The central problem with the present ‘machine learning’ and ‘big data’ hype is that so many think that they can get away with analysing real-world phenomena without any (commitment to) theory. But — data never speaks for itself. Without a prior statistical set-up, there actually are no data at all to process. And — using a machine learning algorithm will only produce what you are looking for.
Machine learning algorithms always express a view of what constitutes a pattern or regularity. They are never theory-neutral.
Clever data-mining tricks are not enough to answer important scientific questions. Theory matters.
Do you agree?