[raincom]

The so-called data-driven science have not understand the notion of science. In a minimal sense, science is to produce knowledge. There are two things to it: hypothesis generation; testing the hypothesis. As the history and philosophy of sciences have shown, there is no algorithmic way of generating hypotheses. Or if you generate hypotheses algorithmically, you are still left to figure out whether these hypotheses are ad hoc or not. After all, the history of sciences have given powerful heuristics to reduce the solution space to generate hypotheses to solve or explain problems or facts. Here, whether one picks 'solve' or 'explain' depends on which philosophy of science one picks up.

Whenever I see statistics and data-sciences, I see tons of adhoc bullshit masquerading as sciences/knowledge. It is always easy to come up with a hypothesis to explain a set of chosen facts; in order for that hypothesis to be non ad hoc, it has to predict surprising facts.

As the fad continues, we may hear like robots replacing scientists to produce knowledge about various phenomena. For a best critique of AI, check the book by UCBerkeley philosopher Hubert Dreyfus: what computers can't do, a critique of artificial reason.