Gist: "My guess is that this emerging method will be one additional tool in the evolution of the scientific method. It will not replace any current methods (sorry, no end of science!) but will compliment established theory-driven science. Let's call this data intensive approach to problem solving Correlative Analytics ... In the coming world of cloud computing perfectly good answers will become a commodity. The real value of the rest of science then becomes asking good questions."
The real value of the rest of science then becomes asking good questions.
It were ever thus. Oddly enough Picasso appears to have been one of the first to recognize the relevance of this in the computer age -- "Computers are useless. They can only give you answers" -- long around 1968.
I fire up Maxima (Macsyma) all the time to do mindless derivatives, integrals, sums, and series, so that I can get frustrated as hell working on less tractable problems. The symbolic rearrangements (the ones that can be done without reference to the more complicated reality at hand) are just bookkeeping, and computers are awesome for that.
If raw data (and lots of it) were sufficient, things like dynamic programming, heuristic decomposition, and approximation algorithms (complete with correctness bounds) would be pointless. It's not and they aren't.
nb. Don't take any of the above to mean that the province of "asking interesting questions" is somehow restricted to scientists, or artists, or anyone else. But it is probably the highest hurdle towards discovering something worth pursuing. The antecedent execution thereof is rarely easy, either. Tools help -- a lot -- but by themselves they cannot somehow pull the future into the present. For the time being, at least, intelligence amplification still trumps artificial intelligence. (I hope it stays that way for a while, being human and all)
In the coming world of cloud computing perfectly good
answers will become a commodity. The real value of the
rest of science then becomes asking good questions.
Will you look at that. The same is true for software engineering. Eventually we will have machines choosing both algorithms and data structures for us based on workload and data models. But someone will still have to come up with models and make sure the relate to user's problem space.
http://www.kk.org/thetechnium/archives/2008/06/the_google_wa...
Gist: "My guess is that this emerging method will be one additional tool in the evolution of the scientific method. It will not replace any current methods (sorry, no end of science!) but will compliment established theory-driven science. Let's call this data intensive approach to problem solving Correlative Analytics ... In the coming world of cloud computing perfectly good answers will become a commodity. The real value of the rest of science then becomes asking good questions."