Brains as Models of Intelligence by Eric Hart (aka Chris Langan) Intelligence testing has, for some time been in disrepute. Critics have a number of complaints with it; it is "culturally biased", it "fails to account for creativity", there are "too many kinds of intelligence to measure on one kind of test", and so on. But advances in the theory of computation, by enabling a general mathematical description of the processes which occur within human brains, indicate that such criticisms may overlook more general parameters of intellectual ability... i.e., that there exist mathematical models of human mentation which allow at least a partial quantification of these parameters. Neural networks are computative structures analogous in general topology and functionability to the human cortex. The elements of such networks act like generalized brain cells linked by excitative and inhibitive "synapses" and conditioned by "learning functions" acting on "sensory" input. Because they are computative machines, they obey the same principles as machine programs, digital computers, and cellular automata. While there are certain aspects of human brains which distinguish them from generalized neural nets, it is instructive to assume a close analogy between them; specifically, that every thing a brain can do has its formal counterpart in the model. Thus, an algorithmic description of mental processes, translated into the "language" of the model, generates patterns with definite consequences in the model's descriptive logic. http://www.scribd.com/doc/30454472/Noesis-Chris-Langan-Comments Reality as a model. There you have it. Human mentation being modeled in the above 1989 paper in Noesis by Christopher Langan. A perfect isomorphic description of the functions of the human cortex.