Machine learning used to probe the building blocks of shapes

Applying machine learning to find the properties of atomic pieces of geometry shows how AI has the power to accelerate discoveries in maths. Mathematicians from Imperial College London and the University of Nottingham have, for the first time, used machine learning to expand and accelerate work identifying 'atomic shapes' that form the basic pieces of geometry in higher dimensions. Their findings have been published in Nature Communications . This could be very broadly applicable, such that it could rapidly accelerate the pace at which maths discoveries are made. Sara Veneziale - The way they used artificial intelligence, in the form of machine learning, could transform how maths is done, say the authors. Dr Alexander Kasprzyk from the University of Nottingham said: "For mathematicians, the key step is working out what the pattern is in a given problem. This can be very difficult, and some mathematical theories can take years to discover." Professor Tom Coates , from the Department of Mathematics at Imperial, added: "We have shown that machine learning can help uncover patterns within mathematical data, giving us both new insights and hints of how they can be proved." PhD candidate Sara Veneziale , from the Department of Mathematics at Imperial, said: "This could be very broadly applicable, such that it could rapidly accelerate the pace at which maths discoveries are made.