Communications of the ACM
Google DeepMind Used a Large Language Model to Solve an Unsolvable Math Problem
FunSearch (so called because it searches for mathematical functions, not because it’s fun) continues a streak of discoveries in fundamental math and computer science that DeepMind has made using AI.
Credit: Stephanie Arnett/MIT Technology Review
Google DeepMind has used a large language model to crack a famous unsolved problem in pure mathematics. In a paper published in Nature today, the researchers say it is the first time a large language model has been used to discover a solution to a long-standing scientific puzzle—producing verifiable and valuable new information that did not previously exist. "It's not in the training data—it wasn't even known," says coauthor Pushmeet Kohli, vice president of research at Google DeepMind.
Large language models have a reputation for making things up, not for providing new facts. Google DeepMind's new tool, called FunSearch, could change that. It shows that they can indeed make discoveries—if they are coaxed just so, and if you throw out the majority of what they come up with.
FunSearch (so called because it searches for mathematical functions, not because it's fun) continues a streak of discoveries in fundamental math and computer science that DeepMind has made using AI. First AlphaTensor found a way to speed up a calculation at the heart of many different kinds of code, beating a 50-year record. Then AlphaDev found ways to make key algorithms used trillions of times a day run faster.
From MIT Technology Review
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