‘AI’s Value Is in Its Predictions’ Says Economist Joshua Gans

By TAP Staff Blogger

Posted on May 2, 2018


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In their new book, Prediction Machines: The Simple Economics of Artificial Intelligence, Joshua Gans and coauthors Ajay Arawal and Avi Goldfarb offer help to the business world by explaining the implications of artificial intelligence (AI) on businesses and providing strategies for evaluating the trade-offs associated with incorporating AI into business processes and machines.

 

In the authors’ own words, from the first few pages of the Prediction Machines book:

Our first key insight is that the new wave of artificial intelligence does not actually bring us intelligence but instead a critical component of intelligence – prediction.

 

Economics provides a well-established foundation for understanding uncertainty and what it means for decision making. As better prediction reduces uncertainty, we use economics to tell you what AI means for the decisions you make in the course of your business. This, in turn, provides insight into which AI tools are likely to deliver the highest return on investment for the work flows inside your business. This then leads to a framework for designing business strategies, such as how you might rethink the scale and scope of your business to exploit the new economic realities predicated on cheap prediction. Finally, we lay out the major trade-offs associated with AI on jobs, on the concentration of corporate power, on privacy, and on geopolitics.

 

Professors Gans, Arawal, and Goldfarb have written a few articles that provide insights from the book. Below are some key take-aways.

 

A Simple Tool to Start Making Decisions with the Help of AI
Harvard Business Review, April 17, 2018

 

There is no shortage of hot takes regarding the significant impact that artificial intelligence (AI) is going to have on business in the near future. Much less has been written about how, exactly, companies should get started with it. In our research and in our book, we begin by distilling AI down to its very simplest economics, and we offer one approach to taking that first step.

 

We start with a simple insight: Recent developments in AI are about lowering the cost of prediction. AI makes prediction better, faster, and cheaper. Not only can you more easily predict the future (What’s the weather going to be like next week?), but you can also predict the present (what is the English translation of this Spanish website?). Prediction is about using information you have to generate information you don’t have. Anywhere you have lots of information (data) and want to filter, squeeze, or sort it into insights that will facilitate decision making, prediction will help get that done. And now machines can do it.

 

In teaching this subject to MBA graduates at the University of Toronto’s Rotman School of Management, we have introduced a simple decision-making tool: the AI Canvas. Each space on the canvas contains one of the requirements for machine-assisted decision making, beginning with a prediction.


Image: AI Canvas

 

Professors Gans, Arawal, and Goldfarb then use an example from their work with a security firm to explain how the AI Canvas works. Read the full article: A Simple Tool to Start Making Decisions with the Help of AI.

 

Make Sure AI Is Right for Your Business Before Taking the Plunge
The Globe and Mail, April 13, 2018

 

As with many technologies, AI does one thing really well.

 

That one thing is prediction. Prediction is part of intelligence. We predict the day’s weather so as to decide what clothes to wear. We predict a person’s emotional state when we see a facial expression and decide what to say next. And we predict marketing effectiveness when we choose the words that will best persuade a customer to buy a product.

 

AI is often able to nail the prediction problem in ways that humans are not.

 

They [AI] use data to make predictions. We define prediction as using information you have to generate information you don’t have. So, for example, we call classification – such as taking a medical image of a tumour (information we have) and classifying it as malignant or benign (information we don’t have) – a type of prediction.

 

When being sold a bill on AI, you need to ask yourself, “What uncertainty is that AI taking away?” Is it something that is really important to decisions you might or can make? Or is it something that is nice to know but that’s it. A fortune teller does you no good by telling you what will happen next week if there is nothing you can do about it.

 

AI offers the potential for the automation of thought. It forces us to reflect carefully on how people think and the parts of our thought process that machines can actually replace. In situations where prediction is the main challenge, there is great potential for automation. Anybody who has had the experience of teaching a teenager to drive knows that what causes you to shout in terror is not that the teenager does not know to brake when there is a car stopped in front of them, but instead, their inexperience in predicting whether a car in front of them is going to stop. Prediction machines excel at this.

 

Read the full article: Make Sure AI Is Right for Your Business Before Taking the Plunge.

 

Artificial Intelligence in the Boardroom
The Corporate Board, March/April 2018

 

AI-related business topics, as opposed to technical topics, can be grouped into three general categories.

 

This first category is opportunities for operational efficiency created through the deployment of AI tools. This category is the largest in terms of commercial activity, although it requires the least amount of attention from the board.

 

A tidal wave of new AI tools that promise enhanced efficiency take advantage of advances in data collection, processing power, and machine learning algorithms. These tools do things like automate document processing, control robots, and respond to customer service queries. They often enable one person to do the same work that previously required many.

 

The second category for AI concerns risks. Corporate boards must ensure that systems are in place to assure that their organizations are protected from the downside risks associated with deploying AI.

 

The third category concerns industry disruption. This can represent either an opportunity or a risk. Although companies usually employ AI tools to better execute their strategy, sometimes an AI tool can change the economics of an industry so fundamentally that it leads to redefining the strategy itself.

 

Read the full article: Artificial Intelligence in the Boardroom.

 

 

Joshua Gans, Ajay Agrawal, and Avi Goldfarb are economists and professors at the Rotman School of Management, University of Toronto. Each is part of the Creative Destruction Lab: a pre-seed stage start-up program with a heavy emphasis on machine learning.

 


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