Bored with beating human chess players, DeepMind has been trackling mathematical problems.

The lab said it developed a way to automate the discovery of algorithms that act as shortcuts when multiplying matrices.

Mathematicians have been applying algorithms to perform array multiplications, some of which are used in computer science, particularly in machine learning and AI.

DeepMind researcher Alhussein Fawzi and his colleagues created AlphaTensor, that plays a game in which the goal is to find the best approach to multiplying two matrices. If the AI agent does well, it is reinforced to make future success more likely.

This process is repeated so that agent generates interesting and improved ways to multiply matrices. It's said that DeepMind's agent was challenged to complete matrix math work in as few steps as possible, and had to figure out the best way forward from potentially trillions of possible moves.

Fawzi told a press briefing this week the work was complex though resulted in the development of algorithms for matrix operations that have not been improved on in more than 50 years of human research, he said.

The researchers claimed the techniques could benefit computational tasks that use matrix multiplication algorithms – such as AI – as well as demonstrate how reinforcement learning can be used to find new and unexpected solutions to known problems, while also noting some limitations.