The challenge that climate change poses to humanity has spurred a rapidly
developing field of artificial intelligence research focused on climate change
applications. The climate change AI (CCAI) community works on a diverse,
challenging set of problems which often involve physics-constrained ML or
heterogeneous spatiotemporal data. It would be desirable to use automated
machine learning (AutoML) techniques to automatically find high-performing
architectures and hyperparameters for a given dataset. In this work, we
benchmark popular AutoML libraries on three high-leverage CCAI applications:
climate modeling, wind power forecasting, and catalyst discovery. We find that
out-of-the-box AutoML libraries currently fail to meaningfully surpass the
performance of human-designed CCAI models. However, we also identify a few key
weaknesses, which stem from the fact that most AutoML techniques are tailored
to computer vision and NLP applications. For example, while dozens of search
spaces have been designed for image and language data, none have been designed
for spatiotemporal data. Addressing these key weaknesses can lead to the
discovery of novel architectures that yield substantial performance gains
across numerous CCAI applications. Therefore, we present a call to action to
the AutoML community, since there are a number of concrete, promising
directions for future work in the space of AutoML for CCAI. We release our code
and a list of resources at
this https URL.

本文讨论气候变化人工智能的应用，评估了自动机器学习技术在三个高影响的气候变化应用领域的性能，并发现当前的自动机器学习库未能显著超越人设计的模型，但存在一些关键弱点，其中之一是当前大部分自动机器学习技术都是为计算机视觉和自然语言处理应用设计的，所以需要开发出为时空数据设计的搜索空间来寻找新的架构，从而提高性能。为此，作者呼吁自动机器学习社区开展未来工作的具体方向，并发布了代码和一系列资源。