Avi Goldfarb
Rotman Chair in AI and Healthcare, Professor of Marketing and Chief Data Scientist, Creative Destruction Lab, Rotman School of Management; Co-author, Prediction Machines: The Simple Economics of Artificial Intelligence
about artificial intelligence today — THE REASON WE HEAR SO MUCH in healthcare and elsewhere — is that a very particular technology has gotten much, much better: prediction technology. When I say prediction, I don’t necessarily mean predictions about the future. I’m talking about using information that you have to fill in information that you don’t have. And every organization can benefit from that.
Recent advances in AI can be seen as better, faster, cheaper prediction. To understand why cheap prediction may be transformative, consider an earlier technology. Think about your computer for a moment. It might seem like it does all sorts of things, but it really only does one thing, and it does that thing so well that over the years we have found all sorts of uses for it. What your computer does is arithmetic. That’s it. And because arithmetic has become so cheap and so instant, we have found endless applications for it that we might not otherwise have thought of.
The first applications for machine arithmetic were good old-fashioned arithmetic problems. In World War II we had cannons that shot cannon balls, and it was a very difficult arithmetic problem to figure out exactly where those cannonballs were going to land. So, we had teams of humans figuring out the trajectory. The movie Hidden Figures was all about these teams of humans with the job title Computer. But before long, machine arithmetic came along that was better, faster and cheaper than the humans. Over the years, arithmetic would continue to get cheaper and cheaper, and we would find all sorts of new applications for it. For instance, it turned out that games, mail and photographs were arithmetic problems.
This is Economics 101. On day one in Economics 101, the first thing we teach is that when the price of something falls, we buy more of it. Once you know what has gotten cheaper and what has gotten better, you can map out all sorts of consequences. So when arithmetic got cheaper, we used more arithmetic. With recent advances in artificial intelligence, as the price of prediction falls, we are going to do more and more prediction. Cheap prediction means we can use prediction in new ways, such as medical diagnosis through image recognition.
The consequences of cheap prediction don’t stop there. On day two of Econ-101, you learn that when the price of coffee falls, we buy less tea. Tea and coffee are substitutes, so when coffee gets cheaper, people buy more coffee and less tea. Likewise, human prediction and machine prediction are substitutes: When machine prediction gets cheap, the prediction aspects of your job will increasingly be done by a machine.
So what is left for humans? Thankfully, on day two of Econ101 you also learn that when the price of coffee falls, we buy more cream and sugar — the ‘complements’ to coffee. Complements become more valuable as something gets cheaper. The question for AI is, what are the cream and sugar to prediction? The answer: The other aspects of decision-making, most notably the judgment to know which predictions to make and what to do with those predictions once we have them.
If you map out the workflow for any organization, you will find that it contains a series of decisions. Once you figure out which of those decisions involve predictions at their core, you can drop in a prediction machine to handle the decision. This will incrementally improve productivity and help the organization. But importantly, sometimes prediction can do much more than this. Better prediction can change strategy; it might even change what your organization does and lead to entirely new opportunities.