Who’s afraid of Watson?

And the answer is, “What is Watson?” Even if you know the correct reference (pick from: Alexander Graham Bell’s assistant, a computer, the founder of IBM), which by far most people on this planet do not, it’s unlikely that fear is attached to it. Watson is not a common synonym for boogeyman. However, a few Watson generations down the line may be a different story.

In February, 2011 half the population of the United States gathered around TV screens, gorged on fat-foods and swilled beer, while watching a sports contest of epic proportions called the Superbowl. Also in February for three consecutive days several million Americans watched a television game show called Jeopardy! It too was promoted as a contest of epic proportions: Man vs. Machine. It featured two men, Ken Jennings and Brad Rutter, considered the best to have ever played the game, who faced (if that’s the word) a computer called Watson, the latest and greatest Artificial Intelligence created by IBM. A team named the Green Bay Packers won the Superbowl. Watson beat the human players.

Most people living outside of North America know nothing about either epic contest. One of these contests has almost zero impact on anything; the other contest symbolizes something that will relatively soon affect the livelihood of people all over the world.

I’m deliberately pushing the irony button. I hope it’s obvious that the Superbowl and Jeopardy events are not equivalent, although given the levels of promotion (hype); they seemed to be approaching comparable importance. (Of course, by the measure of hype, nothing beats the Superbowl…not even the World Cup.)

In 1997 a computer IBM called “Deep Blue” narrowly defeated the reigning champion of chess, Gary Kasparov. It’s quite likely you’ve heard references to the event because it was considered a significant breakthrough for computer intelligence. In reality there was no intelligence involved; just massive calculation of chess positions (called brute force computing) and a set of rules for playing the game. Big Blue was built exclusively for playing chess, in fact, it was built just for the match with Kasparov and was dismantled shortly thereafter. Nevertheless, the victory had big juju, uh, symbolic power for computers.

In some ways the Watson victory in Jeopardy! is similar. More than a few people who watched the show were left with the impression that Watson has some kind of intelligence. It doesn’t, no more than Big Blue did; but Watson is quite a different computer than Big Blue. Where Big Blue calculated millions of chess moves, essentially mathematical calculations, Watson draws on an enormous database of facts (Jeopardy trivia), which it handles much like a specialized search engine. Where Big Blue was programmed with the relatively simple rules of chess, Watson has a much more sophisticated program to evaluate the facts coughed-up by the search engine, determine the most likely answer, and then play by the rules of Jeopardy. Watson also has something Big Blue didn’t have at all – the ability to understand questions in plain language (English, in this case).

It’s this capability, called natural language processing that gets Watson in the game and sets it apart from most other advanced computing. To play Jeopardy it had to understand the questions posed by Alex Trebek, the game’s host. Jeopardy questions are notorious for containing wordplay, ambiguity, wit and double meaning. They’re meant to be tricky. So not only did Watson need to understand plain English (through what’s called a Conversational User Interface), but it had to deal with the language peculiarities of Jeopardy. Of course, the developers of Watson’s programming were able to concentrate on the typical wording, cues, and phrases found in Jeopardy. They did well enough that Watson could correctly interpret most of the questions, but notably it did worst on those questions which were meant to be tricky.

Watson is not perfect and using 2600 processors in 90 IBM servers (not to mention the army of people who set this all up) – it’s way too expensive to be commercially useful for general applications. Nevertheless, even though putting the three elements together – natural language understanding of Jeopardy questions, searching a huge trivia database, and operating within the rules of the game – did not produce general intelligence; what it did is demonstrate for the first time to a mass audience an Artificial Expert.

Expert systems have been around a while, decades in fact. One way or another they capture some specific body of knowledge, nominally something an expert would know, and make it available on demand. With its ability to understand questions and formulate relevant answers in normal language, Watson takes the expert system to another level.

Let’s be clear about this, this Artificial Expert. It can understand questions in normal language. It can look up answers to complex questions in split seconds. It can select the best available information and provide the answer in normal language. It can sound like a human being. What else do you think a machine like Watson can do besides answer Jeopardy questions?

Let’s be specific. Do you take orders over the phone? Are you employed as a receptionist? Are you one of the people who answer questions on a help line? You will probably be out of a job when future generations of Watson become less expensive. How long do think that will take? Three years? Ten years?

Robotics and computer programs are already replacing manual labor and various kinds of information systems (yes, even the terrible phone menus count). As an Artificial Expert, a Watson-like computer will be capable of replacing or augmenting a wide range of jobs in the information industry. Most of these are considered relatively safe white-collar jobs. They’re not that safe, never have been. Most knowledge workers have ‘good enough’ jobs. They don’t really have to be experts; they just need to be good enough to answer more or less routine questions in relatively well defined areas of knowledge. Watson can already do this. Many other computers will be capable within this decade.

Still skeptical? Consider outsourcing – that’s where jobs are transferred to countries where the labor costs are lower. Outsourcing has already eliminated many knowledge worker jobs from some countries. Think of Artificial Experts as the ultimate outsource.

I’m piling on, but it’s necessary: In a modern economy, knowledge workers account for between 30 and 50 percent of the work force. Over the years – and we’re not talking decades – Artificial Experts will replace increasing percentages of this type of worker. The question is: What kind of job will replaced knowledge workers find?

I’m being deliberately blunt because complacency about the future of computer power is not harmless. We need to start considering that future. What happened in the Jeopardy game is not far away from happening to a lot of people’s jobs.

Nor does it help to pull the denialist stunt. You’re going to read and hear from people a lot of “Watson was interesting but technically no big deal.” Technically, they’re right. Substantively, they’ve got it wrong. It’s a bit like the Apple iPad. You can pick that tablet computer apart in all kinds of ways, and make all kinds of disparaging remarks. Much of the criticism is warranted; but it doesn’t matter. The Apple iPad is not only the most successful new computer in a decade or two, but it’s also driving the tablet computer into becoming a standard format for the future. IBM is determined to do something like that with the technologies behind Watson.

Granted, those technologies are in a different league than the Apple iPad. Watson-style computers are a long way from the consumer market. IBM will go first where the big money is available – medical applications, corporate systems and the like. It will take years to improve the price/performance ratio to make the technologies a commercial success. The important thing is that where IBM goes, so will at least a dozen other companies. It does not take a crystal ball to see that Artificial Expert computing is potentially a big, spreading money tree.

“Who’s afraid of Watson?” is not entirely a rhetorical question, but Watson is not Hal. I hope that’s clear. It is not to be feared like the psychotic autonomous thinking machine depicted in Stanley Kubrick’s 2001. Watson does not have intelligence. Many future generations of Watson will not have general intelligence. In practical terms, however, it doesn’t matter. Watson is showing us – right now – just how much even limited computer intelligence will compete with the knowledge of human beings. Maybe we should be just a little more anxious.

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