Google DeepMind’s AI Surpasses Average Gold Medalist in Solving Geometry Problems

A Breakthrough in AI Research

Google DeepMind, the leading AI research lab, has developed an AI system called AlphaGeometry2 that appears to have surpassed the average gold medalist in solving geometry problems in an international mathematics competition. This achievement is a significant milestone in the field of AI research, demonstrating the potential of AI systems to excel in complex mathematical tasks.

The AlphaGeometry2 System

AlphaGeometry2 is an improved version of a system, AlphaGeometry, that DeepMind released last January. The new system has been tested in a newly published study, which claims that AlphaGeometry2 can solve 84% of all geometry problems over the last 25 years in the International Mathematical Olympiad (IMO), a math contest for high school students.

The Significance of Geometry Problems

DeepMind’s interest in geometry problems lies in the potential to discover new ways to solve challenging geometry problems, particularly those related to Euclidean geometry. By solving these problems, the lab hopes to gain insights into the development of more capable AI systems.

Expert Insights

Vince Conitzer, a Carnegie Mellon University computer science professor specializing in AI, has expressed concerns about the limitations of current AI systems. "Proving mathematical theorems, or logically explaining why a theorem (e.g. the Pythagorean theorem) is true, requires common sense problems," he said. "I don’t think it’s all smoke and mirrors, but it illustrates that we still don’t really know what behavior to expect from the next system. These systems are likely to be very impactful, so we urgently need to understand them and the risks they pose much better."

A Promising Path Forward

AlphaGeometry2’s success demonstrates that combining symbol manipulation and neural networks is a promising path forward in the search for generalizable AI. The system’s ability to solve geometry problems without relying on external tools, such as symbolic engines, suggests that large language models can be self-sufficient in certain applications.

Preliminary Evidence of Self-Sufficiency

The DeepMind team has found preliminary evidence that AlphaGeometry2’s language model is capable of generating partial solutions to problems without the help of the symbolic engine. This finding supports the idea that large language models can be self-sufficient without depending on external tools, but further research is needed to fully understand the capabilities and limitations of these systems.

Conclusion

AlphaGeometry2’s achievement marks a significant milestone in the field of AI research, demonstrating the potential of AI systems to excel in complex mathematical tasks. As the field continues to evolve, it is essential to understand the capabilities and limitations of these systems and to address the risks they pose.