As someone with degrees in statistics who now works in ML, all of these simplistic definitions are pithy, but useless. The reality is that the overlap between t

As someone with degrees in statistics who now works in ML, all of these simplistic definitions are pithy, but useless. The reality is that the overlap between the two fields is so enormous that trying to come up with a clean separation for the two is a losing battle. To say that statisticians are uninterested in “the answers” (I.e. predictive accuracy) is foolish and rarely true. To say that ML doesn’t care about parameters (I.e. inference) ignores the vast amount of effort in ML researching topics like causal inference, feature importance, and yes, inference. At this point I’m convinced the debate regarding what is statistics vs what is ML is largely just tribal warfare. The two parties are fighting over shared territory, and while both may have had some claim to uniquely identifying techniques and research, the lines have long since eroded.