Prediction Machines: The Simple Economics of Artificial Intelligence
Recent excitement in artificial intelligence has been driven by advances in machine learning. In this sense, AI is a prediction technology. It uses data you have to fill in information you don't have. These advances can be seen as a drop in the cost of prediction. The framing generates some powerful, but easy-to-understand implications. As the cost of something falls, we will do more of it. Cheap prediction means more prediction. Also, as the cost of something falls, it affects the value of other things. As machine prediction gets cheap, human prediction becomes less valuable while data and human judgment become more valuable. Business models that are constrained by uncertainty can be transformed, and organizations with an abundance of data and a good sense of judgment have an advantage. Details at www.predictionmachines.ai.
Avi Goldfarb is Ellison Professor of Marketing at the Rotman School of Management, University of Toronto, and coauthor of the book Prediction Machines: The Simple Economics of Artificial Intelligence. Avi is also Senior Editor at Marketing Science, Chief Data Scientist at the Creative Destruction Lab, and Research Associate at the National Bureau of Economic Research. Avi's research focuses on the opportunities and challenges of the digital economy. He has published over 60 academic articles in a variety of outlets in marketing, statistics, law, computing, and economics. This work has been discussed in White House reports, Congressional testimony, European Commission documents, The Economist, Globe and Mail, National Post, CBC Radio, National Public Radio, Forbes, Fortune, Atlantic, New York Times, Financial Times, Wall Street Journal, and elsewhere. He holds a Ph.D. in economics of Northwestern University.