Human-in-the-Loop Machine Learning

1 Introduction to human-in-the-loop machine learning

This chapter covers

• Annotating unlabeled data to create training, validation, and evaluation data
• Sampling the most important unlabeled data items (active learning)
• Incorporating human–computer interaction principles into annotation
• Implementing transfer learning to take advantage of information in existing models

Unlike robots in the movies, most of today’s artificial intelligence (AI) cannot learn by itself; instead, it relies on intensive human feedback. Probably 90% of machine learning applications today are powered by supervised machine learning. This figure covers a wide range of use cases. An autonomous vehicle can drive you safely down the street because humans have spent thousands ...