We study the brain and AI within a single space of principles. We ask what the brain optimizes (objective), how it updates (learning rule), and on what structure it computes (architecture). Insights from this analysis loop back into algorithms that generalize from less data, adapt faster, and behave more robustly.We extend this approach by treating theories in Korean medicine as cognitive models abstracted from experience, and by formalizing them with machine learning framework.