The challenge for Watson, and more generally for natural-language-processing technologies, is to program computers to do a better job at understanding our language. Computers can process huge volumes of content quickly without getting tired. We are not that fast or enduring. The problem for computers is that so much of human knowledge is in natural-language content—from textbooks to blogs. It is not enough to deliver lists of thousands of documents that contain a few keywords. To help us, computers need to give us precise answers with meaningful justifications. They need to communicate in our terms rather than demand that we communicate in theirs. If we could program computers to more precisely interpret natural language the way we do, then we could leverage their speed to give us more precise and better justified responses based on enormous resources of knowledge that are simply out of our reach. This capability can dramatically advance business, society, and science in so many different areas.
The Watson you saw on Jeopardy! was built on decades of research and used a novel architecture and methodology for combining and advancing NLP and other AI techniques to deliver remarkable performance at a natural-language question-answering task. But it just scratched the surface. More work is needed to get computers to where they can deliver precise responses tailored to our information demands from the whole of human knowledge, whether it be in health care, science, education, or finance.
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