Learning to Learn: Facets of Generativity in Machine Learning Frameworks

Abstract

We study the spread of several leading machine learning frameworks underlying the current permeation of artificial intelligence in information systems through the lens of generativity. Empirical evidence indicates that machine learning frameworks do, indeed, exhibit generativity. We further identify three different types of generativity in the frameworks’ development process: evolutionary, combinatorial, and reciprocal generativity. We demonstrate predictive relations of those facets of generativity to related constructs like popularity, activity, and growth empirically, based on an analysis of longitudinal archival data. Empirical analysis further demonstrates that all three facets should be selected as part of a comprehensive predictive model of generativity. The research procedure follows an iterative methodology that facilitates the connection of this work\u27s findings to the broader information systems field