I'm envisaging "an AI" as a program that is being fed a sequence of inputs (visual inputs from the world around it, say, or text inputs from someone its interacting with), and comes up with a sequence of outputs (speech, actions, ... ).
Gathering the input/output sequence, it seeks patterns, perhaps particularly patterns with arbitrary time-delays (cluster X of inputs often leads to cluster Y of inputs with some delay).
How do you give it a (or bootstrap its) decision-making process? Why should it choose one output over another? Or rather, what kind of goals should it have?
You could simply feed it data, ask it to extract patterns, and then give it some short-term goal, I guess. I'm not sure how much freedom that gives it for exploring nicely.
Or I may be missing your point completely ... ?
(The beginning of this paper, for example, sounds a little like you're describing: http://citeseer.ist.psu.edu/296596.html as far as learning concepts is concerned. (I haven't read the paper)).
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I'm envisaging "an AI" as a program that is being fed a sequence of inputs (visual inputs from the world around it, say, or text inputs from someone its interacting with), and comes up with a sequence of outputs (speech, actions, ... ).
Gathering the input/output sequence, it seeks patterns, perhaps particularly patterns with arbitrary time-delays (cluster X of inputs often leads to cluster Y of inputs with some delay).
How do you give it a (or bootstrap its) decision-making process? Why should it choose one output over another? Or rather, what kind of goals should it have?
You could simply feed it data, ask it to extract patterns, and then give it some short-term goal, I guess. I'm not sure how much freedom that gives it for exploring nicely.
Or I may be missing your point completely ... ?
(The beginning of this paper, for example, sounds a little like you're describing:
http://citeseer.ist.psu.edu/296596.html as far as learning concepts is concerned. (I haven't read the paper)).