Date of Original Version
© Cambridge University Press
Abstract or Description
Learning by artificial intelligence systems-what I will typically call machine learning-has a distinguished history, and the field has experienced something of a renaissance in the past twenty years. Machine learning consists principally of a diverse set of algorithms and techniques that have been applied to problems in a wide range of domains. Any overview of the methods and applications will inevitably be incomplete, at least at the level of specific algorithms and techniques. There are many excellent introductions to the formal and statistical details of machine learning algorithms and techniques available elsewhere (e.g., Bishop, 1995; Duda, Hart, & Stork, 2000; Hastie, Tibshirani, & Friedman, 2001; Mitchell, 1997). The present chapter focuses on machine learning as a general way of “thinking about the world,” and provides a high-level characterization of the major goals of machine learning. There are a number of philosophical concerns that have been raised about machine learning, but upon closer examination, it is not always clear whether the objections really speak against machine learning specifically. Many seem rather to be directed towards machine learning as a particular instantiation of some more general phenomenon or process. One of the general morals of this chapter is that machine learning is, in many ways, less unusual or peculiar than is sometimes thought.
The Cambridge Handbook of Artificial Intelligence, K. Frankish & W. Ramsey (Eds.), 151-167.