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(A.I.)的研究和实践的飞跃和扩展,维持和支持其运作所需的资源也以越来越快的速度增长。虽然A.I.的创新和应用带来了重大进步,从应用到视觉和自然语言,到医学成像和材料工程等领域的改进,但不应忽视其成本。我们所拥抱的世界中的数据以及A.I.应用的研究和开发数量不断增加,而且研发工作也越来越多,因此,我们肯定会面临不断上升的能源足迹,以维持这些计算预算、数据储存需要和更多。但是,这种可持续的环境,更重要的是,在研究和实践方面,何种环境最适合培育这种可持续的A.I.的最佳条件?在本文件中,我们概述了我们对绿色A.I. -- -- 一个更可持续的、节能的和能觉悟的生态系统 -- -- 用于开发A.I.I.应用、计算和从业者社区的研发和研发工作 -- -- 以及到达那里所需的步骤。我们展示了对各个领域的潜在变化和改进的视觉观点,从AI的底层看,更重要的是,在哪些方面,什么样的环境最适合在研究和实践上培育这种可持续的A.I.I.I.I.和硬件优化,这些研究的目前的行动和前景,以及这些研究/H.C.