A Recursive, Permissioned Architecture for True Global Superintelligence

Abstract

Modern artificial intelligence is built from weighted networks. In a neural model, intelligence does not reside in any single neuron; it emerges from the learned pattern of influence among many computational units. IAN begins from the hypothesis that the same organizing principle can be moved one abstraction level higher: once individual models and other systems become capable intelligences, those intelligences can themselves become the nodes of a larger learnable network.