New Math
AI may enable a lot more cutting-edge math to apply to real problems.
Historically, it usually takes decades to centuries for math research to become “useful.” There are obvious exceptions like calculus where the math was literally invented to solve a science or engineering problem, but the pattern usually looks more like fast fourier transforms (Gauss wrote the algorithm down in the early 1800s) being repurposed for signal processing in the 1970s, quaternions (invented in the 1840s) being the perfect representation for spacecraft and video game objects in the 1970s or 1980s, or Lie Algebras taking more than 50 years to become a key part of particle physics.
Math becoming useful for a problem requires a special confluence of
People who understand the math well enough to apply it to the problem
People who understand the problem well enough to see how the math applies to it
The computational resources to actually execute the math on real situations
AI may be able to compress the timelines for these conditions to be met and enable a lot more cutting-edge math to apply to real problems.
Consistent with Moravec’s paradox, AI has gotten pretty good at “understanding” pretty complicated math. One could imagine doing breadth-first searches of open science or engineering problems to see whether there is applicable math that has been collecting dust on a shelf could be an unlock. Furthermore, the same abundant computational infrastructure that powers the AI could potentially be part of the unlock to using that math.
The reason to do math research won’t and shouldn’t become purely utilitarian, but it might be cool if more mathematicians saw their work directly applied in their lifetimes.
We write Reinvent Science to broaden the conversation around science and science funding, and we rely on you to help us reach as many readers as possible. Please support our work by subscribing and sharing.
 | A guest post by
|