Originally published February 7 2005
Teaching computers to read is the next grand goal for AI research
by Mike Adams, the Health Ranger, NaturalNews Editor
It is a skill that a bright four-year old can acquire without excessive difficulty, but it is almost impossible for machines: reading a sentence or paragraph and extracting its information content. Researchers funded by the Defense Advanced Research Project Agency are trying to build a computerized machine that can learn by reading. Although artificial intelligence has made strides in recent years, reading remains a very difficult hurdle because of the complex ambiguity of natural language.
Among the handiest villains in science fiction are computers that know too much.
Narrowing that cognitive gap between humans and machines -- creating a computer that can read and learn at a sophisticated level -- is a big goal of artificial intelligence researchers.
The Pentagon's Defense Advanced Research Project Agency, or DARPA, granted a contract worth at least $400,000 last fall to two Rensselaer Polytechnic Institute professors who are trying to build a machine that can learn by reading.
Down the road, professor Selmer Bringsjord believes such artificial intelligence, or A.I., machines might be able to read military plans or manuals and adjust on the fly in the heat of battle.
Bringsjord and fellow RPI professor Konstantine Arkoudas want to create algorithms, or mathematical formulas, that allow their Poised-for-Learning machine to convert sentences into formal logic.
The next step would be to create an additional set of algorithms that would allow the machine to use the information it takes in to figure things out -- to reason, in other words.
For example, if the machine reads up on the planets, it should be poised to answer the question "What is the largest planet?"
Bringsjord envisions AI robots of the future taking in information in real time, by either reading or listening to spoken instructions.
Machines that understand spoken words, recognize faces and make inferences based on experience already exist, says Carnegie Mellon University computer science professor Tom Mitchell.
But Mitchell, past president of the American Association of Artificial Intelligence, offers a big caveat: Even though researchers have made a lot of progress in different areas of cognition, there is still a big mystery about how the pieces go together.
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