On the surface, Watson, IBM's "Jeopardy"-playing computer system, is impressive in its ability to answer complex, ambiguous questions expressed in natural language. In fact, Watson will likely be a centerpiece for IBM's centennial celebration in June. But Watson is more than just an impressive computing system. In fact, in the future, after Watson's technology is used and deployed in multiple ways, we expect this week's Jeopardy matches will be seen as a historic event in information technology and big data and analytics that also portends much for society in general.
A "natural" language is any language spoken or written by humans, such as English, rather than a computer language, but there are areas where the two intersect. For example, a user performing a natural language search would enter a question in plain English rather than depending on keywords, such as those that trigger a Google search. Ideally, a true natural language query should return a single, very precise answer with a high degree of confidence rather than the many, many results a search engine delivers, leaving the user to determine what is relevant.
No disrespect to Google (whose original creative breakthrough has proven to be very important and valuable) or to other search engines like Microsoft's Bing, but solving the natural language problem is difficult. The problem is not just in syntactic analysis (computers have been able to parse sentences for some time), but rather in dealing with the subtleties, nuances and ambiguities common in natural language. Faux natural language attempts have failed; the best known is probably Ask Jeeves, which morphed into Ask.com and now is simply a front-end to a conventional search engine. It was a minor league attempt to a major league problem.
Some have suggested that artificial intelligence (AI) would offer a natural answer to the natural language question, and it has had its fits and starts over the years. But, even though AI can now point to a number of successes (primarily in well-defined domains), it has only provided partial solutions to the natural language problem.
IBM decided to take on natural language search as a Grand Challenge--an IBM R&D project that is technologically important and difficult, but whose success is easy for the average person to understand and meaningful for business and society. A past IBM Grand Challenge was the company's Deep Blue, a supercomputing system that defeated the human world chess champion Gary Kasparov in 1997.David Hill is principal of Mesabi Group LLC, which focuses on helping organizations make complex IT infrastructure decisions simpler and easier to understand. He is the author of the book "Data Protection: Governance, Risk Management, and Compliance." View Full Bio