Geordie Williamson, a mathematician at the University of Sydney who worked with Charton on PatternBoost, has not yet tried Axplorer. But he is curious to know what mathematicians do with it. (Williamson still occasionally collaborates with Charton on academic projects but says he is not otherwise affiliated with Axiom Math.)
Williamson says Axiom Math has made several improvements to PatternBoost that (in theory) make Axplorer applicable to a wider range of math problems. “It remains to be seen how significant these reforms will be,” he says.
“We’re in a weird time right now, where a lot of companies have tools they want us to use,” Williamson added. “I would say mathematicians are somewhat overwhelmed by the possibilities. It’s not clear to me what impact having another tool like this would have.”
Hong acknowledged that many AI tools are currently being pushed on mathematicians. Some require mathematicians to train their neural networks. That’s a turnoff, says Hong, himself a mathematician. Instead, Explorer will walk you step-by-step, she says.
Explorer’s code is open source and Available via GitHub.. Hong hopes that students and researchers will use the tool to generate sample solutions and sample responses to the problems they are working on, and accelerate mathematical discovery.
Williamson welcomes the new tools and says he uses the LL.M. a lot. But he doesn’t think mathematicians should throw out whiteboards just yet. “In my biased opinion, Pattern Boost is a lovely idea, but it’s certainly not a panacea,” he says. “I would prefer that we no longer forget the down-to-earth ways.”