Prediction machines : the simple economics of artificial intelligence

Prediction machines The simple economics of artificial intelligence

Ajay Agrawal

Book - 2018

The idea of artificial intelligence--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. And once you start talking about costs, you can use some well-established economics to cut through the hype. The constant challenge for all managers is to make decisions under uncertainty. And AI contributes by making knowing what's coming in the future cheaper and more certain. But decision making has another component: judgment, which is fi...rmly 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. 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.--

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Subjects
Artificial intelligence > Economic aspects.
Decision making > Statistical methods.
Forecasting > Statistical methods.
Published
Boston, Massachusetts : Harvard Business Review Press [2018]
Language
English
Main Author
Ajay Agrawal (author)
Other Authors
Joshua Gans, 1968- (author), Avi Goldfarb
Physical Description
x, 250 pages : illustrations ; 25 cm
Bibliography
Includes bibliographical references and index.
ISBN
9781633695672
  • Acknowledgments
  • 1. Introduction: Machine Intelligence
  • 2. Cheap Changes Everything
  • Part 1. Prediction
  • 3. Prediction Machine Magic
  • 4. Why It's Called Intelligence
  • 5. Data Is the New Oil
  • 6. The New Division of Labor
  • Part 2. Decision Making
  • 7. Unpacking Decisions
  • 8. The Value of Judgment
  • 9. Predicting Judgment
  • 10. Taming Complexity
  • 11. Fully Automated Decision Making
  • Part 3. Tools
  • 12. Deconstructing Work Flows
  • 13. Decomposing Decisions
  • 14. Job Redesign
  • Part 4. Strategy
  • 15. Al in the C-Suite
  • 16. When AI Transforms Your Business
  • 17. Your Learning Strategy
  • 18. Managing Al Risk
  • Part 5. Society
  • 19. Beyond Business
  • Notes
  • Index
  • About the Authors
Review by New York Times Review

Compared with the amount of ink spilled over the prospects of artificial general intelligence and all its accompanying fears - the singularity! - there's been much less attention to the smaller changes already happening in the realm of AT. and their quite profound economic implications. Enter "Prediction Machines," which looks at just how far "narrow AT." has come over the past few years. Computers are already good at performing a single task for which they have been trained, making them more efficient and cost-effective than humans in many cases. Of course, decision-making involves more than just being able to make accurate predictions, but AT. is also being drafted for higher-level functions including using predictions to weigh outcomes and pass judgment. For all their gains, though, computers are significantly better under certain conditions - say, when they have a lot of past data - and decidedly weaker in others, like predicting "unknown unknowns." "Prediction Machines" does a good job of showing where computers work best and where humans still have an edge. The authors argue, though, that we shouldn't see this as an either/or fight to the death. In many cases, the best answer is to combine the powerful pattern recognition of a computer with the insight of a trained human. Take one example they offer, from the field of medicine. A well-trained algorithm was able to find a certain type of breast cancer with 92.5 percent accuracy. Human pathologists were able to do so at 96.6 percent. Stop there and you would say that computers are getting quite good, but not quite as good as highly skilled humans, at least at this task. But one need not stop there, and thankfully the researchers didn't. Combining the work of computers and humans resulted in 99.5 percent accuracy. In part, that's because humans and computers made different kinds of mistakes, ft's certainly a happier outcome to imagine that we and the machines could work together. ina fried is chief technology correspondent for Axios and writes its daily technology newsletter, Login.

Copyright (c) The New York Times Company [July 15, 2018]

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