Tbf, if you work as a research scientist - a solid part of your work is to read new papers every single day. The ML/AI field is not some unified field, so you w

Tbf, if you work as a research scientist - a solid part of your work is to read new papers every single day. The ML/AI field is not some unified field, so you will se A LOT of papers from all kinds of researchers and scientists, from all kinds of fields in the world of science. Sometimes the terms and metrics are borrowed from the (paper) authors domain, other times they're just coined on the fly - if there are no good analogies, or there are too many overlapping or closely related terms. It's like in math where you have a double-digit number of notations for the dot product - all depending on what sub-field of math you're working on. I understand WHY it is that way, but it's super frustrating - because you end up spending time to look up what the notation / terms mean, often with no real clear answer.