Let's take the example of The Netflix Prize, a $1 million bounty that the movie shipping organization ran several years ago. Their purpose was to improve their ratings prediction algorithm, under the pretext that people frequently ran out of ideas of what to rent, and that a successful suggestion algorithm would keep people as customers longer after that point.
So, they carefully defined the success rate of their algorithm - that is, make it predict some set of actually-rated movies X on a 1-5 half-integer scale, take the arithmetic mean of (the sum of (the square of each error from the real rating)) - which we'll call root mean square error, or RMSE - and you have your "score," where towards zero is perfect.
Their predictor had a score of I think 0.973 something (it's been years, don't quote me on that.) Their challenge was simple.
Beat their score by ten percent, and you trigger a one month end-of-game. At the end of that month, whoever's best wins le prize. One million dollars, obligatory Doctor Evil finger and all.
Netflix provided (anonymized) a little over 100 million actual ratings, where all you had was a userID, a movieID, a real rating, and separately, a mapping "this movieID is this title." You were only allowed to use datasets in your solution that were freely available to everybody, and you had to reveal them and write a paper about your strategy within one month after you accept le prize, honh honh honh.
Seriously, it was awesome. They were going to do a second one, but lawyers, and the world sadded.
So, there, you've got a ten times larger dataset. So surely sixteen thousand cores is the drastic thing, right?
Well, not really. I was running my solution on 32 Teslas, which in the day were $340 in bulk and had 480 cores each. So I actually "only" had 15,360 cores, which falls a whopping four percent short of Google's approach, which several years ago cost me about the price of a recently used car, and which I was able to resell afterwards as used, but without the bulk discount, for almost exactly what I paid for them in the first place.
Swish.
And I mean, I've got to imagine that someone else chasing that million dollar prize who thought they were going to get it invested more than I did. There were groups of up to a dozen people, data mining companies, etc.
So if one dude sitting in his then-Boise apartment can spend like $11k on a ten times this dataset dataset over a commercial prize?
Yeah.
Cyc still pantses all of us.