Continual learning is a sub-field of machine learning, which aims to allow
machine learning models to continuously learn on new data, by accumulating
knowledge without forgetting what was learned in the past. In this work, we
take a step back, and ask: "Why should one care about continual learning in the
first place?". We set the stage by surveying recent continual learning papers
published at three major machine learning conferences, and show that
memory-constrained settings dominate the field. Then, we discuss five open
problems in machine learning, and even though they seem unrelated to continual
learning at first sight, we show that continual learning will inevitably be
part of their solution. These problems are model-editing, personalization,
on-device learning, faster (re-)training and reinforcement learning. Finally,
by comparing the desiderata from these unsolved problems and the current
assumptions in continual learning, we highlight and discuss four future
directions for continual learning research. We hope that this work offers an
interesting perspective on the future of continual learning, while displaying
its potential value and the paths we have to pursue in order to make it
successful. This work is the result of the many discussions the authors had at
the Dagstuhl seminar on Deep Continual Learning, in March 2023.

连续学习是机器学习的一个子领域，本文调查了连续学习研究中的内存受限设置、未解决的问题以及未来的研究方向。