Last week a chief marketing officer (“CMO”) mentioned to me that he admired the CEO at a Toronto artificial intelligence company, but could not understand, what, exactly, his company did. And therein lies the black box paradox of artificial intelligence (“AI”) – everyone’s talking about it, but nobody knows exactly what it is. As the authors of the excellent book, Prediction Machines, say:

“Predictions generated by deep learning and many other AI technologies appear to be created by a black box. It isn’t feasible to look at the algorithm or formula underlying the prediction and identify what causes what.” – Prediction Machines, Agrawal, Gans, and Goldfarb

Like other game-changing, general purpose technologies (“GPTs”) like the steam engine, printing press, and internet, many of the applications for artificial intelligence are unknown. This creates a paralyzing uncertainty, which tends to “freeze” adoption by many companies. It also creates a raft of consultants offering to “help” you figure it out for hundreds of thousands of dollars. I remember similarly hiring an email marketing firm in the early 2000s at Dell, to help us send out mass emails, increase conversion rates, and build our email list/database. Today, anyone can do that reasonably well for $20 a month with a Mailchimp account. I predict that machine learning (the essence of AI) will be a core part of the 10th grade curriculum 15 years from now. Not kidding.

So what does AI do? Simply put, it makes predictions. Prediction takes data you have and uses it to generate insights you don’t have. In marketing, my field, the key predictions seem obvious. They will increase user traffic, conversion, or revenue per unit, or they will decrease customer churn (customers who leave or stop buying your product). Here are some examples:

A smart marketer could probably tell you her best 3-4 promotions over time, based on an Excel spreadsheet analysis from some financial database. So that would take care of the premium positions on the front and back of the 8-page flyer. But what should she put in the middle 4 pages? A marketer could also probably tell you that a group of prospects which visited the product overview page for a laptop computer 4 times in the past two days, and put the laptop in an online shopping cart but then abandoned it when shipping was added to the price, was most likely to respond to an offer for free shipping. But which other prospects might respond? One of the key differences between machine intelligence and human intelligence is that human logic works well at either end of the bell curve – in this case, customers with 2-3 characteristics that indicate they are either highly likely to buy or highly likely to churn. Machine learning deals better with the not so obvious –  the “mushy middle” of the bell curve.

What key characteristics of machine learning make this true?

“Analysts built their regression models on hypotheses of what they believed mattered and how – beliefs unnecessary for machine learning. Machine learning models are particularly good at determining which of many possible variables will work best and recognizing that some things don’t matter and others, perhaps surprisingly, do.” – Prediction Machines, Agrawal, Gans, and Goldfarb

Of course, smart humans make predictions, too. Machines can simply do it faster, more cheaply, and using far more variables than humans can. As predictions become cheap, the value of judgment (the skill used to determine the payoff or profit from a given course of action) goes up. In addition, decisions still have to be made and actions taken. Decision-making requires applying judgment to a prediction and then acting. Human judgment and the ability to decide, to act, in conditions of probability and uncertainty, is still greatly valued.

In the coming brave new world, smart machines will not replace humans, as many fear. However, there will likely be a new division of labour, with machines providing recommendations and humans making the final decision. And there will be problems we cannot anticipate and advances we cannot yet imagine that will change the way we live and learn forever (just try explaining to your university/college student how you studied in “the time before the internet” if you don’t believe me). We’ve been blessed to see the world-changing impact of the internet, and we are fortunate to be in the early days of another general purpose technology, artificial intelligence. And we may yet live to see a third.

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