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Trust_Gen_Z: Trustworthy Generative AI for Advanced Industrial DigitaliZation

Authors:

Shahina Begum, Shaibal Barua, Mobyen Uddin Ahmed

Research group:

Artificial Intelligence and Intelligent Systems

Publication Type:

Other

Venue:

7th Artificial Intelligence and Cloud Computing Conference

Abstract

The convergence of advanced analytics and AI has paved the way for transformative applications in industrial digitalization. Among these, “prescriptive analytics” stands out as a powerful tool for optimizing decision-making processes by providing actionable insights and recommendations. However, the efficacy of prescriptive analytics in industrial settings relies heavily on the underlying AI models. In recent years, generative AI (gAI) has been one of the most promising advancements in AI, which holds immense potential for revolutionizing industrial digitalization. gAI refers to algorithms capable of creating new content, such as images, text, or even entire virtual environments, based on patterns learned from data. Nevertheless, the black-box nature of gAI poses challenges in understanding the rationale behind prescriptive recommendations, raising concerns about safety, and bias issues. Here, the primary objective is to develop a multi-modal framework for advancing “prescriptive analytics” in industrial digitalization through gAI.It develops XAI methods based on probabilistic modelling to explain gAI's decisions and investigates multi-modal learning tools for image synthesis and automated analysis of unstructured free text. The proposed prescriptive analytics framework utilizing gAI will drive sustainable industrial digital transformation. The demonstration covers two use cases: Antenna optimization and network maintenance and Undercarriage wear inspection, monitoring, and maintenance.

Bibtex

@misc{Begum7046,
author = {Shahina Begum and Shaibal Barua and Mobyen Uddin Ahmed},
title = {Trust{\_}Gen{\_}Z: Trustworthy Generative AI for Advanced Industrial DigitaliZation},
month = {October},
year = {2024},
url = {http://www.es.mdu.se/publications/7046-}
}