A Minimal Architecture for General Cognition
It offers an alternative approach to achieving general AI by minimizing novelty and using established data science constructs, though it appears incremental as it builds on existing methods.
The paper introduces MANIC, a minimal cognitive architecture with only three function approximators and one state machine, claiming theoretical sufficiency for general cognition and practical trainability.
A minimalistic cognitive architecture called MANIC is presented. The MANIC architecture requires only three function approximating models, and one state machine. Even with so few major components, it is theoretically sufficient to achieve functional equivalence with all other cognitive architectures, and can be practically trained. Instead of seeking to transfer architectural inspiration from biology into artificial intelligence, MANIC seeks to minimize novelty and follow the most well-established constructs that have evolved within various sub-fields of data science. From this perspective, MANIC offers an alternate approach to a long-standing objective of artificial intelligence. This paper provides a theoretical analysis of the MANIC architecture.