Yeah, AUMathTutor, you are on the right track.
A.I. has suffered a lot throughout the years because scientists have been trying to directly emulate human intelligence. The problem is that we don't know nearly enough about the brain, neurons, or psychology to program these principals into a computer.
However, once computer scientists started taking a logical and statistical reasoning approach to A.I., great advances have been made. This alternate approach still relies on psychological principles, but it focuses on areas which we already know how to program into a computer. Then, from this foundation, mathematical and algorithmic techniques are used to create a new, computer specific, notion of intelligence.
To understand this, think about how you read the sentence, "Time flies." You probably interpreted this to mean, "Time moves fast." If you look closely, however, you could also read it as "Time (with a stopwatch) small flying insects called flies." The reason you thought of the first meaning was because this phrase is more common. That is to say that it has a higher probability of occurring. This notion of probability allows computers to make educated decisions. We can tell it what time is and what flies are and now it can figure out what the sentence is supposed to mean.
Unfortunately, the ambiguity present in "Time flies" poses a real challenge for the future of AI. Humans can determine the meaning almost instantly, but computers have to go through both interpretations and determine which the speaker intended. For an even more complicated example, consider all the different meanings of "Time flies like an arrow."
As for the philosophy of intelligent machines, Alan Turing wrote a fantastic article about this subject in 1950. The article is written for a general audience and very understandable, but it's about thirty pages long, so read it if you want to. =)
http://www.loebner.net/Prizef/TuringArticle.html"
