Thomas Hruska wrote: > I don't have any examples. A.I. is a bogus term which basically means > "wing it until you have something that passes for reality". People > typically think "neural networks" when it comes to A.I. but neural > networks are simply statistical engines that have been trained on a > limited set of data and are only semi-useful - the more inputs into a > neural net, the less useful it becomes. >
I have played around with neural networks and attempted to design systems that could make decisions on their own, not just strict if/else logic. What I found is this. 1. Neural networks are not good for A.I. They are good for solving very specific problems where the algorithm is not obvious, or there is no NP solution available: neural networks can prune the solution space such that while the problem may still not be NP complete, it can bring the "real" time to reasonable levels (cheating death as I think of it, big Oh be damned). 2. A.I. is extremely difficult, and generally only possible for people who are not Ph.D.s working at research universities if we simplify the decisions the computer can take. In your previous example, if you were programming a basketball game, you have a finite set of actions such as shoot, pass, move, rebound, etc. and even then you may shoot a 3 pointer, go in for a layup, dunk, etc. Typically there is some sort of probability involved, where the computer gauges the probability of succeeding at each action and tosses a loaded die with the safer options being more likely. While that example may not truly be A.I., it is programming autonomous decision logic into the computer. Instead of hard-coding the actions, it is enabling it to figure out risk factors and determine on its own which action to take. It seems to work well so far, but only for relatively simple problems. -- John Gaughan http://www.jtgprogramming.org/
