Rapid advances in artificial intelligence (AI) in the last decade have largely been built upon the wide applications of deep learning (DL). However, the high carbon footprint yielded by larger and larger DL networks becomes a concern for sustainability. Furthermore, DL decision mechanism is somewhat obsecure and can only be verified by test data. Green learning (GL) has been proposed as an alternative paradigm to address these concerns. GL is characterized by low carbon footprints, small model sizes, low computational complexity, and logical transparency. It offers energy-effective solutions in cloud centers as well as mobile/edge devices. GL also provides a clear and logical decision-making process to gain people's trust. Several statistical tools have been developed to achieve this goal in recent years. They include subspace approximation, unsupervised and supervised representation learning, supervised discriminant feature selection, and feature space partitioning. We have seen a few successful GL examples with performance comparable with state-of-the-art DL solutions. This paper offers an introduction to GL, its demonstrated applications, and future outlook.
翻译:过去十年中,人工智能(AI)的快速进步在很大程度上建立在广泛应用深层学习(DL)的基础之上。然而,大型和大型DL网络产生的高碳足迹成为了对可持续性的关切。此外,DL决定机制有些不稳,只能通过测试数据加以核实。绿色学习(GL)是解决这些关切的替代范例。GL的特点是低碳足迹低、模型小、计算复杂性低和逻辑透明。它为云中心以及移动/前沿装置提供了高能效的解决方案。GL还提供了一个清晰和符合逻辑的决策过程,以赢得人们的信任。近年来,为实现这一目标,开发了几种统计工具,其中包括子空间近似、不受监督和监管的代表学习、监督的特征选择和地貌空间分割。我们看到了几个成功的GL实例,其性能可与最新DL解决方案相比。本文介绍了GL、其示范应用和未来展望。