AI discovers new solutions to 2 famous math problems

This announcement is hot on the heels of another DeepMind paper that used AI to discover hundreds of new materials.

Artificial intelligence and machine learning show tremendous potential to advance scientific knowledge by automating discovery and problem-solving techniques. However, most current AI systems have limitations that prevent the independent generation of truly novel and verifiable factual discoveries. A new research paper by DeepMind (paper link) introduces an innovative new technique called FunSearch that addresses some of these challenges and may help realize AI's potential for driving progress in mathematics. In fact, the authors claim that FunSearch has already discovered new solutions to two famous math problems. Let’s see exactly how that came to be and evaluate how significant these findings are!

The Challenge of AI-Driven Scientific Discovery

Large pre-trained language models have achieved superhuman performance on many language-based tasks. However, as these systems have not experienced the world in the way humans have, they lack a comprehensive understanding of physics and cause-and-effect relationships. For this reason, their ability to autonomously generate new factual scientific insights is limited.

Prior work has shown that language models can "hallucinate" incorrect information when prompted to speculate or hypothesize without constraints. While creativity is useful, unfettered generation of inaccurate claims would not constitute valid scientific discovery. For an AI system to make a legitimate contribution, it must present solutions that are objectively verifiable as true through empirical testing or logical proof.

The research problem FunSearch aims to address is how to harness the generative capabilities of large language models while avoiding incorrect or unverifiable ideas. Directly querying models to provide hypothesized discoveries risks hallucination and leaves subjective human judgement as the sole arbiter of what is deemed factual. A more rigorous approach is needed to systematically develop, refine, and validate potential discoveries in silico.

The FunSearch Methodology

To navigate this challenge, FunSearch introduces a novel methodology for evolutionary AI-driven scientific discovery. In fact, I'd say they actually combine two interesting techniques.