Leveraging General Collective Intelligence to Increase the Impact of Pattern Analysis and Machine Intelligence Algorithms

Abstract

The emerging science of General Collective Intelligence or GCI describes how GCI as a hypothetical cognitive computing platform might enable groups of individuals or intelligent agents working on behalf of those individuals to self-assemble into a massive network of cooperation. Such networks are predicted to be able to radically increase the speed and scale at which the group might construct platforms from a library of logic based functionality (i.e. procedural program) and/or a library of text pattern recognition based functionality (i.e. machine intelligence algorithms), as well as enable groups to do so on a self-sustaining basis, where these self-assembled platforms are also predicted to achieve significantly better outcomes for the group of users. To facilitate this self-assembly, GCI leverages Human-Centric Functional Modeling to represent such components in terms of paths through a functional state space defined to represent all possible behavior of this cognitive computing platform so that all such components can be represented. This article explores how this same approach might be applied to define cognitive visual processing platforms that radically increase capacity to leverage all existing image processing functionality as well as all visual pattern recognition algorithms in order to radically increase capacity for and effectiveness at image recognition.