I'm starting to appreciate this sentiment. The way most ML success stories are presented, you'd think it really is all a case of finding enough data, and let th

I'm starting to appreciate this sentiment. The way most ML success stories are presented, you'd think it really is all a case of finding enough data, and let the computer learn to extract something useful from it. That is not the hard part. The hard part, is finding a dataset that is amenable to the training process. Or at the very least, determining if a collection has any 'educational' value at all. Then, by the time you get to that point, you're probably already looking at metadata, or a simple pattern that could possibly be encoded in a database query. I have some faith in some of these new discoveries (Alphafold is a remarkable case study), but in many cases, the effectiveness of ML seems to be overstated.