Previous algorithms required 80 individual multiplications to multiply a 4x5 matrix by a 5x5. Researchers at artificial intelligence (AI) laboratory DeepMind have created an algorithm that can solve tough mathematical calculations with improved computing efficiency. The AlphaTensor algorithm, described in the journal Nature, was designed to execute matrix multiplication, which entails multiplying numbers arranged in grids that might represent data. AlphaTensor incorporates reinforcement learning as well as tree search, a game-playing approach in which the AI probes the outcomes of branching possibilities while planning its next action. AlphaTensor was tested on input matrices up to 5 x 5.

AlphaTensor was designed to perform matrix multiplications, but the same approach could be used to tackle other mathematical challenges.Credit: DeepMind Researchers at DeepMind in London have shown that artificial intelligence (AI) can find shortcuts in a fundamental type of mathematical calculation, by turning the problem into a game and then leveraging the machine-learning techniques that another of the company's AIs used to beat human players in games such as Go and chess. The AI discovered algorithms that break decades-old records for computational efficiency, and the team's findings, published on 5 October in Nature1, could open up new paths to faster computing in some fields. "It is very impressive," says Martina Seidl, a computer scientist at Johannes Kepler University in Linz, Austria. "This work demonstrates the potential of using machine learning for solving hard mathematical problems." Advances in machine learning have allowed researchers to develop AIs that generate language, predict the shapes of proteins2 or detect hackers.