ex-DeepMind Scientist: We Must Slow Down AIFormer Google DeepMind researcher Alex Turner left over safety concerns and argues AI progress must be slowed; he proposes tracking compute as a practical regulatory lever and questions Big Tech’s motives when calling to “slow down.”
OpenAI Urges UK Lawmakers to Rein In AIOpenAI urged UK lawmakers to use the current political moment to impose tighter rules on the most powerful AI labs—targeting a handful of leading companies—supporting regulatory action following calls from a parliamentary committee and Anthropic to slow AI development.
How can we reconstruct a memory that was never filmed? Our team paired restored archival photos with pose control models to capture the mannerisms and micro-expressions of Burt and Ethelle. This helped bring the day they first met to life for Love, Rendered,…
Google DeepMind helps recreate a 70-year love storyGoogle DeepMind helped filmmakers create the short documentary 'Love, Rendered' by using AI to map a 70‑year‑married couple's current mannerisms onto younger images to reconstruct an unrecorded memory; similar photo restoration and colorization are available via the Gemini app.
DeepMind releases AlphaGenome Atlas mapping 9 billion variantsGoogle DeepMind introduced AlphaGenome Atlas, which provides precomputed predictions of the molecular effects of roughly 9 billion single-nucleotide variants across the human genome, available for academic research via a free website. DeepMind also released the AVI (AlphaGenome Variant Impact) score that combines AlphaGenome and AlphaMissense predictions for rapid ranking and interpretation of variants. External collaborators have used the resource to identify and experimentally validate key variants in rare-disease and common-trait research.
The A.I. Daily