Developing effective predictive models becomes challenging in dynamic environments that continuously produce data and constantly change. Continual Learning (CL) and Streaming Machine Learning (SML) are two research areas that tackle this arduous task. We put forward a unified setting that harnesses the benefits of both CL and SML: their ability to quickly adapt to non-stationary data streams without forgetting previous knowledge. We refer to this setting as Streaming Continual Learning (SCL). SCL does not replace either CL or SML. Instead, it extends the techniques and approaches considered by both fields. We start by briefly describing CL and SML and unifying the languages of the two frameworks. We then present the key features of SCL. We finally highlight the importance of bridging the two communities to advance the field of intelligent systems.

翻译：在持续产生数据且不断变化的动态环境中，开发有效的预测模型变得具有挑战性。持续学习（CL）与流式机器学习（SML）是应对这一艰巨任务的两个研究领域。我们提出了一种统一框架，该框架融合了CL与SML的双重优势：在不遗忘先前知识的前提下快速适应非平稳数据流的能力。我们将此框架称为流式持续学习（SCL）。SCL并非要取代CL或SML，而是扩展了两个领域所考虑的技术与方法。我们首先简要描述CL与SML，并统一两个框架的术语体系。随后阐述SCL的核心特征。最后强调连接这两个研究社群对于推进智能系统领域发展的重要性。