DeepMind researchers unveiled “FunSearch,” using Large Language Models to generate new mathematical and computer science solutions. FunSearch combines a pre-trained LLM to create code-based solutions, verified by an automated evaluator, refining them iteratively. It has successfully provided novel insights into key mathematical problems and demonstrated potential in broad scientific applications, marking a transformative development in algorithmic discovery.
DeepMind’s FunSearch: A Breakthrough in Mathematical Problem-Solving
DeepMind researchers have introduced FunSearch, a groundbreaking method that utilizes Large Language Models (LLMs) to uncover new solutions in mathematics and computer science. FunSearch combines a pre-trained LLM with an automated evaluator to generate inventive code-based solutions and verify their accuracy.
Practical Application
In practical terms, FunSearch is like a collaboration between a very creative thinker (the LLM) and a strict fact-checker, working together to find innovative answers to complex problems. This iterative process allows initial ideas to evolve into verified new knowledge, providing novel insights into key mathematical problems such as the cap set problem and bin-packing problem.
Tackling the Cap Set Problem
FunSearch has demonstrated remarkable success in solving the cap set problem, a complex challenge in mathematical theory. By generating program-based solutions, FunSearch has identified larger cap sets than previously known, representing a significant leap in solving a problem that has puzzled mathematicians for decades.
Potential Uses
Beyond theoretical mathematics, FunSearch has shown its versatility by applying its methodology to the bin-packing problem. The potential applications of FunSearch extend to a wide range of scientific problems, indicating a promising future for human-machine interaction in mathematics and algorithmic discovery.
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