效率还不够：环境可持续型AI的批判性视角
Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI
AI总结：
该研究指出仅靠提升计算与能源效率不足以解决机器学习的环境影响问题，提出需采用系统思维从多维度全面推动环境可持续型AI发展。
摘要：
当前，人工智能（AI）的发展以深度学习等机器学习（ML）方法为核心驱动力，这些方法推动了许多曾被认为AI无法实现的任务取得快速进展。但这类新兴的ML方法通常计算需求大、能耗高，还会产生大量温室气体排放，而温室气体是人为气候变化的已知驱动因素。此外，运行ML系统的平台所带来的环境影响，不止于能耗引发的碳排放。\n工业界和ML社区普遍推崇的提升ML环境可持续性的主要方案，是提高ML系统运行的计算效率和能源效率。在本观点文章中，我们提出，为了让ML更具环境可持续性，是时候超越效率这单一维度了。我们提出了影响ML效率的诸多变量与ML环境可持续性之间存在的三个高层差异。首先，我们讨论了计算效率并不等同于能源效率或碳效率。其次，我们阐述了效率在ML模型整个生命周期中对运行排放产生的意外影响。最后，我们探讨了未被效率纳入考量的更广泛的环境影响。这些差异说明了为何仅靠效率不足以弥补ML带来的不利环境影响。相反，我们主张将系统思维作为全面提升ML环境可持续性的下一步方向。
英文摘要：
Artificial intelligence (AI) is currently spearheaded by machine learning (ML) methods such as deep learning which have accelerated progress on many tasks thought to be out of reach of AI. These recent ML methods are often compute hungry, energy intensive, and result in significant green house gas emissions, a known driver of anthropogenic climate change. Additionally, the platforms on which ML systems run are associated with environmental impacts that go beyond the energy consumption driven carbon emissions. The primary solution lionized by both industry and the ML community to improve the environmental sustainability of ML is to increase the compute and energy efficiency with which ML systems operate. In this perspective, we argue that it is time to look beyond efficiency in order to make ML more environmentally sustainable. We present three high-level discrepancies between the many variables that influence the efficiency of ML and the environmental sustainability of ML. Firstly, we discuss how compute efficiency does not imply energy efficiency or carbon efficiency. Second, we present the unexpected effects of efficiency on operational emissions throughout the ML model life cycle. And, finally, we explore the broader environmental impacts that are not accounted by efficiency. These discrepancies show as to why efficiency alone is not enough to remedy the adverse environmental impacts of ML. Instead, we argue for systems thinking as the next step towards holistically improving the environmental sustainability of ML.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
