Newspaper article International New York Times

After Slow Start, Artificial Intelligence Is Beginning to Move ; Progress of Technology Unfolds in Small Steps, Industry Veterans Say

Newspaper article International New York Times

After Slow Start, Artificial Intelligence Is Beginning to Move ; Progress of Technology Unfolds in Small Steps, Industry Veterans Say

Article excerpt

Silicon Valley veterans argue that people routinely fail to estimate accurately the timing of new technology. A.I., they add, is no exception.

When IBM's Watson computer triumphed over human champions in the quiz show "Jeopardy!" it was a stunning achievement that suggested limitless horizons for artificial intelligence.

Soon after, IBM's leaders moved to convert Watson from a celebrated science project into a moneymaking business, starting with health care.

Yet the next few years after its game-show win proved humbling for Watson. Today, IBM executives candidly admit that medicine proved far more difficult than they anticipated. Costs and frustration mounted on Watson's early projects. They were scaled back, refocused and occasionally shelved.

IBM's early struggles with Watson point to the sobering fact that commercializing new technology, however promising, typically comes in short steps rather than giant leaps.

Despite IBM's own challenges, Watson's TV victory -- five years ago this month -- has helped fuel interest in A.I. from the public and the rest of the tech industry. Venture capital investors have poured money into A.I. start-ups, and large corporations like Google, Facebook, Microsoft and Apple have been buying fledgling A.I. companies. That investment reached $8.5 billion last year, more than three and a half times the level in 2010, according to Quid, a data analysis firm.

And software engineers with A.I. skills are treated like star athletes, with bidding wars for their services.

"We're definitely at a peak of excitement now," said Jerry Kaplan, a computer scientist, entrepreneur and author, who was a co- founder of a long-forgotten A.I. start-up in the 1980s. "Expectations are way ahead of reality."

The term A.I. has long been a staple of science fiction -- as machines that think for themselves and help humankind or as ungrateful creations that try to wipe us out. Or so the thinking at the movies goes.

The reality, however, is a little less dramatic. The automated voice on your smartphone that tries to answer your questions? That's a type of A.I. So are features of Google's search engine. The technology is also being applied to complex business problems like finding trends in cancer research.

The field of artificial intelligence goes back to the beginning of the computer age, and it has rolled through cycles of optimism and disillusion ever since, encouraged by a few movie robots and one very successful game show contestant.

The history of tech tells A.I. backers to hang in there. Silicon Valley veterans argue that people routinely overestimate what can be done with new technology in three years, yet underestimate what can be done in 10 years.

Predictions made in the '90s about how the new World Wide Web would shake the foundations of the media, advertising and retailing industries did prove to be true, for example. But it happened a decade later, years after the dot-com bust.

Today's A.I., even optimists say, is early in that cycle.

"I think future generations are going to look back on the A.I. revolution and compare its impact to the steam engine or electricity," said Erik Brynjolfsson, director of the Initiative on the Digital Economy at Massachusetts Institute of Technology's Sloan School of Management. "But, of course, it is going to take decades for this technology to really come to fruition."

There are reasons for enthusiasm. Computers continue to get cheaper even as they get more powerful, making it easier than ever to crunch vast amounts of data in an instant. Also, sensors, smartphones and other tech devices are all over the place, feeding more and more information into computers that are learning more and more about us.

Just in the last year or two, researchers have made rapid gains using a machine-learning technique called deep learning to improve the performance of software that recognizes images, translates languages and understands speech. …

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