Chapter 4. Introduction to Human-in-the-Loop Machine Learning

Chapter 1 from Human-in-the-Loop Machine Learning by Robert Munro

This chapter covers

  • An overview of Human-in-the-Loop Machine Learning architectures and the key components
  • An introduction to Annotation
  • An introduction to Active Learning
  • An introduction to Human-Computer Interaction
  • An introduction to Transfer Learning

Unlike robots in the movies, most of today’s Artificial Intelligence (AI) cannot learn by itself: it relies on intensive human feedback. Probably 90% of Machine Learning applications today are powered by Supervised Machine Learning. This covers a wide range of use cases: an autonomous vehicle can drive you safely down the street because humans have spent thousands of hours telling it when its sensors are seeing a ‘pedestrian’, ‘moving vehicle’, ‘lane marking’, and every other relevant object; your in-home device knows what to do when you say ‘turn up the volume’, because humans have spent thousands of hours telling it how to interpret different commands; and your Machine Translation service can translate between languages because it has been trained on thousands (or maybe millions) of human-translated texts.

1.1       The Basic Principles of Human-in-the-Loop Machine Learning

1.2       Introducing Annotation

1.3       Introducing Active Learning: improving the speed and reducing the cost of training data

1.4       Machine Learning and Human-Computer Interaction

1.5       Machine Learning-Assisted Humans vs Human-Assisted Machine Learning

1.6       Transfer learning to kick-start your models

1.7       What to expect in this text

1.8       Summary