Lifelong machine learning: a paradigm for continuous learning
Machine learning (ML) has been instrumental for the advances of both data analysis and artificial intelligence (AI) The recent success of deep learning brings it to a new height. ML algorithms have been successfully used in almost all areas of applications in industry, science, and engineering. The current dominant paradigm for ML is to run an ML algorithm on a given dataset to generate a model. The model is then applied in real life tasks. We call this paradigm isolated learning because it does not consider any other related information or the knowledge learned in the past. The fundamental problem with this isolated learning is that it has no memory. It does not retain knowledge learned in the past and use it to help future learning. As a result, a large number of training examples are needed to learn effectively. For supervised learning, training data labeling is often done manually, which is very labor intensive and time consuming. Since the world simply has too many possible tasks, it is almost impossible to label a large number of examples for each task in order for an ML algorithm to learn. To make matters worse, everything also changes constantly, and the labeling thus needs to be done continuously, which is a daunting task. The current isolated learning paradigm is probably not suitable for building a truly intelligent system, but only suitable for solving problems in very narrow domains. We human beings seem to learn quite differently. We never learn in isolation. Instead, we always retain and accumulate the knowledge learned in the past and use it seamlessly in future learning. Over time we learn more and more and become more and more knowledgeable, and more and more effective at learning. Lifelong machine learning (LML) (or simply lifelong learning) aims to mimic this human learning
Frontiers of Computer Science