1 Introduction to Human-in-the-Loop Machine Learning
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.