Teaching AI to build scientific models

An essay uses the Hodgkin-Huxley nerve-cell model of 1951 to break scientific modeling into three steps—building, constraining, and interpreting—and examines how AI systems are increasingly involved in each. It argues that simulation-based inference can return full distributions of plausible parameters rather than a single fit, and that LLM-based agents can propose and revise candidate models, though interpretation remains hardest to hand to AI. Because many models can explain the same data, the value lies in knowing which features are pinned down, which alternatives failed, and what remains open.