ML is search. If you have more parameters, you can do more, but the search problem is harder. Deep NN is a way to parallelize the search problem with # of grad students (by tweaks, etc.), also a general template to guide local-search-via-gradient (e.g. make it look for "interesting" features in the data).
I don't mean to be disparaging, btw. I think it is an important innovation to use human AND computer time intelligently to solve bigger problems.
In some sense it is voodoo (not very interpretable) but so what? Lots of other solutions to problems are, too. Do you really understand how your computer hardware or your OS work? So what if you don't?
In some sense it is voodoo (not very interpretable)
There is research in that direction, particularly in the field of visual object recognising convolutional networks. It is possible to interpret what a neural net is looking for.
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