The reason that Perl hasn't become a bigger contender in the field of AI programming is because there's no one in the ring willing to give it a chance.

I'd go further, and say that the reason AI hasn't become a bigger contender in any field is that there are no compelling AI applications. Sure, machine learning has been making some advances in recent years (SVMs and boosting come to mind). But:

1. The advances are precisely in the "non-AI" fields -- the more "statistical" the classification problem, the better the performance of the methods.
2. Humans do much better, except when the sheer volume of data overwhelms them. (So the chief advantage of modern ML is in simple data processing -- not in displaying any intelligence).
3. ML seems to work initially due to the "80%-20%" problem: it is quite easy to solve 80% of any given classification problem (this is one of the reasons why so many classification techniques give such similar results). But this does nothing to help solve the remaining 20% of the problem.

   For instance, classifying 80% of webpages is easy (even if you discard the 97% p0rn, 80% of the remaining 3% is easy to achieve). Google does a fair job, and on smaller collections researchers can do much better. But all these systems make huge, embarrassing mistakes.

After around 50 years, I think AI people could at least show applications rather than just "ideas".

Hi Monks

Isn't Deep Blue (the chess program) an excellent example of an application in AI? From what I know of the program, it can make novel decisions based on past "experience", which means that there's some "learning" taking place.

kiat

No, I would not consider any chess program to be an example of an application in AI. Most, if not all, chess programs are just brute force algorithms, combined with extensive libraries for the opening game. After the opening, they just explore all possible plies (a ply is a half-move) to a certain depth, evaluate the postions, and prune to select the best moves so far. Then they repeat the process of "try all, evaluate, prune to select best so far" zero or more times.

This doesn't even come close to how a human chess player works. Not much is know how exactly a human chess mind works, but (grand)masters only explore a few moves in depth when its their turn to move.

As for the learning process of Deep Blue, the only ones that are "learning" are the programmers.

Abigail