Machine learning (ML) has recently gained in popularity, spurred by well-publicized advances like deep learning and widespread commercial interest in big data analytics. Despite the enthusiasm, some renowned experts of the field have expressed skepticism, which is justifiable given the disappointment with the previous wave of neural networks and other AI techniques. On the other hand, new fundamental advances like the ability to train neural networks with a large number of layers for hierarchical feature learning may present significant new technological and commercial opportunities. This paper critically examines the main advances in deep learning. In addition, connections with another ML branch of reinforcement learning are elucidated and its role in control and decision problems is discussed. Implications of these advances for the fields of process and energy systems engineering are also discussed.

机器学习（ML）在深度学习等广泛宣传的进步以及对大数据分析的广泛商业兴趣的刺激下，最近已变得越来越流行。尽管有热情，但该领域的一些知名专家表示了怀疑，鉴于以前的神经网络和其他AI技术浪潮令人失望，这是有道理的。另一方面，诸如训练具有大量层次的神经网络以进行分层特征学习的能力等新的基本进步可能会带来大量的新技术和商业机会。本文批判性地研究了深度学习的主要进展。此外，阐明了与强化学习的另一个ML分支的联系，并讨论了其在控制和决策问题中的作用。