Data and System Perspectives of Sustainable Artificial Intelligence
Tao Xie, David Harel, Dezhi Ran, Zhenwen Li, Maoliang Li, Zhi Yang,, Leye Wang, Xiang Chen, Ying Zhang, Wentao Zhang, Meng Li, Chen Zhang, Linyi, Li, Assaf Marron
TL;DR
This paper discusses the challenges and opportunities in developing sustainable AI systems, focusing on data and system perspectives to reduce environmental impact of AI training and inference.
Contribution
It provides a comprehensive overview of current issues, potential solutions, and future challenges in sustainable AI from data and system viewpoints.
Findings
Identifies key issues in data acquisition and processing for sustainable AI.
Highlights opportunities for system-level improvements to reduce environmental impact.
Discusses future research directions and challenges in sustainable AI.
Abstract
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.
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