IBM has been explaining the technology that makes its Watson platform the equal of some human minds at answering questions.
Watson made headlines earlier in the year after beating human contestants in the game show Jeopardy.
IBM has a long history of pitching computers against humans, starting with the Big Blue system that beat grandmasters at chess, but has faced bigger challenges when it comes to understanding language.
"With chess there are only so many moves you can make, but language presented new challenges," Aditya Kalanpu, a research staff member at IBM, told V3.co.uk at the SemTech 2001 conference.
"This is very hard for a machine to do, but we've taken a big step with Watson. We're a long way from full language understanding, but this is a major move forwards."
The Watson team spent four years designing a system capable of understanding language, syntax and simile on a human level. Doing so meant going back to the drawing board from the hardware and software design perspectives.
The entire system was rebuilt around massive parallel processing systems, and the Jeopardy-winning configuration used 2,800 processing cores to generate answers in seconds.
Using a standard PC, the Watson software would take about two hours to answer a question, Kalanpu said.
The software can break down questions into component parts and search for possible answers, rating each one against the evidence for or against the proposition. Answers are also ranked based on a variety of data metrics.
What differentiates IBM's software is the level of data it is able to mine. Standard question and answer software is capable of matching questions against structured databases, but Watson uses a system dubbed DeepQA to examine structured and unstructured data for results.
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