2) The definition clearly says at least something about how to measure this degree of intelligence (rather than just handwaving about the possibility that there might be different degrees), and
This is the shortcoming of the optimization-based approach to defining intelligence, at present. Let's say we define intelligence as "the ability to solve complex optimization problems" and let's say we define the intensity of a pattern P in an entity X as the degree to which P compresses X, and let's say we define the complexity of an objective function f as "the total intensity of the set of patterns in the graph of f, subtracting off for overlap" (ignoring for compactness the subtleties of subtracting off for overlap in this context) Then, this is all dandy mathematically speaking, but how do we actually calculate all the patterns in the graph of a complex real-world objective function, let alone a whole bunch of such? Pragmatically, we can posit a particular pattern-recognition system S, and talk about "all the patterns in the graph of f that S can recognize." If we let S = a typical human, then I suggest that the above definition of intelligence captures a lot of the commonsense human language notion of "intelligence." However, letting S=a typical human, is obviously not the only choice one could make. According to some other assumed pattern recognition system, different judgments of intelligence might be made. I do not claim that this approach captures all aspects of the common language notion of "intelligence" -- and I'm not that interested in trying to precisely formalize natural language concepts, either. That seems like a dead-end pursuit, as someone already noted. My suggestion is that this is the right sort of conceptualization of "intelligence" to use for guiding AGI research. -- Ben G -- Ben ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=fabd7936
