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HomeNatureDeepMind AI crushes tough maths problems on par with top human solvers

DeepMind AI crushes tough maths problems on par with top human solvers

A photo illustration of silhouetted figurines next to a screen displaying a logo of Google DeepMind.

Google DeepMindā€™s AI AlphaGeometry2 aced problems set at the International Mathematical Olympiad.Credit: Sebastien Bozon/AFP via Getty

A year ago AlphaGeometry, an artificial-intelligence (AI) problem solver created by Google DeepMind, surprised the world by performing at the level of silver medallists in the International Mathematical Olympiad (IMO), a competition that sets tough maths problems for gifted high-school students.

The DeepMind team now says the performance of its upgraded system, AlphaGeometry2, has surpassed the level of the average gold medallist. The results are described in a preprint on the arXiv1.

ā€œI imagine it wonā€™t be long before computers are getting full marks on the IMO,ā€ he says Kevin Buzzard, a mathematician at Imperial College London.

Solving problems in Euclidean geometry is one of the four topics covered in IMO problems ā€” the others cover the branches of number theory, algebra and combinatorics. Geometry demands specific skills of an AI, because competitors must provide a rigorous proof for a statement about geometric objects on the plane. In July, AlphaGeometry2 made its public debut alongside a newly unveiled system, AlphaProof, which DeepMind developed for solving the non-geometry questions in the IMO problem sets.

Mathematical language

AlphaGeometry is a combination of components that include a specialized language model and a ā€˜neuro-symbolicā€™ ā€” one that does not train by learning from data like a neural network but has abstract reasoning coded in by humans. The team trained the language model to speak a formal mathematical language, which makes it possible to automatically check its output for logical rigour ā€” and to weed out the ā€˜hallucinationsā€™, the incoherent or false statements that AI chatbots are prone to making.

For AlphaGeometry2, the team made several improvements, including the integration of Googleā€™s state-of-the-art large language model, Gemini. The team also introduced the ability to reason by moving geometric objects around the plane ā€” such as moving a point along a line to change the height of a triangle ā€” and solving linear equations.

An aerial view of a competing student intensely studying problems on sheets of paper during the International Mathematical Olympiad Amsterdam 2011.

The International Mathematical Olympiad is a prestigious annual competition for gifted school students.Credit: Valerie Kuypers/AFP via Getty

The system was able to solve 84% of all geometry problems given in IMOs in the past 25 years, compared with 54% for the first AlphaGeometry. (Teams in India and China used different approaches last year to achieve gold-medal-level performance in geometry, but on a smaller subset of IMO geometry problems2,3.)

The authors of the DeepMind paper write that future improvements of AlphaGeometry will include dealing with maths problems that involve inequalities and non-linear equations, which will be required to to ā€œfully solve geometryā€.

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