Demis Hassabis is co‑founder and CEO of Google DeepMind, responsible for guiding the lab's research and product work. Recent coverage highlights DeepMind's launch of AlphaGenome Atlas—building on AlphaFold and able to predict the impact of 9 billion single‑letter DNA variants, freely available for academic research—and broader industry discussion about pacing and frontier‑AI standards, plus resignations at Anthropic.
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.
OpenAI boss: world 'right to be afraid' but should trust AI firmsOpenAI CEO Sam Altman said at a San Francisco conference that public fear of AI is justified but people should trust AI firms to do the right thing; his remarks followed a viral post by a departing Anthropic researcher and amid calls for slowing development and discussing regulation.
Google, OpenAI and Anthropic Discuss AI Standards BodyReports say Anthropic, Google and OpenAI have held talks about creating an industry standards body; Dario Amodei proposed the idea and it has support from Sam Altman and Demis Hassabis to promote voluntary safety auditing and testing.
Anthropic researcher quits, voices concernA 27-year-old researcher who left Anthropic said major decisions are made by a few San Francisco engineers on their laptops and described a culture of fear inside labs. The report mentions CEO Dario Amodei's pacing plan, other leaders' responses, and ongoing resignations.
Dario's essay points towards the right path forward. The details need working through, but the direction is correct for meeting this critical moment. This is also why we recently put out our proposal for an industry-wide standards body for frontier AI. https:…
With AlphaFold we mapped the protein universe - now with AlphaGenome Atlas we’re charting the human genome. It can predict the impact of all 9 billion possible single-letter DNA variants, helping scientists better understand disease. Freely available for acad…
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