Towards Real-world Continual Learning on Unrestricted Task Steams

该项目旨在使机器能够不断学习而不会忘记，并从包含不同任务的顺序数据流中积累知识。该项目期望将持续学习推进到长期和复杂的不受限制的现实世界任务流，并推动人工智能朝着能够在与世界互动中自动进化的人类级别的智能发展。该项目的预期结果包括用于在现实世界场景中处理不受限制的任务流的范式转换的持续学习框架和技术。它们将通过增强人工智能的适用性，使社会和经济在国内和国际上受益。

This project aims to enable machines to continually learn without forgetting and accumulate knowledge from the sequential data streams containing diverse tasks. This project expects to advance the continual learning to unrestricted real-world task steams that are long-term and complex and promote artificial intelligence toward the human-level intelligence that can automatically evolve during interaction with the world. Expected outcomes of this project include the paradigm-shifting continual learning framework and techniques for handling unrestricted task steams in real-world scenarios. They will benefit society and the economy nationally and internationally by enhancing the applicability of artificial intelligence.