Unsupervised learning on discrete data is just ensembling modes. But let's look at how that helps in some cases. So if we already know the object is a cup, an

Unsupervised learning on discrete data is just ensembling modes. But let's look at how that helps in some cases. So if we already know the object is a cup, and we know how it's positioned, then its shadow is an actual guide to its particular geometry. So in cases where we have enough a priori scientific information, we can rig datasets (shadows) to be informative of the target domain. Here the target is discrete: say the peaks and tips of a mountain line. Now can we rig a photo of a mountain to have in its ink an informative structure? Sure. Now if we didn't know it was a mounting apparent peaks aren't even 'peaks' at all, they're just patterns of ink. A priori explanatory models are needed to rig data for statistical modelling. No such rigging can take place absent them And with them, we aren't really discovering any new science -- rather we're gaining highly particular knowledge typically useful in engineering Biologists in these areas describe this research as quite trivial low-hangimg fruit. It's not of much research interest just to automate these kinds of investigations My masters was on a very similar project applied to quantum metrology -- it's always 'useful' but it's always also just a kind of engineering utility. We couldn't even do it if we hadn't already done the science