In "Prediction Machines," economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb explore the advancement and growing use of artificial intelligence (AI). The key to AI is not actually intelligence but prediction. This text looks at the value of prediction and data, the importance of trade-offs, and the impact of AI in the workplace. Beneficial to business leaders, financial analysts, policy makers, and students, "Prediction Machines" offers insights, tools, and strategies on how to adapt businesses to the world's ever-growing use of AI. Part 1-consisting of chapters 3 through 6-discusses prediction itself, including how prediction machines work, why many call prediction machines artificial intelligence, the need for data with prediction, and how prediction machines affect the division of labor within organizations. Chapter 4 looks at machine learning versus statistics and specifically contrasts machine learning and regression analysis. Machine learning improvements are often described as advances in artificial intelligence rather than prediction because the systems learn and improve over time, they produce more accurate predictions than other approaches, and their prediction accuracy allows them to perform tasks previously considered to only be within the domain of human intelligence.