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’.<p>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 (<a href="https://osf.io/fk3xr/" rel="nofollow">https://osf.io/fk3xr/</a>).<p>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.