By @SimonCocking, review of PREDICTION MACHINES The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans and Avi Goldfarb
The idea of artificial intelligence (AI) – job-killing robots, self-driving cars, and self-managing organizations – captures the imagination, evoking a combination of wonder and dread for those of us who will have to deal with the consequences. But what if it’s not quite so complicated?
The real job of artificial intelligence, argue these three eminent economists, is to lower the cost of prediction. In PREDICTION MACHINES: The Simple Economics of Artificial Intelligence (Harvard Business Review Press) University of Toronto’s Rotman School of Business professors and economists, Ajay Agrawal, Joshua Gans, and Avi Goldfarb examine that the constant challenge for all managers is to make decisions under uncertainty and show how AI contributes by making knowing what’s coming in the future cheaper and more certain. But decision making has 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 assess them with our better (human) judgment. Once managers can separate tasks into components of prediction and judgment, we can begin to understand how to optimize the interface between humans and machines.
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.
After an early round of books with grand and large predictions of what AI can and can’t do, this book is a good, grounded analysis of what we can expect to see in the near future. AI, and considerations of it’s potential have been around for longer than many realise. From the initial excitement of the potential possibilities in the 1950s, to the early progress, followed by the disillusionment of the subsequent AI winter. We have now moved into happier times when the technological capacity has begun to catch up with the big picture ideas. At the same time the authors are very clear to demonstrate that they are exploring the possibilities of local AI, not a wider, general AI.
Large amounts of data and the ability to process them quickly is enabling exciting possibilities in predictive intelligence and with those insights and smart actions. This is the key kernel of this book. This doesn’t make it less exciting or interesting, rather it gives a much more realistic analysis of what we can expect to see now, in the near future, and how this can then offer exciting new possibilities. Industry 4.0, IoT, and blockchain based innovations will all begin to coalesce into overall combined business propositions, allied with the powerful possibilities of predictive AI.
This is a timely book, well written, and accessible putting forward their insights, and is well worth reading. If it means we don’t have general AI just yet, that might just be ok for now. We saw something recently that just felt a little too much like Robocop (when it goes wrong), so we’d be happy with merely helpful prediction machines for the moment.
PREDICTION MACHINES: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans and Avi Goldfarb is published by Harvard Business Review Press on 17 April 2018, 256 pages, £22.00, ISBN: 9781633695672
ABOUT THE AUTHORS
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. 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. Avi 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.