Got questions or answers about data mining, statistical inference, machine learning, neural networks, clustering, support vector machines, genetic algorithms, heuristics and so on?
You could argue that it's not very different at all. And it also overlaps significantly in some areas (particularly statistical machine learning) with http://stats.stackexchange.com/
Not that that's a bad thing. Competition is good, and the difference may evolve to be nothing more than culture, or focus or whatever. I mean, HN is similar to certain reddit sub-reddits (or combinations thereof) such as /r/programming and /r/startups. But there's room for both. Same deal, IMO, with AI / Machine Learning / Data Mining / etc. related Q&A sites.
Oh, and never mind that there are multiple subreddits devoted to these topics as well! /r/machinelearning, /r/artificial, /r/sysor, etc. jump to mind.
Why are genetic algorithms included? They are just a tool to explore multidimensional search spaces, and have nothing to do with AI unless they happen to be applied to an AI problem.
It would be nice if this was separated out into "old-fashioned" AI (data mining, statistical inference, machine learning, etc) and bio-inspired technologies (genetic algorithms, cellular automata, neural networks - although the latter may be a grey area).
Alternatively, a better name than "Artificial Intelligence" should have been used for the site. Probably too late now though..
"old-fashioned AI" is usually a term reserved for symbolic techniques fashionable in the 60s, 70s, 80s. Learning and data-oriented techniques are mostly the new kids on the block (for Computer Scientists at least-- stats people have been at this game all along).
As for bio-inspired techniques-- why should their point of inspiration be anything but a footnote? Genetic algorithms are just one instance of a stochastic search algorithm (there are many others), cellular automata can be pretty much anything, and so can neural networks (depending on whether you include the dozens of models more esoteric than multilayer perceptrons).
Actually you can include genetic algorithms into the Artificial Intelligence when your fitness-function applied to individuals gets a little bit "intelligent" (heuristics, neural networks...)
Of course - but one of the on-topic example questions is "How can I avoid premature convergence to a local optima on my genetic algorithm?". That has nothing to do with AI.
I agree, that way they could have included "traditional" local optimization as well, although probably that will come up in machine learning questions anyway.
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