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Prediction Machines: The Simple Economics of Artificial Intelligence

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Ajay Agrawal, Joshua Gans, Avi Goldfarb

When it comes to artificial intelligence (AI), most of us feel a mix of excitement and dread. On the one hand, AI could make our lives significantly easier by automating everyday tasks. At the same time, we can’t help but wonder how this technology might make our jobs — and us — redundant.

As Rotman professors Ajay Agrawal, Joshua Gans and Avi Goldfarb explain in their new book, Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business School Press, April 2018), the key to making sense of AI and the future is to think like an economist.

It’s the great advances in prediction technology that make AI so exciting, the authors explain. From rapid language translation programs to smart speakers like Alexa and Google Home, prediction is the basis for most of the smart technologies we use today.

As prediction continues to get better and cheaper, certain skills and qualities will become more — or less — valuable.

Specifically, our workforce will be hungry for the things that machines cannot master, including human judgment and action. We’ll need managers who are capable of assessing situations, mentoring and taking the steps to execute plans. And data, the input for prediction machines, will become even more valuable.

In Prediction Machines, the authors present frameworks, approaches and anecdotes that set the stage for how to think through new workflows, make decisions and develop strategies.

The books premise is clear, says Goldfarb. “If you can understand the advances in prediction technology, then you can understand AI’s impact on companies and society.”

In many ways, Agrawal, Gans and Goldfarb are uniquely qualified to take on a subject as hefty as AI. These Rotman professors built their academic careers exploring the impact of the last major technological innovation, the internet, on business and the economy.

As early as 2012, when machine learning tools became more prominent, the three professors, who all serve executive functions with the Creative Destruction Lab, began to work more and more with AI companies. At the same time, they were also observing some of the great strides their colleagues from the Department of Computer Science were making in this space.

“We got in in on the ground floor,” says Goldfarb. “We knew that AI would be the next big technology and it was worth dissecting and exploring further.”

This book aligns perfectly with a course that the professors teach for Rotman executive programs. Executives from various organizations and sectors learn the basic principles of AI technology and discuss how to apply new frameworks to their current business practices.

“We move pretty quickly in the course and focus on application,” says Goldfarb. ”Program participants leave thinking about the immediate changes that they can make at work to stay relevant.”

For Goldfarb, he’s thinking about his own plan and there’s a lot to be excited about. It’s quite possible that AI will make his role as a parent easier in some ways — it’s becoming far easier to ask Alexa or Google Home about homework questions and self-driving cars could mean that he won’t need to teach his kids to drive.

“Beyond that, I’m looking forward to the applications and the possibilities we can’t even imagine yet.”

This book was originally published by Harvard Business Review Press in 2018.

Prediction Machines: The Simple Economics of Artificial Intelligence

Ajay Agrawal, Joshua Gans, and Avi Goldfarb
HBR Press

Ajay Agrawal is the Geoffrey Taber chair in entrepreneurship and innovation and professor of entrepreneurship at the University of Toronto's Rotman School of Management where he conducts research on the economics of artificial intelligence, science policy, entrepreneurial finance, and geography of innovation. Professor Agrawal is a research associate at the National Bureau of Economic Research in Cambridge, MA, co-founder of The Next 36 and NextAI, and founder of the Creative Destruction Lab.
Joshua Gans holds the Jeffrey S. Skoll chair in technical innovation and entrepreneurship and is a professor at Rotman (with a cross-appointment in the department of economics). His research is primarily focused on understanding the economic drivers of innovation and scientific progress, and has core interests in digital strategy and antitrust policy. Joshua is the department editor (Strategy) of Management Science, managing director of the Core Research consultancy and writes regularly for HBR and Digitopoly.
Avi Goldfarb is the Rotman chair in artificial intelligence and healthcare, and professor of marketing at Rotman. Avi is also chief data scientist at the Creative Destruction Lab, senior editor at Marketing Science, and a research associate at the National Bureau of Economic Research. His research on the economics of technology has been discussed in White House reports, Congressional testimony, European Commission documents, the Economist, the Globe and Mail, the National Post, CBC Radio, National Public Radio, Forbes, the Atlantic, the New York Times, the Financial Times, the Wall Street Journal, and elsewhere. Along with Ajay Agrawal and Joshua Gans, Avi is the author of the Globe & Mail best-selling book Prediction Machines: The Simple Economics of Artificial Intelligence.