经过70 多年的发展，机器翻译取得了巨大成就。特别是近年来，随着神经网络机器翻译（NMT）的出现，翻译质量得到了极大提高。本文首先回顾机器翻译的发展历程，从基于规则的机器翻译、基于实例的机器翻译，到统计机器翻译。然后详细介绍神经网络机器翻译技术的进展，包括基本原理和当前主流模型（Transformer），以及多语言翻译。接下来介绍机器同声传译的最新进展，探讨如何在翻译质量和时间延迟方面取得平衡。之后，介绍机器翻译丰富的产品形式和应用。最后，简要讨论机器翻译面临的挑战和未来的研究方向。

After more than 70 years of evolution, great achievements have been made in machine translation. Especially in recent years, translation quality has been greatly improved with the emergence of neural machine translation (NMT). In this article, we first review the history of machine translation from rule-based machine translation to example-based machine translation and statistical machine translation. We then introduce NMT in more detail, including the basic framework and the current dominant framework, Transformer, as well as multilingual translation models to deal with the data sparseness problem. In addition, we introduce cutting-edge simultaneous translation methods that achieve a balance between translation quality and latency. We then describe various products and applications of machine translation. At the end of this article, we briefly discuss challenges and future research directions in this field.