What’s the point of doing mathematics, or training to be a mathematician, in the age of AI? Mathematician Stephen Wolfram argues that it’s partly about finding human ways to represent the truths mathematics reveals. It’s also about asking the right questions:
Great math is—more than anything else—defined by the questions it asks. Yes, the AI can successfully automate things that humans would normally have had to do themselves before. But […] t the core of pure mathematics is the human imagination that guides what questions to ask.
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At a more practical level, some part of the doing of math is about figuring out how to achieve goals that have been set—and this is something we can imagine AI doing. But what’s ultimately more important is the setting of the goals in the first place. And almost by definition, these must come from “outside the system”. Or, in particular, from us.
It’s a notable observation that the most significant reported successes for AI in math so far tend to come from some of the most skilled human mathematicians. And in some sense we should not be surprised—because it’s the setting of goals (or, in effect, knowing the questions to ask) that is the most important and unique part. Indeed, it’s often the case that once one really knows the question to ask, one’s already done the lion’s share of the work to answer it.
Related: Statistician Cosma Shalizi’s thoughts on AI as information retrieval.
