Harnessing Large Language Models for Cognitive Assistants in Factories

Conference paper (2023)

Authors

Mina Foosherian University of Bremen
C.W. Wang Human-Centred Artificial Intelligence - IDE
Research Group
Human-Centred Artificial Intelligence (IDE) (TU Delft)
Copyright: © 2023 S. Kernan Freire, Mina Foosherian, C.W. Wang, E. Niforatos
DOI
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https://doi.org/10.1145/3571884.3604313
Knowledge management Knowledge sharing Cognitive assistant Conversational user interfaces Human-centered AI Industry 5.0
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Published Date

2023

Language

English

Reuse Rights

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Faculty

Industrial Design Engineering

Department

Sustainable Design Engineering

Research Group

Human-Centred Artificial Intelligence

Abstract

As agile manufacturing expands and workforce mobility increases, the importance of efficient knowledge transfer among factory workers grows. Cognitive Assistants (CAs) with Large Language Models (LLMs), like GPT-3.5, can bridge knowledge gaps and improve worker performance in manufacturing settings. This study investigates the opportunities, risks, and user acceptance of LLM-powered CAs in two factory contexts: textile and detergent production. Several opportunities and risks are identified through a literature review, proof-of-concept implementation, and focus group sessions. Factory representatives raise concerns regarding data security, privacy, and the reliability of LLMs in high-stake environments. By following design guidelines regarding persistent memory, real-time data integration, security, privacy, and ethical concerns, LLM-powered CAs can become valuable assets in manufacturing settings and other industries.

Files

3571884.3604313.pdf
(.pdf | 0.844 Mb)