Building Watson: An Overview of the DeepQA Project

Ferrucci, David (IBM T. J. Watson Research Center) | Brown, Eric (IBM T. J. Watson Research Center) | Chu-Carroll, Jennifer (IBM T. J. Watson Research Center) | Fan, James (IBM T. J. Watson Research Center) | Gondek, David (IBM T. J. Watson Research Center) | Kalyanpur, Aditya A. (IBM T. J. Watson Research Center) | Lally, Adam (IBM T. J. Watson Research Center) | Murdock, J. William (IBM T. J. Watson Research Center) | Nyberg, Eric (Carnegie Mellon University) | Prager, John (IBM T. J. Watson Research Center) | Schlaefer, Nico (Carnegie Mellon University) | Welty, Chris (IBM T. J. Watson Research Center)

AI Magazine 

IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV Quiz show, Jeopardy! The extent of the challenge includes fielding a real-time automatic contestant on the show, not merely a laboratory exercise. The Jeopardy! Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After 3 years of intense research and development by a core team of about 20 researches, Watson is performing at human expert-levels in terms of precision, confidence and speed at the Jeopardy! Quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating and advancing a wide range of algorithmic techniques to rapidly advance the field of QA.

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