Ways to think about machine learning
> They address a class of questions that were previously ‘hard for computers and easy for people’, or, perhaps more usefully, ‘hard for people to describe to computers’.Those aren't the only problems.  ML can also solve problems that were previously 'hard for people and with no good algorithm for computers'.  These are problems where there is a good labeled dataset, but no good algorithm to map from data to label.  For example, the work determining sexual orientation from images (https://osf.io/fk3xr/).The problem with this approach is you get predictive ability, but no insight.  But still can be of great value, and potentially great danger too.