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, 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.
Abstract (translated)
人工智能(A.I.)研究和实践的发展迅速,需要维持和支持它们的运作的资源也在以递增的速度增长。虽然人工智能的创新和应用已经取得了重大进展,包括应用、视觉和自然语言、以及改善医学影像和材料工程等领域,但它们的成本不容忽视。随着我们越来越依赖数据和人工智能应用,以及研究和开发人工智能应用的不断增加,我们肯定会面临日益增长的能源足迹,以维持这些计算预算、数据存储需求以及其他更多需求。但这种方式是否可持续,更重要的是,哪种环境最适合 nurturing 这样的可持续 A.I. 在研究和实践中?在本文中,我们概述了我们对绿色 A.I. 的未来展望 - 一个更加可持续、高效和能源意识的生态系统,用于开发和部署 across 研究、计算和从业者社区的人工智能 - 以及实现这些目标的所需步骤。我们呈现了从数据center/HPC 的地面层到当前人工智能研究和实践中 incentives 结构的各个潜在变化和改进之处的俯瞰视角。我们希望这些点能促使进一步讨论和采取行动,解决这些问题及其可能解决方案。
URL
https://arxiv.org/abs/2301.11581
