| I would like to offer what I believe is a counterpoint, but I am not a trained ML researcher so I am not sure if it is even a counter-point. Maybe it is just an observation. I recently participated in the following Kaggle competition: https://www.kaggle.com/allen-institute-for-ai/CORD-19-resear... Now, you can see the kinds of questions the contest expects the ML to answer, just to take an example: "Effectiveness of movement control strategies to prevent secondary transmission in health care and community settings" All I can say is that the contest results, on the whole, were completely underwhelming. You can check out the Contributions page to verify this for yourself. If the consequences of the failure weren't so potentially catastrophic, some might even call it a little comical. I mean, its not as if a pandemic comes around every few months, so we can all just wait for the computational power to catch up to solve these problems like the author suggests. Also, I couldn't help but feel that nearly all participants were more interested in applying the latest and greatest ML advancement (Bert QA!), often with no regard to the problem which was being solved. I wish I could tell you I have some special insight into a better way to solve it, given that there is a friggin pandemic going on, and we could all very well do with some real friggin answers! I don't have any such special insight at all. All I found out was that everyone was so obsessed with using the latest and greatest ML techniques, that there was practically no first principles thinking. At the end, everyone just sort of got too drained and gave up, which is reflected by a single participant winning pretty much the entire second round of 7-8 task prizes by the virtue of being the last man standing :-) I have realized two things. 1) ML, at least when it comes to understanding text, is really overhyped 2) Nearly everyone who works in ML research is probably overpaid by a factor of 100 (just pulling some number out of my you know what), given that the results they have actually produced have fallen so short precisely when they were so desperately needed |