A Working Hypothesis for General Intelligence

Humans can construct powerful mental programs for many domains never seen before. We address the questions of how this occurs, and how it could possibly be accomplished in software. Section one surveys a theory of natural understanding, as follows. One understands a domain when one has mental programs that can be executed to solve problems arising in the domain. Evolution created compact programs understanding domains posed by nature. According to an extrapolation of Occam's razor, a compact enough program solving enough problems drawn from a distribution can only be found if there is simple structure underlying the distribution and the program exploits this structure, in which case the program will generalize by solving most new problems drawn from the distribution. This picture has several important ramifications for attempts to develop Artificial General Intelligence (AGI), suggesting for example, that human intelligence is not in fact general, and that weak methods may not suffice to reproduce human abilities. Section 2 exemplifies this picture by discussing two particular thought processes, the mental program by which I solve levels in the game of Sokoban, and how I construct this mental program. A computer program under construction to implement my introspective picture of my mental program is based on a model of a particular mental module called Relevance Based Planning (RBP). Section 3 argues that programs to address new problems (such as my mental program to play Sokoban) can be constructed (both naturally and artificially) if and only if sufficient guidance is already present. It proposes a computational structure called a scaffold that guides rapid construction of understanding programs when confronted with new challenges.