Motivating a Systems Theory of AI. Issue 1 (15th April 2020)

• Record Type: Journal Article
• Authors: Cody, Tyler; Adams, Stephen; Beling, Peter
• Is Part Of: Insight. Volume 23:Issue 1(2020)
• Page Start: 37
• Page End: 40
• Publication Date: 2020-04-15
• Subjects: Systems engineering -- Periodicals; Systems engineering; Periodicals; 620.0011
• DOI: 10.1002/inst.12283
• Languages: English
• ISSNs: 2156-485X

Abstract

ABSTRACT: Artificial intelligence is an emerging technology with few principled engineering frameworks guiding its application, particularly theoretical frameworks for understanding the interrelationships between systems and their learning processes. We propose addressing this gap by using systems theory as a mathematical superstructure for learning theory. Such a framework would connect learning theory and machine learning directly to model‐based systems engineering practices. A general systems learning theory would show how general learning processes relate to general systems and, when applied to particular systems, could reveal how particular learning processes relate to particular systems. Condition‐based maintenance and actuators for context to discuss these concepts.