Chapter 4

To say some piece of art is "great art", I think you need a single characteristic: the ability of many people to interpret the same piece in totally different ways. This is one of the craziest things about high level perception -- the fact that, when you take some objects (tangible or non-tangible), glue them together with some sticky relations, and then recognize that as a "situation" or brand new entity which may be regarded as X to you, but Y to your friends, you've got high-level perception. This is what brought us down from the trees and into our suits and ties. Sure, the sun rises on one side of the horizon and sets in the other -- but what does that mean?

How do you get your computer to produce these same kinds of meanings? Well, for a while, it was easy enough to hard code the knowledge into them.. But, of course, this is cheating. We want the computer to take data and abstract things like objects, relationships, probabilities, and meaning from the whole picture, right? Hofstadter says that figuring out how to appropriate represent a set of data is absolutely essential to discovering how humans process the information. This is a common mantra in the AI communities..

AI researchers have long tailored their data to match their algorithms.. The idea being that in order to emulate cognitive processing, one is able to leave out the mushy perceptual details by defining a more rigid stimulus of their own. In other words, you can remove the low-level input's original state and replace it with a nicer one for the higher-level systems to work on. Obviously, this makes things easier to watch when you're doing research. Hofstadter says you shouldn't be mad at the AI people for this.