Version 1
: Received: 7 February 2022 / Approved: 8 February 2022 / Online: 8 February 2022 (12:31:01 CET)
How to cite:
Lelli, F. On Exploring the Possibilities and the Limits of AI for an Interoperable and Empowering Industry 4.0. Preprints2022, 2022020109. https://doi.org/10.20944/preprints202202.0109.v1
Lelli, F. On Exploring the Possibilities and the Limits of AI for an Interoperable and Empowering Industry 4.0. Preprints 2022, 2022020109. https://doi.org/10.20944/preprints202202.0109.v1
Lelli, F. On Exploring the Possibilities and the Limits of AI for an Interoperable and Empowering Industry 4.0. Preprints2022, 2022020109. https://doi.org/10.20944/preprints202202.0109.v1
APA Style
Lelli, F. (2022). On Exploring the Possibilities and the Limits of AI for an Interoperable and Empowering Industry 4.0. Preprints. https://doi.org/10.20944/preprints202202.0109.v1
Chicago/Turabian Style
Lelli, F. 2022 "On Exploring the Possibilities and the Limits of AI for an Interoperable and Empowering Industry 4.0" Preprints. https://doi.org/10.20944/preprints202202.0109.v1
Abstract
This paper aims to raise awareness on certain interoperability issues as we intend to shape industry 5.0 in order to enable a human-centric resilient society. We advocate that the need of sharing small and specific data will become more intensive as AI-based solutions will become more pervasive. Consequently, dataspaces should be carefully designed to address this need. We advance the conversation by presenting a case study from HR demonstrating how to predict the possibility of an employee experiencing attrition. Our experimental results show that we need more than 500 samples for developing a machine learning model to be sufficiently capable to generalize the problem. Consequently, our experimental results show the feasibility of the idea. However, in small and medium sized companies this approach cannot be implemented due to the limited number of samples. At the same time, we advocate that this obstacle may be overcome if multiple companies will join a shared dataspace, thus raising interoperability issues
Keywords
Industry 4.0; industry 5.0 interoperability; Machine Learning; AI; HR; Attrition
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
