Theoretical Discussion on General Intelligence Frameworks

178 10 A Mind-World Correspondence Principle 10.2 What Might a General Theory of General Intelligence Look Like? It’s not clear, at this point, what a real "general theory of general intelligence" would look like — but one tantalizing possibility is that it might confront the two questions: e How does one design a world to foster the development of a certain sort of mind? e How does one design a mind to match the particular challenges posed by a certain sort of world? One way to achieve this would be to create a theory that, given a description of an environment and some associated goals, would output a description of the structure and dynamics that a system should possess to be intelligent in that environment relative to those goals, using limited computational resources. Such a theory would serve a different purpose from the mathematical theory of "universal intelligence" developed by Marcus Hutter [Hut05] and others. For all its beauty and theoreti- cal power, that approach currently gives it useful conclusions only about general intelligences with infinite or infeasibly massive computational resources. On the other hand, the approach suggested here is aimed toward creation of a theory of real-world general intelligences utilizing realistic amounts of computational power, but still possessing general intelligence comparable to human beings or greater. This reflects a vision of intelligence as largely concerned with adaptation to particular classes of environments and goals. This may seem contradictory to the notion of "general" intelligence, but I think it actually embodies a realistic understanding of general intelligence. Maximally general intelligence is not pragmatically feasible; it could only be achieved using infinite com- putational resources [ITut05]. Real-world systems are inevitably limited in the intelligence they can display in any real situation, because real situations involve finite resources, including finite amounts of time. One may say that, in principle, a certain system could solve any problem given enough resources and time but, even when this is true, it’s not necessarily the most in- teresting way to look at the system’s intelligence. It may be more important to look at what a system can do given the resources at its disposal in reality. And this perspective leads one to ask questions like the ones posed above: which bounded-resources systems are well-disposed to display intelligence in which classes of situations? As noted in Chapter 7 above, one can assess the generality of a system’s intelligence via looking at the entropy of the class of situations across which it displays a high level of intelligence (where “high” is measured relative to its total level of intelligence across all situations). A system with a high generality of intelligence will tend to be roughly equally intelligent across a wide variety of situations; whereas a system with lower generality of intelligence will tend to be much more intelligent in a small subclass of situations, than in any other. The definitions given above embody this notion in a formal and quantitative way. If one wishes to create a general theory of general intelligence according to this sort of perspective, the main question then becomes how to represent goals/environments and systems in such a way as to render transparent the natural correspondence between the specifics of the former and the latter, in the context of resource-bounded intelligence. This is the business of the next section. HOUSE_OVERSIGHT_013094