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, energy-efficient and energy-aware ecosystem for developing A.I. across the research, computing, and practitioner communities alike -- and the steps required to arrive there. We present a bird's eye view of various areas for potential changes and improvements from the ground floor of AI's operational and hardware optimizations for datacenters/HPCs to the current incentive structures in the world of A.I. research and practice, and more. We hope these points will spur further discussion, and action, on some of these issues and their potential solutions.

翻译：随着人工智能研究与应用的快速发展，支撑其运行所需的资源也以更快的速度增长。尽管人工智能的创新与应用在视觉、自然语言处理等方向取得重大突破，并推动了医学影像、材料工程等领域的进步，但其带来的成本却不容忽视。当我们拥抱数据量持续膨胀、人工智能研发不断深化的世界时，支撑计算预算、数据存储等需求所产生的能源足迹必将日益加重。但这一趋势是否可持续？更重要的是，何种环境最有利于在研究与实践中培育可持续的人工智能？本文阐述了我们对绿色人工智能的展望——一个面向研究、计算与实践社区的更可持续、更节能、更具能效意识的人工智能开发生态系统，以及实现这一愿景所需的步骤。我们从数据中心/高性能计算的运营与硬件优化，到人工智能研究与实践领域的现有激励机制等维度，全面审视了潜在变革与改进的方向。希望这些观点能引发对相关问题及其解决方案的进一步讨论与行动。