Coming from a ML domain, I feel there is so much terminology and its duplication floating around which could be better explained, maybe with an example or a pic

Coming from a ML domain, I feel there is so much terminology and its duplication floating around which could be better explained, maybe with an example or a picture (or even a simple howto). The technical debt is just too high. For example, hundreds of papers talking about 'inductive bias' or 'SE(3) operators' and what not. There isn't even a general boundary between unsupervised and self supervised learning (Remember Yann Lecun stepping in one such debate but without a conclusively clear demarcation). Very few actually try to explain a view that general practitioner can connect (with some basic exposure). Google maintains a ML glossary but that sorely needs some updation beyond the very basic stuff.