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."