Data and System Perspectives of Sustainable Artificial Intelligence
It tackles the problem of high energy use in AI for researchers and practitioners, but is incremental as it reviews existing approaches rather than introducing new methods.
The article addresses the environmental impact of AI by discussing issues and solutions from data and system perspectives, focusing on data acquisition, processing, and model training/inference to reduce computing power consumption.
Sustainable AI is a subfield of AI for concerning developing and using AI systems in ways of aiming to reduce environmental impact and achieve sustainability. Sustainable AI is increasingly important given that training of and inference with AI models such as large langrage models are consuming a large amount of computing power. In this article, we discuss current issues, opportunities and example solutions for addressing these issues, and future challenges to tackle, from the data and system perspectives, related to data acquisition, data processing, and AI model training and inference.