Abstract
This paper explores the rapidly evolving landscape of Artificial Intelligence (AI) as a General Purpose Technology and its dual role in driving and sustaining innovation across various domains. Central to this paper is the development of an AI Concept List and it’s application onto research papers to generate several semantic networks. Numerous stages are involved, including data acquisition and keyphrase extraction, extension through semantic similarities and validation using regression analysis. The AI Concept List, created through a custom unsupervised machine learning pipeline, consists of clustered keyphrases that encapsulate the broad field of AI, each annotated with an importance weight to aid in-depth analysis in various research and industry domains. The findings unveil a steady rise in the prevalence of AI concepts across certain research domains. Subsequent discussions delve into potential implications, practical applications, and inherent limitations alongside with future research directions and subsequent improvements. This work proposes a novel methodology for measuring innovation, aiming to benefit the academic and industrial communities by highlighting groundbreaking innovations and uncovering AI applications in new domains.