This is a guest essay by Jessica Hullman, Ginni Rometty Professor of Computer Science and Policy Fellow at Northwestern University.
In his essay on the role that rigorous proofs play in mathematical advances, Bill Thurston writes that the measure of success in math research is “whether what we do enables people to understand and think more clearly and effectively about mathematics.” Many recent discussions of AI for science hinge on a tension between understanding and answers. Is science about humans achieving insight, or the archiving of verifiable solutions?
One side prioritizes outputs. Progress is implied to be evident in verifiable proofs of longstanding conjectures and cures for diseases, whether humans were involved in their making or not. The other side argues that human understanding is paramount, because it drives integration of new findings into the canon, because it shapes and inspires the next generation, and so on. But the first side, those who emphasize correct outputs, dismisses this as gatekeeping or nostalgia.
