After all these years, artificial brains are still hard to come by
James Gaskin: “If you define artificial intelligence as self-aware, self-learning, mobile systems, then artificial intelligence has been a huge disappointment. On the other hand, every time you search the Web, get a movie recommendation from NetFlix, or speak to a telephone voice recognition system, tools developed chasing the great promise of intelligent machines do the work.”
Stanford University computer science professor John McCarthy coined the phrase in 1956 to mean “the science and engineering of making intelligent machines,” In the early years of the artificial intelligence movement, enthusiasm ran high and artificial intelligence pioneers made some bold predictions.
In 1965, artificial intelligence innovator Herbert Simon said that “machines will be capable, within 20 years, of doing any work a man can do.”
Two years later, MIT researcher Marvin Minsky predicted, “Within a generation … the problem of creating ‘artificial intelligence’ will substantially be solved.”
Popular culture jumped onto the artificial intelligence bandwagon and gave us Rosie the Robot from the Jetsons, HAL from the movie 2001 and R2D2 from Star Wars.
Yet, here we are, decades later and what has artificial intelligence done for us lately? If you define artificial intelligence as self-aware, self-learning, mobile systems, then artificial intelligence has been a huge disappointment.
On the other hand, every time you search the Web, get a movie recommendation from NetFlix, or speak to a telephone voice recognition system, tools developed chasing the great promise of intelligent machines do the work. In other words, we may not have full-functioning robots that cater to our every need, but artificial intelligence is embedded in our everyday lives.
“Once tools get far enough out of the lab, they’re no longer AI, just common computer science,” says Professor George Luger of the University of New Mexico. “AI just went to work.”
One of the biggest boosts to artificial intelligence is Moore’s Law, because artificial intelligence needs CPU power. “It took 20 years to go from a 5MHz chip to a 500MHz chip, but only eight months after that to get to a 1GHz chip,” says futurist Daniel Burrus, author of the best seller Technotrends: How to Use Technology to Go Beyond Your Competition and founder of Burrus Research.
“The new Sony Playstation came out a year ago,” says Burrus, “but if it came out five years earlier it would be considered a supercomputer.” Burrus likens the growth of processing power on a graph to a hockey stick. “In the 90s, the graph was still low. In 2000, the graph started up a little. In 2008, we’re getting on the handle of the hockey stick.”
Burrus listed off multiple uses of artificial intelligence and expert systems that work behind the scenes. “The first application of successful AI was in the financial services industry for loan qualifications. Loan qualification went from one to two weeks down to minutes.” Other examples include systems that help Navy pilots land jets on aircraft carriers.
His personal favorite is the use of an expert system to manage room service orders at Marriott hotels. “AI tells them when to start cooking and when to deliver. Marriott tells me exactly when breakfast will be delivered while others give me a 15 minute window. That’s a competitive advantage for Marriott.”
While energy prices soar, Burrus noted the cost of intelligence keeps going down. “Maybe we can offset the energy trend as we make appliances more intelligent.”
Access to tools
Part of offsetting that trend will be better software tools, the type favored by Luger in his book, Artificial Intelligence: Structures and Strategies for Complex Problem Solving (Sixth Edition). “Modern languages have roots in AI research, including object oriented design, C++, C#, and Java,” Luger says. “The coolest stuff we’ve done is build a set of exciting tools.”
Yet tools and embedded intelligent systems don’t answer the “grand challenges” of artificial intelligence, including robots and language processing. Very few projects have captured the public’s imagination.
NASA got great public response with their Mars rovers, but little was made of the artificial intelligence components. Artificial intelligence techniques considered pure research 15 years earlier guided rovers Spirit and Opportunity around rocks a world away.
Defense Advanced Research Projects Agency (DARPA) provides money for “grand challenges” including Internet development in their earlier incarnation of ARPA. Now it sponsors a contest to build autonomous vehicles (see Urban Challenge). This forces teams to integrate separate discipline areas such as machine vision, learning systems and problem solving while moving through unfamiliar areas.
One of the most successful artificial intelligence products is literally underfoot. Roomba, the home vacuuming product from iRobot, has sold over 2 million units. One survey showed over half of the deployed Roombas have been given pet names by their owners.
Colin Angle, CEO and co-founder of iRobot, says, “When we started shipping Roomba in 2002, we asked focus groups if it was a robot. They said no, a robot was humanoid and this was an intelligent floor vacuum. Now people are definitely changing to accept robot appliances.”
Hollywood again set the bar high. “Since the Jetsons in 1962, they created expectations we failed to meet for over 40 years. Big AI projects have largely gone by the wayside, but you can see effective behavior that solves real world problems,” Angle says.
As you might expect from someone making work tools for the real world, Angle takes a practical look at artificial intelligence and robotics. “In general, software is algorithms and code that can be reused across platforms. The more low-level tasks used to handle different situations, such as obstruction avoidance, the more successful. We call it bottom-heavy cognition,” he says.
See me, feel me, touch me
Seeing and avoiding obstacles remains tough. “Years ago, researchers had the idea that machine vision was a straightforward problem, and was given to a graduate student for a summer project. Turns out things are radically harder than what people in the field though,” Angle says.
Many remember Phillipe Kahn from his high profile days running Borland, but now he’s CEO of Fullpower Technologies. The company provides an operating environment for sensors in camera phones and consumer electronic devices.
“What we do is all about sensors. Imaging sensors, proximity sensors, and touch sensors are all part of what needs to be put to work. Sensors produce piles of organized data. Great software turns that raw data into actionable information. Fullpower is working on such solutions,” Kahn says.
Micro-controllers often only have 8KB of RAM, so Fullpower writes in C and Assembler. “In the real world of next-generation intelligent devices, small, lean and frugal rule,” Kahn says. “I predict that most of the successful and useful advances will come from sensor-enabled devices and networks of such sensor-enabled devices.”
The language barrier
If machine vision remains a barrier for robot movement and navigation through the environment, the language barrier still looms large but is shrinking. Workable systems are appearing, particularly when a voice-recognition system can be trained or remains limited to certain vocabulary word groupings.
Larry Harris founded Artificial Intelligence Corporation in 1975, then founded Linguistic Technology Corporation in 1994, which became EasyAsk Software. Now vice president and general manager for the EasyAsk division of Progress Software, Harris continues to help machines solve language problems.
“We translate over 60,000 natural language questions per month into queries,” Harris says. When people type more than two or three words into an e-commerce search field, the system has to understand enough to search the product database accurately.
“The base work for Ask Jeeves was at the AI Lab at MIT,” Harris says. “They were at the top until Google came out.” Google uses artificial intelligence techniques for word stemming (getting the word down to the root), language analysis, and applying the results to the index.
As an example of artificial intelligence tools becoming commonplace programming modules, Harris listed word stemmers. “You can now buy them off the shelf and plug them in. And you choose stemming rules for the language you need, since the rules for German are different than French and English.”
Harris warns there are no silver bullets in artificial intelligence, just incremental advances. “People don’t want to claim their product is AI,” Harris says. “They just focus on the voice recognition angle. There’s no real advantage to calling it AI, and even some baggage. Once you have a high proficiency example, you don’t mention AI.”
University of New Mexico’s Luger says “language processing is a big area. We’re working with a small company to answer questions in the context of a knowledge base that knows the area of inquiry.” Asking machine language processing to understand all words and speech idioms still leads to failure, but building in a knowledge base of a topic area works.
“Go to the Next I.T. Web site and check Ask Jenn from Alaska Airlines and Ask SGT STAR from the U.S. Army, two natural-language bots we put together,” Luger says. “We want to give the same answers to the same questions, which you don’t always get with people.”
Research yields results
Eric Horvitz, manager of the Adaptive Systems group at Microsoft, says “about a quarter of all Microsoft research is focused on AI efforts.” Microsoft Research includes close to 1,000 Ph.D level researchers spread across eight campuses around the world, and a completely open research and publication environment. “It’s a think tank, but not a captive one. We have an open publication model.”
“Microsoft Research’s No. 1 goal is to push the state of the art forward without regard to Microsoft,” Horvitz says. “Researchers do their best work, publish in journals, and then work with product teams to build the best software or service.” One project that started in Microsoft Research became the new SYNC voice recognition technology used by audio systems in Ford vehicles.
Horvitz and fellow researchers also have the ability to turn thousands of Microsoft employees into guinea pigs. The kernel of the Vista operating system includes machine learning to predict, by user, the next application that will be opened, based on past use and the time of the day and week. “We looked at over 200 million application launches within the company,” Horvitz says. “Vista fetches the two or three most likely applications into memory, and the probability accuracy is around 85 to 90%.”
Desktop application traffic is one thing, but city traffic prediction is another. ClearFlow, a project born of the frustration of sitting in Seattle traffic, examined thousands of routes for people based on the inference of local street traffic flow reacting to highway accidents. Realizing side streets become clogged when drivers seek to escape highway congestion, Microsoft’s maps.live.com site includes side street congestion history in rerouting suggestions. Microsoft rolled out this free service for 72 cities in early April.
The excessive hype over artificial intelligence promises in the 1950s, 1960, 1970s, 1980s, and 1990s have made the public weary of unfulfilled promises. While almost every consumer electronic device includes some artificial intelligence tools inside, the box labels never include artificial intelligence in the parts list.
Artificial intelligence is not only still around, but in more places than ever. Rather than calling the tools artificial intelligence, manufacturers just call technologies developed by artificial intelligence research “tools.” Just remember that the next time you perform a Web search, write an address on an envelope the Post Office sorts automatically, or ask Microsoft Word for a grammar check, artificial intelligence does the heavy lifting.