Re:Google admits brainteasers were useless for hiring (2013)
by true_religion on Thursday May 31, 2018 @11:59PM
(#17202466)
You could do per-state licensing as happens in other professions.
So perhaps, California would have the highest standards with Google/Facebook employees pushing them to do so to create barriers to entry, but Mississippi would end up with the bare minimum.
I wouldn't find it terrible if Software Engineering were licensed, in the same way that law is---with the board consisting of prominent practicing members of the existing population, and all current members grandfathered in so it only affects upcoming generations.
Re:Google admits brainteasers were useless for hiring (2013)
by southern_cross on Friday June 01, 2018 @06:08AM
(#17203988)
You know, if they went down the licensing path then they would probably have to pretty much ban Windows for any serious professional use, since it's not really "fit for purpose" and never has been. (Minimal acceptable standards would probably be some type of hard-core Unix-like OS.) The same might be true for a lot of the open source stuff which is currently so popular.
As more recent examples, a few years back I took all three of the original free MOOCs from Stanford. In the ML class, Andrew Ng at one point said something like "If you've ever taken calculus then you will recognize this particular equation as being a derivative; otherwise don't worry about it because that's not really important." Later he said something similar about linear algebra and matrix multiplication. And indeed I had taken both calculus and linear algebra back in the day so I recognized what I was looking at, but it just wasn't important within the context of that advanced class.
I also took the Database MOOC, and during that the instructor taught quite a bit of formal database theory. (I forget the proper name for it.) But despite having worked with databases for decades now I've only seen such theory referenced maybe twice, and that just in passing, so it's hardly the "critical, need-to-know" stuff that the instructor insisted that it was.
Meanwhile, in the AI MOOC almost everything taught there was new to me, despite my having taken several AI classes back in the day; so basically zero overlap between then and now. (I did recognize some of the statistical stuff, though, from my statistics class way back when.) But will any of the new stuff ever be of any practical use to me? Probably not. Interesting certainly, but probably not at all practical.