Mind-World Correspondence Principle discussed in theoretical AI manuscript

10.5 How Might the Mind-World Correspondence Principle Be Useful? 181 That is, a little more loosely: the hypothesis is that, for intelligence to occur, there has to be a natural correspondence between the transition-sequences of world-states and the corresponding transition-sequences of mind-states, at least in the cases of transition-sequences leading to relevant goals. We suspect that a variant of the above proposition can be formally proved, using the definition of general intelligence presented in Chapter 7. The proof of a theorem corresponding to the above would certainly constitute an interesting start toward a general formal theory of general intelligence. Note that proving anything of this nature would require some attention to the time-scale-dependence of the link weights in the transition graphs involved. A formally proved variant of the above proposition would be in short, a "MIND-WORLD CORRESPONDENCE THEOREM." Recall that at the start of the chapter, we expressed the same idea as: MIND-WORLD CORRESPONDENCE-PRINCIPLE For a mind to work intelligently toward certain goals in a certain world, there should be a nice mapping from goal-directed sequences of world-states into sequences of mind-states, where "nice" means that a world-state-sequence W composed of two parts W, and Ws, gets mapped into a mind-state-sequence M composed of two corresponding parts MM, and Mg. That is a reasonable gloss of the principle, but it’s clunkier and less accurate, than the statement in terms of functors and path transfer functions, because it tries to use only common- language vocabulary, which doesn’t really contain all the needed concepts. 10.5 How Might the Mind-World Correspondence Principle Be Useful? Suppose one believes the Mind-World Correspondence Principle as laid out above so what? Our hope, obviously, is that the principle could be useful in actually figuring out how to architect intelligent systems biased toward particular sorts of environment. And of course, this is said with the understanding that any finite intelligence must be biased toward some sorts of environment. Relatedly, given a specific AGI design (such as CogPrime), one could use the principle to figure out which environments it would be best suited for. Or one could figure out how to adjust the particulars of the design, to maximize the system’s intelligence in the environments of interest. One next step in developing this network of ideas, aside from (and potentially building on) full formalization of the principle, would be an exploration of real-world environments in terms of transition graphs. What properties do the transition graphs induced from the real world have? One such property, we suggest, is successive refinement. Often the path toward a goal in- volves first gaining an approximate understanding of a situation, then a slightly more accurate understanding, and so forth — until finally one has achieved a detailed enough understanding to actually achieve the goal. This would be represented by a world-path whose nodes are state-sets involving the gathering of progressively more detailed information. HOUSE_OVERSIGHT_013097