The research shows the potential of AI techniques like FunSearch to drive progress in computational mathematics and other scientific domains. By developing and refining executable programmatic expressions of discoveries without relying on human judgment, FunSearch has produced verifiably novel solutions and insights. The technique could be scaled across diverse scientific domains and could help tackle problems that are intractable through human or machine efforts alone.
Key takeaways:
- DeepMind's new research introduces FunSearch, a novel methodology for AI-driven scientific discovery that combines a pre-trained large language model with an automated evaluator to generate and refine programmatic solutions to problems.
- FunSearch was applied to two long-standing open problems: the "cap set problem" in extremal combinatorics and the "bin packing problem" in operational research, and it produced verifiably novel solutions and new combinatorial insights.
- The methodology allows for systematic development of discoveries in a manner similar to the scientific method, guided by empirical results rather than subjective impressions, and its solutions can be easily inspected, deployed, and built upon.
- While preliminary, the research demonstrates the potential of AI techniques like FunSearch to drive progress across computational mathematics and other scientific domains, and opens new avenues for leveraging AI to automate discovery processes and accelerate progress against humanity's hardest challenges.