Achieving artificial general intelligence through peewee granularity

Abstract

The general intelligence of any autonomous system must in large part be measured by its ability to automatically learn new skills and integrate these with prior skills/Cognitive architectures addressing these topics are few and far between - possibly because of their difficulty. We argue that architectures capable of diverse skill acquisition and integration, and real-time management of these, require an approach of modularization that goes well beyond the current practices, leading to a class of architectures we refer to as peewee-granule systems. The building blocks (modules) in such systems have simple operational semantics and result in architectures that are heterogeneous at the cognitive level but homogeneous at the computational level.