Machine Learning and Artificial Intelligence

A child tries to scoop liquid out of a cup with a fork, and doesnt get much to drink.  The child then tries a spoon, and gets some success.  The memory is registered, forks arent good for scooping liquid, spoons are.  One problem found, two options tried, one option equals success, one equals failure, and the child gains experience.

This seems like a pretty simple problem, but it's actually more complex than we might think.  What if the liquid is hot?  What if the liquid is bitter, and the child tastes the bitterness on the end of the fork?  Good thing we didnt try the spoon first.

Learning and gaining experience is about more than just selecting from a set number of  possible choices and measuring the outcome.  As humans, we 'feel' the experience.  We register every event in multiple ways.  We feel pleasure with some outcomes, and we feel pain with others.  These influences are critical to our ability to gain knowledge.

So how do we teach machines to feel, to measure the satisfaction of an experience?  To determine pleasure or pain?  Anger or happiness?  Good or evil?

I'm starting to work on this problem from a programming perspective using some of the concepts developed with neural nets or parallel distributed processing.  Neural nets are actually an older technology and from what I have seen, they are pretty limited in their capabilites.  But the idea of establishing highly specialized neural nodes that work in unison to make complex decisions is still very valuable.

My idea is to take the neural net concept and twist it around a bit.  I need a system is that is focused enough to draw from previous occurances, and draw inferences based on probability, and in some cases, inprobability.  The concept is closely related to the science of synchronous events, think of hive behaviors such as ants and bees.  Anybody read Ender's Game?

BlackJack is going well...   more soon.