Dynamic Programming, Memoization, and Artificial Intelligence

Back in high school, in my Grade 10 Computer Science class, I was introduced to the concept of "dynamic programming" for the first time. We never used it, but our teacher only mentioned what it was like. He said that the code changes as the program runs, which I thought was a pretty cool concept. Of course, in my naive little mind, what I thought was that the actual code changed - that is, if you looked at your code from time to time as your program was running, it could be different. (Yea, yea, laugh it up. There's a reason I said 'naive'!)

The idea of memoization is much more plausible and still just as awesome (and efficient!). I actually unknowingly used memoization back in Assignment 1 with the Tour of Anne Hoy, using a dictionary to keep track of what was the best split value for different numbers of cheeses. Running into it again in the context of call stacks, efficiency, and the Fibonacci sequence made me realize how powerful this concept is, even though it's not really a complex one.

The implications of memoization take my thought process straight to artificial intelligence. It brings to mind the idea that the computer is "learning", in a sense, and is able to retain information as it is running just like we learn things through our senses in reality. I don't know much (or anything) about artificial intelligence, but memoization seems like a very basic application of it. As I mentioned in a previous post, my Grade 11 and 12 Computer Science teacher once said that computers do exactly what programmers tell them to do, the only difference being that computers can do them much faster than humans ever could. But, with the advancement of computer science, artificial intelligence is giving computers much more than just blinding speed. Combine the speed of a computer with artificial intelligence, and ... Well ... You've seen the Terminator movies. You should know. ;)