工厂(面向对象编程)
认知
劳动力
敏捷软件开发
计算机科学
可靠性(半导体)
桥(图论)
知识管理
业务
心理学
软件工程
功率(物理)
医学
物理
量子力学
神经科学
内科学
经济
程序设计语言
经济增长
作者
Samuel Kernan Freire,Mina Foosherian,Chaofan Wang,Evangelos Niforatos
标识
DOI:10.1145/3571884.3604313
摘要
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.
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