The problem with the AI Alphagemetry2 Aced in Google Deepmind is set in the International Mathematical Olympiad.Credit: Sebastien Bozon/AFP via Getty
A year ago, Artificial Intelligence (AI) problem solver Alphageometry, created by Google DeepMind, surprised the world by performing at the level of a silver medalist at the International Mathematics Olympiad (IMO). School students.
The Deepmind team says that the upgraded system Alphageometry2 performs better than average gold medalists. The results are explained in the arxiv1 preprint.
“I don’t think it’s long before a computer gets the full mark in IMO,” says Kevin Buzzard, a mathematician at Imperial College in London.
Solving the Euclidean geometry problem is one of four topics covered in IMO problems. Geometry requires certain skills in AI, as competitors need to provide strict evidence of statements about geometric objects on planes. In July, Alphageometry2 made its public debut along with Alphaproof, a newly announced system developed by Deepmind to solve non-geometric questions in the IMO problem set.
Mathematical Language
Alphedimetry is a combination of a special language model and components that include a “neurosymbolic” system. This is a combination of components that do not learn from data like neural networks, but have abstract inferences encoded by humans. The team trained language models to speak formal mathematical language. This allows you to automatically check logical rigor.
For Alphageometry2, the team made several improvements, including integration of Gemini, Google’s cutting-edge, large-scale language model. The team also introduces the ability to infer by solving linear equations, such as moving points along the line to change the height of the triangle by moving geometric objects around the plane. I did.
International Mathematics Olympiad is an honorable annual competition for talented school students.Credit: Valerie Keepers/AFP via Getty
The system was able to solve 84% of all geometric problems given in IMOS over the past 25 years, compared to 54% of the initial alphage measurements. (The Indian and Chinese teams used a variety of approaches last year to achieve geometric gold medal-level performance, but for a small subset of IMO geometry issues, 2,3.)
The authors of Deepmind Paper write that future improvements in alphage measurements include addressing mathematical problems with inequality and nonlinear equations.
