@inproceedings{38eb5e3989ad468d98ee361e89eec031,
title = "Towards Industrial Foundation Models: Framework, Key Issues and Potential Applications",
abstract = "Foundation models have demonstrated remarkable capabilities in various tasks such as natural language processing, content generation, and complex reasoning and have the potential to spark new technology and application revolutions in the industrial domain. However, Industrial Foundation Models (IFMs) remain almost unexplored, and the industrial sector has domain-specific issues and challenges to address when harnessing the capabilities of foundation models. Therefore, we introduce the concept and construction paradigm of IFMs and propose a 5-dimensional general framework of the IFMs. Moreover, we present the key research issues and technologies of IFMs and discuss some advanced and potential industrial applications. We hope this paper can serve as a useful resource for researchers seeking to innovate within the domain of IFMs.",
keywords = "deep learning, foundation models, industrial foundation models",
author = "Yingchao Wang and Chen Yang and Shulin Lan and Weilun Fei and Lihui Wang and Huang, \{George Q.\} and Liehuang Zhu",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 27th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2024 ; Conference date: 08-05-2024 Through 10-05-2024",
year = "2024",
month = jul,
doi = "10.1109/CSCWD61410.2024.10580089",
language = "English",
series = "Proceedings of the 2024 27th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3269--3274",
editor = "Weiming Shen and Weiming Shen and Jean-Paul Barthes and Junzhou Luo and Tie Qiu and Xiaobo Zhou and Jinghui Zhang and Haibin Zhu and Kunkun Peng and Tianyi Xu and Ning Chen",
booktitle = "Proceedings of the 2024 27th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2024",
}