Troubling trends in machine learning scholarship (2018)
This is not just isolated to machine learning, but most technical fields (at least one other to my knowledge).I used to work in microscopy image analysis and the papers often would obfuscate the fact that they were not exactly doing anything new by using what looks like fancy math and some trendy names.One of the most outrageous examples is this "high profile" paper that says it does compressive sensing with superresolution microscopy -  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3477591/ except I don't think they do; the math when you remove the bullshit sounds more like deconvolution than anything else (and the results are only as good). Yet, it got reviewed and accepted by Nature Methods, and is cited by 360 papers already. Why? Apparently no one in this field knows what compressive sensing really means. At least one professor in the field when I confronted him, just said he doesn't have time to go through compressive sensing literature first before evaluating this paper.What's the root cause? Frankly I'd argue the majority of professors nowadays aren't smart in innovation but smart in hustling. Because hustlers are who become professors in today's academic climate. They are able to publish good papers still if the field isn't mature, but if the field is saturated, you have to be really smart to make meaningful progress, and these hustlers are not. So they just try to find some way to wrap meaningless progress in fancy math and shove it in papers. The papers also go to reviewers who are similar hustlers (not every paper can be reviewed by Hinton ) so they either don't notice the problem or they do but let it slide because it's just their colleague (yay for journals asking for "suggested reviewers" to the authors itself).