1. Introduction

Cybernetics emerged in the post-war era in the UK and USA with the aim of delving into the complexities of the brain and comprehending the fundamental mechanisms governing both organic and computational systems. It laid the foundation for the development of machine learning, originating in the 1980s and experiencing a resurgence in the 2000s. The interdisciplinary nature of cybernetics led to the creation of adaptive and autonomous machines (Walter 1950, Ashby 1954), as well as the formulation of new theories of control and communication (Shannon 1948, Wiener 1961).

Cybernetics and artificial intelligence, though often conflated, represent distinct paradigms in understanding intelligent systems. While cybernetics focuses on examining complex systems and their self-regulatory mechanisms, artificial intelligence (AI) aims to instil machines with behaviours resembling human actions. Both disciplines explore the conditions necessary for learning, but they approach the concept from differing perspectives. Artificial intelligence relies heavily on datasets to inform intelligent behaviour, whereas cybernetics emphasises grounded behaviours which express intelligence and learning capacity through interactions and feedback mechanisms.

Cybernetics and AI operate within the framework of binary logic and share a fundamental principle: intent. While the logic is universal, the intentions are culturally contingent.