While AI systems can now learn a game and beat champions within hours, they are hard to apply to business applications.
When you look at well-known applications of AI, such as Google’s AlphaGo Zero, you get the impression it is like magic: AI learned the world’s most difficult board game in just three days and beat champions; Nvidia’s AI can generate photorealistic images of people who look like celebrities just by looking at pictures of real ones.
AlphaGo and Nvidia used a technology called generative adversarial networks, which pits two AI systems against each another to allow them to learn from each other. The trick was that before the networks battled, they received a lot of coaching. And, more important, their problems and outcomes were well defined.
But most business problems can’t be turned into a game; you have more than two players and there aren’t clear rules. The outcomes of business decisions are rarely clear wins or losses, and there are far too many variables. It is a lot more difficult for businesses to implement AI than it seems.
Today’s AI systems do their best to emulate the functioning of the human brain’s neural networks but do this in a very limited way. They use a technique called deep learning, which adjusts the relationships of computer instructions designed to behave like neurons. To put it simply, you tell an AI exactly what you want it to learn and provide it with clearly labeled examples, and it analyzes the patterns in that data and stores them for future application. The accuracy of its patterns depends on data, so the more examples you give it, the more useful it becomes.
Herein lies a problem. An AI is only as good as the data it receives and is able to interpret it only within the narrow confines of the supplied context. It doesn’t “understand” what it has analyzed, so it is unable to apply its analysis to scenarios in other contexts. And it can’t distinguish causation from correlation. AI is more like an Excel spreadsheet on steroids than like a thinker.
The bigger difficulty in working with this form of AI is that what it has learned remains a mystery: a set of indefinable responses to data. Once a neural network is trained, not even its designer knows exactly how it is doing what it does. As New York University professor Gary Marcus explains, deep learning systems have millions or even billions of parameters, identifiable to their developers only in terms of their geography within a complex neural network. They are a “black box,” researchers say.
Speaking about the new developments in AlphaGo, Google/DeepMind’s chief executive Demis Hassabis reportedly said, “It doesn’t play like a human, and it doesn’t play like a program. It plays in a third, almost alien, way.”
Businesses can’t afford to have their systems making alien decisions. They face regulatory requirements and reputational concerns and must be able to understand, explain and demonstrate the logic behind each decision they make.
For AI to be more valuable, it needs to be able to look at the big picture and include many more sources of information than the computer systems it is replacing.
In inventory management, for example, purchasing decisions are usually made by experienced individuals called buyers, department by department. Their systems show them inventory levels by store, and they use their experience and instincts to place orders. An AI could consolidate data from all departments to see the larger trends — and relate them to socioeconomic data, customer-service inquiries, satellite images of competitors’ parking lots, predictions from the Weather Company, and other factors. Many retailers, including Amazon.com, are doing some of these things. (Amazon founder and chief executive Jeffrey P. Bezos owns The Washington Post.)
AI is advancing rapidly and will surely make it easier to clean up and integrate data. But business leaders will still need to understand what it really does and create a vision for its use — that is when they will see the big benefits.