Artificial Intelligence (AI) is currently spearheaded by machine learning (ML) methods such as deep learning (DL) which have accelerated progress on many tasks thought to be out of reach of AI. These ML methods can often be compute hungry, energy intensive, and result in significant carbon emissions, a known driver of anthropogenic climate change. Additionally, the platforms on which ML systems run are associated with environmental impacts including and beyond carbon emissions. The solution lionized by both industry and the ML community to improve the environmental sustainability of ML is to increase the efficiency with which ML systems operate in terms of both compute and energy consumption. In this perspective, we argue that efficiency alone is not enough to make ML as a technology environmentally sustainable. We do so by presenting three high level discrepancies between the effect of efficiency on the environmental sustainability of ML when considering the many variables which it interacts with. In doing so, we comprehensively demonstrate, at multiple levels of granularity both technical and non-technical reasons, why efficiency is not enough to fully remedy the environmental impacts of ML. Based on this, we present and argue for systems thinking as a viable path towards improving the environmental sustainability of ML holistically.

翻译：人工智能（AI）当前主要由深度学习等机器学习方法引领，这些方法加速了诸多曾被认为超出AI能力范围的任务的进展。然而，这些机器学习方法往往计算密集、能耗巨大，并产生显著碳排放——这是人为气候变化的一个已知驱动因素。此外，机器学习系统运行的平台还伴随着碳排放之外的环境影响。工业界和机器学习社区为提升ML环境可持续性而推崇的解决方案，是提高ML系统在计算和能耗两方面的运行效率。在本评论中，我们论证单一效率不足以使ML作为一种技术实现环境可持续。为此，我们从三个高层次角度揭示：当考虑与效率相互作用的众多变量时，效率对ML环境可持续性的影响存在矛盾。通过这一分析，我们在多个技术与非技术细粒度层面充分论证了为何效率无法完全补救ML的环境影响。基于此，我们提出并论证将系统思维作为全面改善ML环境可持续性的可行路径。