Just as the queen is the most important piece in chess, with its ability to move anywhere across the board, DeepMind founder and chief executive Demis Hassabis is at the centre of an emerging world order where artificial intelligence (AI) will impact every job, industry and country.

A world-class chess player himself, Hassabis is the winner of the 10th edition of the UKtech50, Computer Weekly’s annual list of the most influential people in UK technology. A researcher, neuroscientist, video game designer and entrepreneur, Hassabis founded machine learning AI startup DeepMind in 2010, alongside his friends Shane Legg and Mustafa Suleyman. The startup was acquired by Google in 2014 for about £400m.

A child prodigy, Hassabis was taught how to play chess at the age of four, and by age 12 he was a chess master, representing England in tournaments around the world.

Speaking at a Google Zeitgeist event in 2015, he recalled that his musings about how the mind works also started at an early age.

“If you have a quiet, reflective personality like I had when I was young, you can’t help but think and introspect about what it is about your mind that allows you to come up with these moves in such a complex game as chess,” he said.

Hassabis started programming on a ZX Spectrum 48K computer at the age of eight, his first achievement at coding being an application that could play chess. This was what led one of the most respected entrepreneurs in the world onto the beginning of his path towards AI.

Shortly after graduating in computer science at the University of Cambridge in the late 1990s, Hassabis went to work at games developer Lionhead Studios, where he was the lead AI programmer for the game Black & White.

That brief stint was followed by his first experience as an entrepreneur, as the founder of his own games venture, Elixir Studios. The company released a number of games with various levels of success during its lifespan of about seven years, before selling its intellectual property and folding in early 2005.

A focus on neuroscience There are only two subjects worth studying, according to Hassabis: physics and neuroscience. The latter was the subject he embraced in his PhD from University College London (UCL), as Hassabis returned to the academic world following the end of Elixir. “While physics is all about explaining the external world, including the entire universe, neuroscience and psychology is, conversely, all about explaining what’s inside our internal world,” Hassabis told the Google Zeitgeist event. “One of the things I’m excited about with artificial intelligence [is that] I think it will help us understand our minds better” Demis Hassabis, DeepMind “When I thought about this more, I came to the conclusion that the mind was more important, because, obviously, that’s the way we interpret the external world out there,” he added. “And that’s one of the things that I’m excited about with artificial intelligence, as I think ultimately it will help us understand our minds better,” said Hassabis, citing one of his greatest scientific heroes, US theoretical physicist Richard Feynman, who defended the view that, to understand something, one has to be able to recreate it. The years between the end of the 2000s and start of the 2010s were academically intense for Hassabis, who also earned a Henry Wellcome postdoctoral research fellowship to the Gatsby Charitable Foundation Computational Neuroscience Unit of UCL. He focused on the field of autobiographical memory and amnesia, and produced a number of highly influential papers on related subjects. Along with recognition in the academic world, where his work is seen as achieving scientific breakthroughs, Hassabis contributed to bringing themes related to the functioning of the brain to the mainstream.

Advancing artificial general intelligence The academic work in UCL led to a collaboration between Hassabis, Legg and Suleyman towards what would become DeepMind, in 2010. The company was born under the premise that neuroscience, combined with AI and machine learning, as well as other emerging technologies, could lead to powerful algorithms learning to the concept of artificial general intelligence (AGI). Setting up DeepMind was also an opportunity for Hassabis, who earned a CBE for services to sciences and technology in 2018, to work once again with his friend and partner at Elixir, David Silver, who came on board to contribute with his games expertise. The company gained prominence by training learning algorithms to master games, having notoriously achieved automated game-playing at a “superhuman level”, by using the raw pixels on the screen of Atari games as inputs. “AlphaGo successes hinted the possibility for general AI, to be applied to a wide range of tasks and areas to perhaps find solutions to problems that we as human experts may not have considered” Demis Hassabis, DeepMind Following the Google buyout, Hassabis’s company achieved further developments in the games arena, notably with AlphaGo, which famously defeated world champion Lee Sedol in the complex, ancient board game of Go. “I think AlphaGo successes hinted the possibility for general AI, to be applied to a wide range of tasks and areas to perhaps find solutions to problems that we as human experts may not have considered,” Hassabis said in an interview published by DeepMind. The concept of AGI developed by the team led by Hassabis has, in recent years, accomplished a number of other breakthroughs in areas beyond training computers to play games. This has included advancing research on AI safety and the development of a partnership with London’s Moorfields Eye Hospital for the use of artificial intelligence to identify and treat degenerative eye conditions. More recently, the company has been delving even deeper into investigating some of the most serious issues around the future of health, including work around learning to predict the 3D shapes of proteins, elements on which the human biological machinery is built. The company has already made progress with AlphaFold, its algorithm that can predict protein structures, and has inspired other systems that can predict the 3D structures of proteins from their amino-acid sequences. By cracking what leads to anomalies on proteins, Hassabis hopes to shine a light on paths that may lead to the design of new proteins, thus fighting disorders such as Alzheimer’s and diabetes.