Modern systems can augment people’s capabilities by using machine-learned models to surface intelligent behaviors. Unfortunately, building these models remains challenging and beyond the reach of non-machine learning experts. We describe interactive machine teaching (IMT) and its potential to simplify the creation of machine-learned models. One of the key characteristics of IMT is its iterative process in which the human-in-the-loop takes the role of a teacher teaching a machine how to perform a task. We explore alternative learning theories as potential theoretical foundations for IMT, the intrinsic human capabilities related to teaching, and how IMT systems might leverage them. We argue that IMT processes that enable people to leverage these capabilities have a variety of benefits, including making machine learning methods accessible to subject-matter experts and the creation of semantic and debuggable machine learning (ML) models. We present an integrated teaching environment (ITE) that embodies principles from IMT, and use it as a design probe to observe how non-ML experts do IMT and as the basis of a system that helps us study how to guide teachers. We explore and highlight the benefits and challenges of IMT systems. We conclude by outlining six research challenges to advance the field of IMT.

现代系统可以通过使用机器学习的模型来显示智能行为来增强人们的能力。不幸的是，建立这些模型仍然具有挑战性，并且超出了非机器学习专家的范围。我们描述了交互式机器教学（IMT）及其简化了机器学习模型创建的潜力。IMT的主要特征之一是它的迭代过程，在此过程中，在环人员扮演老师的角色，教机器如何执行任务。我们探索替代学习理论，作为IMT的潜在理论基础，与教学相关的内在人类能力以及IMT系统如何利用它们。我们认为，使人们能够利用这些功能的IMT流程具有多种好处，包括使主题专家可以使用机器学习方法，以及创建语义和可调试机器学习（ML）模型。我们提供了一个集成的教学环境（ITE），体现了IMT的原理，并将其用作设计探究，以观察非语言专家如何进行IMT，并作为帮助我们研究如何指导老师的系统的基础。我们探索并强调了IMT系统的好处和挑战。最后，我们概述了六项研究挑战以推进IMT领域。我们探索并强调了IMT系统的好处和挑战。最后，我们概述了六项研究挑战以推进IMT领域。我们探索并强调了IMT系统的好处和挑战。最后，我们概述了六项研究挑战以推进IMT领域。