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Despite what it seems, I think ML might be suffering from everyone focusing on the local maxima of backprop-trained affine transforms (CNNs/LSTM/etc). The space of possible approaches is vast, and although this one has turned out to work okay, many alternatives remain possible
If thousands of researchers dedicate their efforts to making incremental improvements to a given technique, it's bound to progress and succeed (within its fundamental limits). But you could have seen similar progress if the same resources had been invested in a different paradigm
I think science moves faster when many different approaches are competing, not when everyone agrees on what works best.
Any suggestions for readings on different paradigms?
Bayesian statistics for data analysis and probabilistic programming.
some of us are doing very diffrent things. im sure many others are as well. your point though is a good one
Go out and breathe!
Perhaps the paradigm shift has been so rapid there a key underlying factors missed..
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