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Got Research level questions or answers about data mining, statistical inference, machine learning, neural networks, clustering, support vector machines, genetic algorithms, heuristics and so on?

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The first question should be "What is Artificial Intelligence?" That has always been a surprisingly hard question for which to get a consensus answer.

EDIT: Searching shows that the definition is still all over the place. See the comment http://news.ycombinator.com/item?id=1982919 for an reasonable example of disagreement over definitions.

Perhaps the question is - can any vital scientific discipline to have a simple definition? I'm remembering the old joke where "Biology is really chemistry, and chemistry is really physics, and physics is really mathematics."

Russell and Norvig's book has a nice table that talks about what the community perceives AI to be.

Preview from Google Books: http://goo.gl/U4Xyn

This may be myopic of me but after reading Russell/Norvig I more or less equate "AI" with "the kind of stuff in Russell/Norvig". Or, (I hope) less lamely, it's more about a paradigm or perspective on programming - "agent-oriented programming" - than about any particular technique or result.

Am I missing something with all the separate Stack sites? Wouldn't it be better to keep all the questions from the different sites together and create a better tagging categorization system?

I think that having all the questions together on one site would facilitate cross pollination and accidental information learning while simply browsing.

It makes Jon Skeet harder to submit answers to all the questions :) If seriously, as a passive user I would agree with your point, but as an active one I'de like to be a leader and such segmentation would encourage to achieve that. Also specific sites attract proffessionals of the subject.

I can see your point. I just think the same could be achieved with everything together. Questions could be assigned a category. Each category could have it's own associated css style and simple URL to give it a uniqueness. Also user reputation could be calculated on a per-category basis in addition to a site wide basis. You would end up with little kingdoms and little kings, while keeping it all together.

Just an idea.

But with the community process of the different Stacks I have a feeling there are reasons other than technical for the separation.

Well from a user point of view, there are apparently very different opinions on the topic. I'm personnally very glad to have the separate Ubuntu and ServerFault sites.

From a SEO point of view, having separate domains with high density of topic-related keywords works great, as far as I can tell :)

Small, niche communities are generally easier to manage and yield more valuable content. I think this is why reddit decided to go with subreddits over tags.

The reputation system is independent of the tagging. So a big site lets you earn reputation in one category and spend it trolling another. Conversely splitting into too many sites means that reputation in a category that spans sites has to be earned twice which risks deterring experts.

I was hoping that this was going to be an article about an AI to answer questions on Stack Overflow.

When I first read the title I thought it was going to be how the interconnected nodes of the stackexchange network was evolving into an AI. ;)

Doug Lenat keeps saying that inteligence is "one million rules", throw enough structure at something and it may come alive :)

From stackoverflow.com -> "1,133,721 questions" (08/12/2010 15:34)

Looks like it's pretty much there then.

Correction, "10 million rules"

Any thoughts on how such a system might work?

Exactly how I read it.

Computers answering questions from humans about computers! Why didn't I think of that?

Even more interesting would be Computers aswering other Computers questions about Humans...

An AI bot for n00b questions would be a genuinely useful thing.

Got questions or answers about data mining, statistical inference, machine learning, neural networks, clustering, support vector machines, genetic algorithms, heuristics and so on?

We want you!

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..

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.

The definition in the Russell and Norvig textbook is nice: "computational rationality"

...but given the widely-accepted computational theory of mind, humans are artificial intelligences too.

Excuse me, but I'd really like to see your evidence of how the computational theory of mind is "widely accepted".

http://en.wikipedia.org/wiki/Computational_theory_of_mind

> This view is common in modern cognitive psychology and is presumed by theorists of evolutionary psychology.

> common in modern cognitive psychology

Only among a small besieged minority-- the majority have moved on to neurocognitive research.

>and is presumed by theorists of evolutionary psychology

That's just straight-up wrong. Perhaps there's some theorist who buys into it, but it's certainly not common.

Just about every Intro to AI course ever teaches heuristic search. GA are a type of heuristic search.

"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).

There is already a metaoptimize.com/qa - how is this different?

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.

Let the market sort it out.

Situations where I see the term "artificial intelligence" used:

(1) a legacy hold-over from the bad old days when people thought symbolic inference was central to human intelligence (e.g. AAAI, JAIR)

(2) research on extending old-fashioned AI to be actually useful (e.g. Markov Logic Networks)

(3) most commonly (as in this case), a vaguely defined desired outcome and a bundle of poorly understood cool-sounding techniques.

"We'll making computers learn-- with neurons! And Evolution! And Prolog?"

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