Cognition is pattern recognition, and we don't ever access the raw data, just the output labels - this is so Buddhist. In a sense we create our (inner) world because it is just patterns.
Recognizing patterns in data is so easy to do, that how easy it is, turns out to be a major problem in machine learning. You can always find some explanation that perfectly fits all the data you see, but that doesn't mean it will fit the data you haven't yet seen.
The challenge is finding the simplest patterns that will generalize to explain the most data, while wasting as little effort as possible on the irrelevant patterns. In high dimension data, the number of possible relationships to analyze explode, you can find patterns everywhere you look, so it's deciding where to bother looking with your limited resources that's hard.
That patterns are nothing special doesn't seem obvious to us because, evolution has done a pretty good job solving this problem(in the domain of inputs we evolved to deal with), and we only perceive those patterns that are likely to generalize.
"What lay behind the illusion of the park was just the real park."
So no, not that Buddhist. The function of cognition is not to detach ourselves from an insignificant, not-really-there illusionary world, but in fact to create increasingly accurate, but still tractable, "illusions" that let our brains draw closer and closer to the real world.
Exactly. I'm secretly thinking that some of the reason we have failed so far at general AI is we are coming at the problem from a western philosophical tradition :) Nice to meet you brother
