No one could accuse Demis Hassabis of dreaming small.
In 2016, the company he co-founded, DeepMind, shocked the world when an artificial intelligence model it created beat the best human player of the strategy game Go. Then Hassabis set his sights even higher: in 2019, he told colleagues that his goal was to win Nobel prizes with the company’s AI tools.
Chemistry Nobel goes to developers of AlphaFold AI that predicts protein structures
It took only five years for Hassabis and DeepMind’s John Jumper to do so, collecting a share of the 2024 Nobel Prize in Chemistry for creating AlphaFold, the AI that revolutionized the prediction of protein structures.
AlphaFold is just one in a string of science successes that DeepMind has achieved over the past decade. When he co-founded the company in 2010, Hassabis, a neuroscientist and game developer, says his aim was to make “a world-class scientific research lab, but in industry”. In that quest, the company sought to apply the scientific method to the development of AI, and to do so ethically and responsibly by anticipating risks and reducing potential harms. Establishing an AI ethics board was a condition of the firm’s agreement to be acquired by Google in 2014 for around US$400 million, according to media reports.
Now Google DeepMind is trying to replicate the success of AlphaFold in other fields of science. “We’re applying AI to nearly every other scientific discipline now,” says Hassabis.
Demis Hassabis co-founded DeepMind in 2010.Credit: Antonio Olmos/Guardian/eyevine
But the climate for this marriage of science and industry has changed drastically since the release of ChatGPT in 2022 — an event that Hassabis calls a “wake-up moment”. The arrival of chatbots and the large language models (LLMs) that power them led to an explosion in AI usage across society, as well as a scramble by a growing number of well-funded competitors to achieve human-level artificial general intelligence (AGI).
Google DeepMind is now racing to release commercial products — including iterations of the firm’s Gemini LLMs — almost weekly, while continuing its machine-learning research and producing science-specific models. The acceleration has made doing responsible AI harder, and some staff are unhappy with the firm’s more commercial outlook, say several former employees.
All of this raises questions about where DeepMind is headed, and whether it can achieve blockbuster successes in other fields of science.
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