Which brings up the question of what all these academic machine learningists (especially the theorists) are up to? Why aren't they winning kaggle? Why aren't Andrew Ng's own grad students collecting the prizes? Some self-reflection needs to go on.
Maybe they are spending their time conducting research instead of entering contests? I'm not trying to be snarky, but do you know that they're even entering the contests?
Why are they not entering contests? What is the point of their research if it isn't to engineer better algorithms for the task of machine learning? How do they prove that their research led to better algorithms?
I would imagine they publish papers that are reviewed by other academics. It is possible to use their algorithm on a dataset without entering a contest to do so.
It's a little like asking why Electrical Engineering PhDs are not designing the new iPhone (probably some are involved somewhere, I know). Research and applications are two separate endeavors.
The researchers actually do set up competitions to see who is best. However, there are few entrants. In kaggle there are 1000s of competitors, so it's a better judge of whether they are legitimately improving the state of the art (rather than simply schmoozing their local funding agencies).
Oh, just noticed the username. I hope someone hires Marshall soon, or he finishes the book he is working on , or whatever, so this long form trolling can come to conclusion.
within the constraints of by the competition, the optimal method of winning is almost always to use well known existing algorithms and methods. academic research, however, is extremely time consuming and is of uncertain value ex ante. but without it we would not have many of the most popular machine learning tools used today...
Well, capitalism, which is about fostering competition, has led to faster progress than the alternatives (assigning duties to specific people - communism or dictatorship).
I don't understand how this is a response to the above point. Kaggle competitions and academic research are on a different timescale and have different goals. Most of the algorithms used by Kagglers were designed by academics - and those academics were rewarded for producing those algorithms with grants from both for-profit and non-profit organizations. Both individual and deeply collaborative endeavors have a place in capitalism and contribute massively to economic growth and scientific progress.
I understand what you're saying, but I'm skeptical that acdemic research is all that it's cracked up to be. Studies are always popping up about how most scientific findings are fraud. And then there's things like the X prizes and darpa challenges which really show who's right and who's wrong and what's actually possible. I'd much rather see open competitions used than grants. Kaggle can be that "central" competition for the machine learning community.
But an open competition doesn't actually fund anything. The money is awarded after all of the work has been done. Somebody has to pay to feed and house scientists while they're working on something, as well as for lab equipment and support staff, etc, etc. An open competition solves none of those problems.
Furthermore, a competition only compensates the winner. That's all well and good for competitions, but sometimes science advances as much by finding things that don't work (and why they don't work) as by finding things that do.
There are probably solutions to all the above. "Tapered" prize giving (even 100th place gets a small prize), more frequent changeovers (rather than tenure and multiyear student commitments, have monthly or weekly reviews about wh stays and who leaves, just like in the private sector). Anyway, I acknowledge your points, I just think the present system isn't necessarily the perfect system.
Are you sure grad students get paid more than that (that's who we're discussing here). And kaggle competitors aren't in it for the money. Almost every entrant will lose (it's winner take all) so all have them have day jobs.
