Artificial intelligence, machine learning, and autonomous technologies in the mining industry
Publication Type
Journal Article
Publication Date
4-2019
Abstract
The implementation of artificial intelligence (AI), machine learning, and autonomous technologies in the mining industry started about a decade ago with autonomous trucks. Artificial intelligence, machine learning, and autonomous technologies provide many economic benefits for the mining industry through cost reduction, efficiency, and improving productivity, reducing exposure of workers to hazardous conditions, continuous production, and improved safety. However, the implementation of these technologies has faced economic, financial, technological, workforce, and social challenges. This article discusses the current status of AI, machine learning, and autonomous technologies implementation in the mining industry and highlights potential areas of future application. The article presents the results of interviews with some of the stakeholders in the industry and what their perceptions are about the threats, challenges, benefits, and potential impacts of these advanced technologies. The article also presents their views on the future of these technologies and what are some of the steps needed for successful implementation of these technologies in this sector.
Keywords
Artificial intelligence, Autonomous technology, Autonomous trucks, Challenges of AI and machine learning, Machine learning, Mining industry
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Information Systems and Management
Publication
Journal of Database Management
Volume
30
Issue
2
First Page
67
Last Page
79
ISSN
1063-8016
Identifier
10.4018/978-1-6684-3694-3.ch024
Publisher
IGI Global
Citation
HYDER, Zeshan; SIAU, Keng; and NAH, Fiona Fui-hoon.
Artificial intelligence, machine learning, and autonomous technologies in the mining industry. (2019). Journal of Database Management. 30, (2), 67-79.
Available at: https://ink.library.smu.edu.sg/sis_research/9554
Additional URL
https://doi.org/10.4018/978-1-6684-3694-3.ch024