Cognitive Computing and its Relationship to Computing Methods and Advanced Computing from a Human-Centric Functional Modeling Perspective

Abstract

Recent advances in modeling human cognition have resulted in what is suggested to be the first model of Artificial General Intelligence (AGI) with the potential capacity for human-like general problem-solving ability, as well as a model for a General Collective Intelligence or GCI, which has been described as software that organizes a group into a single collective intelligence with the potential for vastly greater general problem-solving ability than any individual in the group. Both this model for GCI and this model for AGI require functional modeling of concepts that is complete in terms of meaning being self-contained in the model and not requiring interpretation based on information outside the model. The combination of a model of cognition to define an interpretation of meaning, and this functional modeling technique to represent information that way together results in fully self-contained definitions of meaning that are suggested to be the first complete implementation of semantic modeling. With this semantic modeling, and with these models for AGI and GCI, cognitive computing and its capacity for general problem-solving ability become far better defined. However, semantic representation of problems and of the details of solutions, as well general problem-solving ability in navigating those problems and solutions is not required in all cases. This paper attempts to explore the cases in which it is, and how the various computing methods and advanced computing paradigms are best utilized in each case from the perspective of cognitive computing.