September 15, 2000


Austin, TEXAS — James Janega reports that most Americans may be oblivious to artificial intelligence, but soon it will be hard to avoid, according to researcher Bruce Buchanan.

The so-called “AI” community has made stunning – and inaccurate – predictions before. In the 1970s and 1980s, for instance, researchers boasted that by now computer systems would understand spoken language and carry on fluid conversations with humans. Public interest in artificial intelligence flared briefly, but when scientists’ predictions of artificial intelligence failed to materialize, it waned considerably.

Yet that experience led to two realizations in the industry, says Buchanan, a University of Pittsburgh professor and president of the American Association for Artificial Intelligence, which met in August along with researchers from the Innovative Applications of Artificial Intelligence.

The first was that computing power had to be boosted considerably before most artificial intelligence theories could be put into practice. The other was to deliver before talking a big game.

Artificial intelligence now seems poised to do just that.

Coming into its own within the past 10 years, AI research has drawn enormous benefit from parallel booms in networking, wireless communication, and the Internet.

More powerful computers not only make individual AI functions possible, they allow them to be combined. And, increasingly, they are being combined to the benefit of commercial products, such as Internet search engines that recognize patterns in data (an earlier spinoff from AI technology) and then learn which types of search results their users typically look at (an artificial intelligence spinoff now beginning to hit the market).

Perhaps more promising is the fact that researchers have found programming techniques that work in many different artificial intelligence subfields, a development likely to ensure dramatic crossover breakthroughs that will allow artificial intelligence to leap ever more deeply into our everyday lives. Think of refrigerators that make up shopping lists as they grow empty, electronic office assistants that shuffle our schedules — even cars that drive themselves.

But for the time being, AI experts are downplaying their industry’s promise, even as they quietly slip it into our lives.

Hence the AI-powered Tip Wizard in newer versions of Microsoft Word, the script recognition in Apple’s Newton, and the artificial intelligence programming in Tiger Electronics’ Furby dolls.

Do we know that artificial intelligence makes those things possible? Probably not, Buchanan says. And that’s the point.

Just because artificial intelligence has been unobtrusive doesn’t mean the science behind it isn’t remarkable. Over the past decade, the field has enjoyed broad advances on a number of fronts – the kind of success that sparks enthusiastic debates among researchers on the comparative merits of programming through symbolic logic (as in flowcharts) or neural networks (which react quickly to outside stimuli).

Those approaches are, in turn, used to solve problems within artificial intelligence’s subfields. Expert systems run through lists of likelihoods to make assumptions.

Pattern recognition compares probabilities to evaluate large amounts of data, such as photographs or speech. Machine learning devises rules based on observations. Significant understanding has grown in each area – so much, in fact, that AI purists no longer consider them signs of intelligence.

“As soon as something works, it’s no longer considered artificial intelligence,” says Chuck Thorpe, a principal research assistant at Carnegie Mellon University’s Robotics Institute. “As soon as something is understood, it’s spun off and becomes its own field.”

On the other hand, subfields complement each other nicely and can be combined with surprising results. Ron J. Brachman, a research vice president for AT&T Labs, says several groups are working on a help program – an expert system – with speech recognition. That way, you could call a support line, talk instantly with a computer that walks you through your problem, and solve simple issues in minutes.

“That technology is in a sense this close,” Brachman says. “It’s working in the labs, and you can go a surprisingly long way with what an AI purist would think of as shallow-knowledge processing.”

Robots, which perhaps have the most to gain from combining artificial intelligence tools, have been able to get a lot of mileage from the approach.

Cerebus, a bare-bones robot brought to the meetings by Ian Horswill and a team of graduate students from Northwestern University, can respond to basic questions, avoid bumping into people in crowded hallways, and follow someone at a distance when told to do so. Despite obvious rough edges, Cerebus has a lot on his mind, Horswill says. The robot still considers information from surroundings, plans routes in which to travel, and knows enough to respond out loud to typewritten queries. Really, he says, Cerebus is a rough draft of robots to come.

“I think service robots, at least the underlying technology, will be commonplace within about five years,” says Alan Schultz, head of the Naval Research Laboratory’s Intelligent Systems Section.

Future robots probably will find careers in military reconnaissance, hauling office supplies or other equipment, and working in coal mines.

Schultz says, “We talk about the three D’s: dull, dirty, and dangerous.”

Most of us won’t be saying hello to R2-D2 by the office water cooler, but if the computer industry has anything to say about it, we are likely to be talking to our computers soon, says Eric Horvitz, a senior researcher at Microsoft.

A user might talk to the computer “to clarify understanding about a project,” Horvitz explained. “Just as you would with a colleague.”

At Microsoft’s headquarters in Redmond, Wash., Horvitz works with a group of researchers to create useful, easy-to-talk-to office assistants.

One of their inventions tracks personal calendars and plans made by e-mail, pointing out scheduling conflicts as they occur. Another creation prioritizes e-mail messages so “Question from boss” shows up in a list before “Funny joke.”

Adding voice recognition to those applications would improve them greatly, Horvitz says, and the ultimate goal is to get computers to converse like people, figuring things out by asking questions until understanding is gained. Another step will be getting systems to function in the uncertainty of changing situations.

From talking to other researchers, one gets the idea such innovations are no longer impossible dreams. Buchanan says it would not be complicated to install sensors in a house that allow a home computer to figure out what room you are in. It could then turn off lights in empty rooms to save electricity, for instance, though Buchanan says that would by no means be the limit of how AI-empowered electronics could be useful in the home.

With a little extra artificial intelligence, he says, the dishwasher could decide how dirty the dishes are and therefore decide how long to run the scrub cycle. The refrigerator could communicate with the date book on your personal computer and warn that you’ll need to buy more hot dogs before the barbecue you scheduled for Saturday. Or it could communicate with your car and start defrosting the chicken you planned for dinner as you are leaving the office.

If it sounds as though a big element in the presumptive Artificial Intelligence Revolution involves communication between devices, researchers generally agree that’s correct. But as computer power increases and computer size shrinks, it also seems likely that AI programming will grow yet more complex, in turn allowing it to become still more adaptive and less noticeable.

But even so, building better machines – even seamlessly interconnected machines – is only half of the AI community’s goal.

“There’s sort of two Holy Grails,” Thorpe says. “The engineering Holy Grail is to make things natural and unobtrusive; the scientific Holy Grail is to understand how people think.”


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