A Green(er) World for A.I
Dan Zhao, Nathan C. Frey, Joseph McDonald, Matthew Hubbell, David, Bestor, Michael Jones, Andrew Prout, Vijay Gadepally, Siddharth Samsi

TL;DR
This paper advocates for a sustainable and energy-efficient ecosystem for AI development, emphasizing operational, hardware, and incentive reforms to reduce AI's growing environmental footprint.
Contribution
It provides a comprehensive outlook on creating a greener AI ecosystem, highlighting necessary changes in research practices, hardware optimization, and incentive structures.
Findings
Identifies key areas for reducing AI's energy consumption.
Proposes strategies for sustainable AI research and deployment.
Encourages community discussion on green AI initiatives.
Abstract
As research and practice in artificial intelligence (A.I.) grow in leaps and bounds, the resources necessary to sustain and support their operations also grow at an increasing pace. While innovations and applications from A.I. have brought significant advances, from applications to vision and natural language to improvements to fields like medical imaging and materials engineering, their costs should not be neglected. As we embrace a world with ever-increasing amounts of data as well as research and development of A.I. applications, we are sure to face an ever-mounting energy footprint to sustain these computational budgets, data storage needs, and more. But, is this sustainable and, more importantly, what kind of setting is best positioned to nurture such sustainable A.I. in both research and practice? In this paper, we outline our outlook for Green A.I. -- a more sustainable,…
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