The taxonomy problem: what naming 640 ML concepts taught me about the field

When you have to name 640 machine learning concepts and decide how they relate to each other, the field starts looking different. Not because the concepts change. But because the act of organizing them forces you to make decisions the ML literature has quietly avoided. The boundary problem Where does "mechanistic interpretability" end and "feature visualization" begin? Both involve understanding what neural networks compute. Both involve identifying circuits or features that drive specific behaviors.