Watson and the Barometer

An op-ed I wrote that runs in today's Wall Street Journal.

In the weeks since IBM’s computer, Watson, thrashed two flesh-and-blood champions in the quiz show “Jeopardy!,” human intelligence has been punching back—at least on blogs and opinion pages. Watson doesn’t “know” anything, experts say. It doesn’t laugh at jokes, cannot carry on a conversation, has no sense of self, and commits bloopers no human would consider. ( Toronto , a U.S. city?) What’s more, it’s horribly inefficient, requiring a roomful of computers just to match what we carry between our ears. And it probably would not have won without its inhuman speed on the buzzer.

This is all enough to make you feel reinvigorated to be human. But focusing on Watson’s shortcomings misses the point. It risks distracting people from the transformation that Watson all but announced on its “Jeopardy!” debut: These question-answering machines will soon be working alongside us in offices and laboratories, and forcing us to make adjustments in what we learn and how we think. Watson is an early sighting of a highly disruptive force.

The key is to regard these computers not as human wannabes but rather as powerful tools, ones that can handle jobs currently held by people. The “intelligence” of the tools matters little. What counts is the information they deliver. In our history of making tools, we have long adjusted to the disruptions they cause. Imagine an Italian town in the 17th century. Perhaps there’s one man who has a special sense for the weather. Let’s call him Luigi. Using his magnificent brain, he picks up on signals—changes in the wind, certain odors, perhaps the flight paths of birds or noises coming from the barn. And he spreads word through the town that rain will be coming in two days, or that a cold front might freeze the crops. Luigi is a valuable member of society.

Along comes a traveling vendor who carries a new instrument invented in 1643 by Evangelista Torricelli. It’s a barometer, and it predicts the weather about as well as Luigi. It’s certainly not as smart ~~as him~~, if it can be called smart at all. It has no sense of self, is deaf to the animals in the barn, blind to the flight patterns of birds. Yet it comes up with valuable information.

In a world with barometers, Luigi and similar weather savants must find other work for their fabulous minds. Perhaps using the new tool, they can deepen their analysis of weather patterns, keep careful records and then draw conclusions about optimal farming techniques. They might become consultants. Maybe some of them drop out of the weather business altogether. The new tool creates both displacement and economic opportunity. It forces people to reconsider how they use their heads.

The same is true of Watson and the coming generation of question-answering machines. We can carry on interesting discussions about how “smart” they are or aren’t. But it’s academic. They make sense of complex questions in English and fetch answers, scoring each one for the machines’ level of confidence in it. When asked if Watson can “think,” David Ferrucci, IBM’s chief scientist on the “Jeopardy!” team, responds: “Can a submarine swim?”

As these computers make their way into law offices, pharmaceutical labs and hospitals, people who currently make a living by answering questions must adjust. They’ll have to add value in ways that machines cannot. This raises questions not just for individuals but for entire societies. How do we educate students for a labor market in which machines answer a growing percentage of the questions? How do we create curricula for uniquely human skills, such as generating original ideas, cracking jokes, carrying on meaningful dialogue? How can such lessons be scored and standardized?

These are the challenges before us. They’re similar, in a sense, to what we’ve been facing with globalization. Again we will find ourselves grappling with a new colleague and competitor. This time around, it’s a machine. We should scrutinize that tool, focusing on the questions it fails to answer. Its struggles represent a road map for our own cognitive migration. We must go where computers like Watson cannot.