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Comparison to Deep Blue

Deep Blue illustrated a turning point in the global appreciation for the capability of the computer relative to the human. It demonstrated that computers can solve problems once thought the exclusive domain of human intelligence, albeit in perhaps very different ways than humans do. Deep Blue was an amazing achievement in the application of compute power to an extraordinarily challenging but computationally well-defined and well-bounded game. By searching and evaluating a huge space of possible chess board configurations, Deep Blue has the compute power to beat a grand master.

We are now at a similar juncture. This time it is about how vast quantities of digitally encoded unstructured information (e.g., natural language documents, corporate intranets, reference books, textbooks, technical reports, blogs) can be leveraged by computers to do what was once considered the exclusive domain of human intelligence -- rapidly answer and rationalize open-domain natural language questions confidently, quickly and accurately.

Watson faces a challenge that is entirely open-ended and defies the sort of well-bounded mathematical formulation that fits a game like Chess. Watson has to operate in the near limitless, ambiguous and highly contextual domain of human language and knowledge. Ultimately Watson's scientific goal is to demonstrate how computers can get at the meaning behind a natural language question and infer precise answers from huge volumes of content with justifications that ultimately make sense to humans.

Rather than challenging the human to search a vast mathematical space, Watson challenges the computer to operate in human terms. To understand and answer human questions and to know when it does and doesn't know the answer -- to assess its own knowledge and ability -- something humans find relatively easy.

Like Deep Blue, Watson may not mimic human thought processes to get the job done, but unlike Deep Blue there is ultimately no guarantee for Watson that an answer exists or that it can even be inferred from its sources, no matter how long it may search. Watson's language and knowledge processing infrastructure must therefore combine statistical and heuristic techniques to assess its own knowledge and produce its best answer with an accurate confidence -- a measure of the likelihood it is correct based on a self-assessment of its sources, inference methods, and prior performance.

Jeopardy! challenges us to build a computer to play in the human world of language and meaning rather than in the world of precise mathematical logic. If this challenge is met, Watson may still not answer every question, but it will lead the way to systems that can have much greater social and business impact in the way they interact with and assist humans.

IBM Jeopardy! Challenge