Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent.

《终身机器学习》第二版介绍了一种先进的机器学习范式，它通过积累过去的知识不断学习，然后用于未来的学习和问题解决。相比之下，当前占主导地位的机器学习范式是独立学习的：给定一个训练数据集，它在数据集上运行机器学习算法，生成一个模型，然后用于其预期应用。它不试图保留所学知识并在后续学习中使用。与这个孤立的系统不同，人类只需要几个例子就可以有效地学习，这正是因为我们的学习是由知识驱动的：过去学习的知识帮助我们用很少的数据或努力学习新事物。终身学习旨在模仿这种能力，因为没有它，人工智能系统就不能被认为是真正的智能系统。