Re:All of Statistics, by Larry Wassserman (2013) [pdf]
by petewailes on Monday May 16, 2016 @02:39AM
(#11704645)
Straw man going on here. The problem is, whilst you can get numbers out without understanding the statistics underneath, you can easily misinterpret that output if you don't understand it.
Look at how p values are used in science journals for an example of poor stats knowledge affecting real life outputs.
There are some things in life that do require you to do the requisite reading. Things based on stats fall into that camp.
Or like programming in C without also knowing assembly and compiler theory? Or flying a plane without having a degree in aerodynamics?
I think we can extract a lot of use from high level frameworks that abstract away much of the gritty statistics and math. For most applications all we need is to have well behaved, well tested libraries and some basic intuition about how they work.
Fortunately, in machine learning almost everything is a function from inputs X to outputs y, and we know how functions work from programming. It's easy to integrate in apps. The devil is in hyperparameter tuning, but we can get away with good initializations and some measure of web research.
In time people will just use precomputed neural nets for standard tasks like Syntaxnet (text parsing), Inception (image classification) ore use web APIs to hosted services (less secure for sensitive data). We make those better, maybe fine tune them to our needs and get away with it 100x faster.
There is also work in automated hyperparameter search. Machine learning could become a black box when they get good enough.