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PREDICTION MACHINES: The Simple Economics of Artificial Intelligence.

April 17, 2018

Toronto – Artificial Intelligence does the seemingly impossible, magically bringing machines to life - driving cars, trading stocks, and teaching children. But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future.

But in PREDICTION MACHINES: The Simple Economics of Artificial Intelligence (HBR Press, April 17, 2018) University of Toronto’s Rotman School of Management professors and economists, Ajay Agrawal, Joshua Gans, and Avi Goldfarb recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs.

"What does AI mean for your business? Read this book to find out."

-- Hal Varian, Chief Economist, Google

"This book makes artificial intelligence easier to understand by recasting it as a new, cheap commodity--predictions. It's a brilliant move. I found the book incredibly useful."

-Kevin Kelly, founding executive editor, Wired; author, ‘What Technology Wants and The Inevitable’

The impact of AI will be so significant because prediction is a key input for decision making, and decision making is everywhere. However, decision making also relies on another component: judgment, which is firmly in the realm of humans, not machines. Making prediction cheaper means that we can make more predictions more accurately and utilize them in combination with human judgment. Once we can separate tasks into components of prediction and judgment, then we can optimize the interface between humans and machines.

The authors are also the founders of the Creative Destruction Lab (CDL), home to the greatest concentration of AI-oriented startups of any program on Earth. Being so close to so many applications of AI gives them a front seat to focus on how this technology affects business strategy. PREDICTION MACHINES is their bridge between the technologist and the business practitioner. In it, they tackle these three key points:

» The current wave of advances in AI doesn’t actually bring us intelligence but instead a critical component of intelligence: prediction.

» Prediction is a central input into decision-making. The new and poorly understood implications of advances in prediction technology can be combined with the old and well-understood logic of decision theory from economics to deliver a series of insights to help navigate the approach to AI.

» Answers based on AIs involve trade-offs: more speed, less accuracy; more autonomy, less control; more data, less privacy. PREDICTION MACHINES provides a method for identifying the trade-offs associated with each AI-related decision.

More than just an account of AI's powerful capabilities, PREDICTION MACHINES shows managers how they can most effectively leverage AI, disrupting business as usual only where required, and provides businesses with a toolkit to navigate the coming wave of challenges and opportunities.


Ajay Agrawal is Professor of Strategic Management and Peter Munk Professor of Entrepreneurship at the University of Toronto's Rotman School of Management. He is also a Research Associate at the National Bureau of Economic Research, cofounder of The Next 36 and Next AI, and founder of the Creative Destruction Lab.

Joshua Gans is Professor of Strategic Management and the holder of the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship at the Rotman School of Management, University of Toronto. Prof. Gans is a frequent contributor to outlets like the New York Times, Harvard Business Review, Forbes, Slate, and the Financial Times.

Avi Goldfarb is the Ellison Professor of Marketing at the Rotman School of Management, University of Toronto. He is also Chief Data Scientist at the Creative Destruction Lab, Senior Editor at Marketing Science, a Fellow at Behavioral Economics in Action at Rotman, and a Research Associate at the National Bureau of Economic Research.

The Simple Economics of Artificial Intelligence
Ajay Agrawal, Joshua Gans, and Avi Goldfarb
Harvard Business Review Press

Get your copy today!


Related Videos

Ajay Agrawal speaks at the Machine Learning and the Market for Intelligence Conference, October 2017

Avi Goldfarb on Understanding the Economics of AI, #HBRLive

Joshua Gans discusses how to apply AI to your business at this HBR Webinar.

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