Demis Hassabis grew up in North London. A child prodigy in chess, Hassabis reached master standard at the age of 13 with an Elo rating of 2300 (at the time the second highest rated player in the world Under-14 after Judit Polgár who had a rating of 2335) and captained many of the England junior chess teams.

After graduating from Cambridge, Hassabis worked at Lionhead Studios. In 1998, he left the company and found Elixir Studios, a London-based independent games developer. In addition to managing the company, which he grew to 60 people, Hassabis served as executive designer of the BAFTA-nominated games, Republic: The Revolution and Evil Genius.

He earned a PhD in cognitive neuroscience at University College London, where he sought to find inspiration in the human brain for new AI algorithms. Hassabis then pursued postdoctoral work at MIT and Harvard before earning a Sir Henry Wellcome postdoctoral fellowship to continue his research at UCL.

Working in the field of autobiographical memory and amnesia, he co-authored several influential papers published in Nature, Science, Neuron and PNAS. Hassabis also developed a new theoretical account of the episodic memory system identifying scene construction, the generation and online maintenance of a complex and coherent scene as a key process underlying both, memory recall and imagination. This work received widespread coverage in the mainstream media and was listed in the top 10 scientific breakthroughs of the year in any field by the journal Science.

In 2010, Hassabis co-founded DeepMind Technologies, a London-based machine learning AI startup, with Shane Legg and Mustafa Suleyman. Hassabis and Suleyman had been friends since childhood, and he met Legg when both were postdoctoral students at University College London’s Gatsby Computational Neuroscience Unit. Hassabis also recruited his university friend and Elixir partner David Silver.

DeepMind’s mission is to ‘solve intelligence’ and then use intelligence ‘to solve everything else’. More concretely, DeepMind aims to meld insights from neuroscience and machine learning with new developments in computing hardware to unlock increasingly powerful general-purpose learning algorithms that will work towards the creation of an artificial general intelligence (AGI).