Towards Industrial Convergence : Understanding the evolution of scientific norms and practices in the field of AI

TL;DR

This study investigates how the increasing collaboration and influence of industry in AI research are shaping academic norms and practices, revealing partial convergence driven by mixed teams that succeed across artifacts.

Contribution

It provides empirical evidence on the extent of industrial influence on academic AI practices and highlights the role of mixed teams in fostering convergence.

Findings

Industrial presence influences academic practices towards industry norms.

Mixed academic-industrial teams achieve greater success in research artifacts.

Practices of academics and industry differ significantly despite overlapping activities.

Abstract

In the field of artificial intelligence (AI) research, there seems to be a rapprochement between academics and industrial forces. The aim of this study is to assess whether and to what extent industrial domination in the field as well as the ever more frequent switch between academia and industry resulted in the adoption of industrial norms and practices by academics. Using bibliometric information and data on scientific code, we aimed to understand academic and industrial researchers' practices, the way of choosing, investing, and succeeding across multiple and concurrent artifacts. Our results show that, although both actors write papers and code, their practices and the norms guiding them differ greatly. Nevertheless, it appears that the presence of industrials in academic studies leads to practices leaning toward the industrial side, but also to greater success in both artifacts, suggesting that if convergence is, then it is passing through those mixed teams rather than through pure academic or industrial studies.