The articles in this special section examine machine learning (ML) for end consumers. ML is a discipline that grew out of artificial intelligence (AI). At a minimum, an intelligent agent needs to perceive the environment around it, deliberate, and take the best course of actions to maximize some actual or estimated performance measures. ML was originally a trait of AI that concerned training intelligent agents to perform tasks that cannot be preprogrammed. ML has received much attention recently with advances in technologies that permeate many facets of our everyday lives, e.g., autonomous vehicles, lifelike chatbots, speech synthesis and recognition, intelligent web search, financial forecasting, personal healthcare, traffic navigation, and many other consumer applications. Key enablers that have propelled ML to the forefront of AI research include availability of vast volumes of data, algorithmic advancements that have enabled effective training of deep neural networks, and accessibility and affordability of powerful computing resources. Consequently, novel learning paradigms have been developed beyond the classical discriminative supervised, unsupervised, and semisupervised approaches. Notable novel learning paradigms include reinforcement learning, transfer learning, lifelong learning, generative adversarial learning, and more.

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面向最终用户的机器学习

此特殊部分中的文章为最终用户研究了机器学习（ML）。机器学习是一门源于人工智能（AI）的学科。至少，智能代理需要感知，仔细思考并采取最佳行动，以最大化某些实际或估计的性能指标。ML最初是AI的一个特征，它涉及培训智能代理以执行无法预先编程的任务。近年来，随着自动驾驶，逼真的聊天机器人，语音合成和识别，智能网络搜索，财务预测，个人医疗保健，交通导航以及许多其他消费类应用渗透到我们日常生活的方方面面，ML受到了广泛关注。将ML推向AI研究前沿的关键推动力包括海量数据的可用性，算法的改进（使深度神经网络的有效训练成为可能）以及强大的计算资源的可访问性和可负担性。因此，除了经典的区分监督，无监督和半监督方法之外，还开发了新颖的学习范式。新颖的学习范例包括强化学习，迁移学习，终身学习，生成对抗性学习等等。超越经典的区分监督，无监督和半监督方法，已经开发了新颖的学习范式。新颖的学习范例包括强化学习，迁移学习，终身学习，生成对抗性学习等等。超越经典的区分监督，无监督和半监督方法，已经开发了新颖的学习范式。新颖的学习范例包括强化学习，迁移学习，终身学习，生成对抗性学习等等。