Academic discussion of everyday-world properties for intelligence research

162 9 General Intelligence in the Everyday Human World 9.2 Some Broad Properties of the Everyday World That Help Structure Intelligence The properties of the everyday world that help structure intelligence are diverse and span multiple levels of abstraction. Most of this chapter will focus on fairly concrete patterns of this nature, such as are involved in inter-agent communication and naive physics; however, it’s also worth noting the potential importance of more abstract patterns distinguishing the everyday world from arbitrary mathematical environments. The propensity to search for hierarchical patterns is one huge potential example of an ab- stract everyday-world property. We strongly suspect the reason that searching for hierarchical patterns works so well, in so many everyday-world contexts, lies in the particular structure of the everyday world — it’s not something that would be true across all possible environments (even if one weights the space of possible environments in some clever way, say using program- length according to some standard computational model). However, this sort of assertion is of course highly “philosophical,” and becomes complex to formulate and defend convincingly given the current state of science and mathematics. Going one step further, we recall from Chapter 3 a structure called the “dual network”, which consists of superposed hierarchical and heterarchical networks: basically a hierarchy in which the distance between two nodes in the hierarchy is correlated with the distance between the nodes in some metric space. Another high level property of the everyday world may be that dual network structures are prevalent. This would imply that minds biased to represent the world in terms of dual network structure are likely to be intelligent with respect to the everyday world. In a different direction, the extreme commonality of symmetry groups in the (everyday and otherwise) physical world is another example: they occur so often that minds oriented toward recognizing patterns involving symmetry groups are likely to be intelligent with respect to the real world. We suspect that the number of cognitively-relevant properties of the everyday world is huge ... and that the essence of everyday-world intelligence lies in the list of varyingly abstract and concrete properties, which must be embedded implicitly or explicitly in the structure of a natural or artificial intelligence for that system to have everyday-world intelligence. Apart from these particular yet abstract properties of the everyday world, intelligence is just about “finding patterns in which actions tend to achieve which goals in which situations” ... but, the simple meta-algorithm needed to accomplish this universally is, we suggest, only a small percentage what it takes to make a mind. You might say that a sufficiently generally intelligent system should be able to infer the various cognitively-relevant properties of the environment from looking at data about the ev- eryday world. We agree in principle, and in fact Ben Kuipers and his colleagues have done some interesting work in this direction, showing that learning algorithms can infer some basics about the structure of space and time from experience [MIX07]. But we suggest that doing this really thoroughly would require a massively greater amount of processing power than an AGI that embodies and hence automatically utilizes these principles. It may be that the problem of inferring these properties is so hard as to require a wildly infeasible ATXI“ / Godel Machine type system. HOUSE_OVERSIGHT_013078