Okay, I am bored, or maybe just lazy today, so please let me weigh in and ramble a bit:
Vectors and scalars are great, and may be the best route to learning for a given system, but it hardly seems obvious that they are a prerequisite to learning for an AI that exceeds general human intellectual capacity. I was a chemical engineer in one of my former lives, and I can say that vectors are definitely more lovable than the criminal defendants I was appointed to represent in my former life as an attorney. The defendants were mostly interested in the rather binary guilty vs. not guilty. Retinas have pixels don't they? Perhaps our perception of scalars is actually recognition of patterns in discrete points. You could readily make an image people recognize as a circle, using only pawns as discrete points on a chessboard. Wouldn't chess be a domain where an AGI could learn and excel, with no vectors or scalars in sight? Much of what is fundamental is binary: on/off, dead/alive, male/female, married/single, smile/frown, and so on. A miss is a good as a mile. . . . Kevin C. P.S. To me a key fundamental is "Artificial Motivation." Give an entity the desire to accomplish goals, plus tools to use, then the ability to learn. Example: I was hungry, but now am full. I wanted to reproduce, and satisfied that urge. Now I am tired of thinking, and want to consume more of that wet fermented grain to stop the process for a while. Ahh, cultivating barely to make beer is good. Oops, inadvertently founded civilization. ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/?[EMAIL PROTECTED]
