timkin, the sole purpose for attempting to use a neural net to respond to language, is to see what happens when you try to make a neural net respond to language. I''m not trying to test neural nets in general, I just want to experiment. I got the answer to my question: no. But why should I not experiment with it anyway? Maybe I''ll even make it do something useful.



-~-The Cow of Darkness-~-

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Ah Baylor, the crusader from comp.game.ai. I''ve read some of your threads with interest, you occasionally have some interesting angles, but you do tend to fire from the hip in a valiant effort to wind everyone up. For example, you wrote a long diatribe about how useless A* is to game programmers... without really knowing how A* works or how it is applied in games, or even how fast it is compared to other search methods. You were not even aware of the proof that A* is guaranteed to find the shortest path given an appropriate heuristic.

I am responding to your comments because you seem to be doing the same with ANNs. You appear to have learned a little about them and are now making enormous assumptions based upon that limited amount of information. Exactly the sort of thing you dislike when you say "because they get recommended an awful lot and i think people who are given that advice don''t know the limitations of the technique."

Your description of what an ANN is, and what it can or cannot do, is telling. Most ANNs do not use Booleans and the input or output from an ANN can be any real value, although it is true to say that the majority of feedforward networks use normalized inputs because this makes for easier training/learning.

You go on to say

"Any time someone tells you a neural network can compute any function, try having it add 2 and 2

(no, you cannot map to binary; if you do, i change the program to add any two arbitrary numbers)"

What makes you think an ANN cannot add two numbers? Why do you think that mapping to binary makes it a simpler task? Given a range of integer values, a neural net can be created that will add any two of them together. (input in binary or decimal, it makes no difference) An ANN could do the same with real numbers also, but it would take a hell of a lot of training to get the error levels down!

In addition, you have this to say about face recognition:

"But if you wanted to do it the human way, you wouldn''t use an ANN or decision tree or any other single-purpose algorithm. You''d do lots of processing in a special facial recognition module. You''d be looking for curves, lines, distance between features, feature location, etc."

How do you think ANNs are trained to recognize faces? If you want an ANN to catagorize something then it is common practice to use some form of feature extraction. ANNs can be extremely good at catagorization based upon incomplete or fuzzy data (compared to other methods), that''s why they are used so much in commercial ''thing'' recognition software.

You are correct of course that ANNs get recommended far too much by people who don''t really know them. (You are living proof of the converse). Unfortunately ANNs are a ''buzz'' technology, and as such are likely to get promoted and rubbished far too often. This is just human nature. ANNs do have their uses though. Certainly their ability to generalize and to learn is a very powerful feature. However, they should be used as just another tool in the AI programmers work belt (just like your favorite, the decision tree) and not as some sort of magical panacea to all a game''s AI problems.


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