Recently a problem at work cropped up for which I felt an "AI"* solution might be worth exploring. Despite my dabbling in the area, I didn't know where to start. While I didn't have much hope for an answer, a quick email to the Perl AI mailing list yielded many interesting suggestions. Currently, I am playing with AI::NeuralNet::Mesh. My first attempts at teaching my computer binary resulted in a rather slow random number generator. Another email to the list showed me the error of my ways. Later, I'll be off to Powell's books to find some good resources about Neural Nets and pattern recognition.
Curiously, my link to the AI module points to the CPAN Testers page because I can't actually find the module on the CPAN. I was able to install it with CPAN.pm, though, which just tells me that I have plenty more to learn about how the CPAN works.
*Why did I quote "AI"? There's a weird contradiction in AI research. Teaching computers to behave like human brains has, for the most part, been a failure. They don't do a good job of it. As a result, many traditional AI problems are called AI when we can't solve them, but are no longer AI once solved. Chess, for example, is generally brute forced. That's not AI. The same goes for speech recognition and similar problems. Some people mistakenly thing that the type of work I was doing in Prolog is AI, but that's actually logic programming and not the same thing at all -- though some argued that it was when it was first in vogue. I guess the actual distinction we make in AI becomes "once the human mind understands what's going on, it's no longer AI". That strikes me as a tad hubristic. If we ever develop a computer that passes the Turing test and claims to be self-aware, will we deny that it's intelligent if we think we know how it works?