TY - GEN
T1 - Harnessing Large Language Models for Cognitive Assistants in Factories
AU - Kernan Freire, Samuel
AU - Foosherian, Mina
AU - Wang, Chaofan
AU - Niforatos, Evangelos
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - cognitive assistant
KW - conversational user interfaces
KW - human-centered AI
KW - industry 5.0
KW - knowledge management
KW - knowledge sharing
UR - http://www.scopus.com/inward/record.url?scp=85167837764&partnerID=8YFLogxK
U2 - 10.1145/3571884.3604313
DO - 10.1145/3571884.3604313
M3 - Conference contribution
AN - SCOPUS:85167837764
T3 - Proceedings of the 5th International Conference on Conversational User Interfaces, CUI 2023
BT - Proceedings of the 5th International Conference on Conversational User Interfaces, CUI 2023
PB - Association for Computing Machinery (ACM)
T2 - 5th International Conference on Conversational User Interfaces, CUI 2023
Y2 - 19 July 2023 through 21 July 2023
ER -