I have a question. When you say that you are going to write a book titled "When Machine Learning Fails", is your book going to based on the boundary, the limit

I have a question. When you say that you are going to write a book titled "When Machine Learning Fails", is your book going to based on the boundary, the limits of machine learning or is it going to talk about obvious limitations and examples? Will it have interesting, insightful case studies, or the regular toxic, baseless anti-ML stuff? ° ° ° | ° ° ° ° ° ° | ° ° ° ° | ° ° | ° ° ° | ° ^ ° | |B° ^ | A ML Applies ML Fails Are we going to get stuff about points in A or trivial stuff like B? Will this be a lesson for MBAs so they can steer away from use of ML in projects, or the limitations of ML in an ML 101 course? OR Will this be on ML theory and practice deep enough to teach ML/DL practitioners new lessons and insights?