Alhussein Fawzi

Alhussein Fawzi, 34, is pioneering the use of game-playing AI to speed up fundamental computations. Small improvements to popular algorithms can make a huge difference, cutting costs and saving energy across every device that runs them.

But identifying shortcuts in code that has been studied by human scientists for decades is hard. Fawzi’s key insight was to treat the problem of finding new algorithms as a kind of game—and use DeepMind’s game-playing AI, AlphaZero, to master it.

To make moves in a game like chess, AlphaZero searches through an astronomical number of possibilities before picking a move that is most likely to lead to a win. Lining up the sequential steps in a correct algorithm is a little like choosing moves in a winning game. Like chess, it involves scouring through countless possibilities to reach a goal.

Using an adapted version of AlphaZero, Fawzi and his colleagues found a way to speed up matrix multiplication, a fundamental element of math at the heart of many common computer programs in areas from graphics to physics to machine learning itself. They discovered algorithms that were faster than the previous best human-devised ones, beating a record that had stood for 50 years.

Google DeepMind has also used Fawzi’s approach to discover previously unknown shortcuts in sorting algorithms, another fundamental computation that runs trillions of times a day.

“It’s astounding when you think that many of the basic algorithms that we use today were really invented before the era of modern computers, most of them on paper,” says Fawzi. “There’s mileage in using machine learning to try to improve on them.”