Machine learning is not about getting insight into what intelligence is (it might do so as a byproduct but very few people are using it with that goal in mind).

Machine learning is not about getting insight into what intelligence is (it might do so as a byproduct but very few people are using it with that goal in mind). However, ML is useful to generalist science as long as you are be aware of its shortcomings and not just trying to replace something with ML without thinking about it. To give you an example I worked on (to be published): I worked with some physicists that use an incredibly slow and expensive iterative solver to get information on particules. We introduced a machine learning algorithm that predicts the end result. It does not replace the solver (you could not trust its results, contrary to a physics based numerical algorithm) but, using its guess as a starting point for the iterative solver, you can make the overall solving process orders of magnitude faster.