Building Watson: An Overview of the DeepQA Project

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

Abstract

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.

Author Biographies

Eric Nyberg, Carnegie Mellon University
Professor
Language Technologies Institute, School of Computer Science
Nico Schlaefer, Carnegie Mellon University
Language Technologies Institute, School of Computer Science
Published
2010-07-28
Section
Articles