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 researcherss, 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. The architecture and methodology developed as part of this project has highlighted the need to take a systems-level approach to research in QA, and we believe this applies to research in the broader field of AI. We have developed many different algorithms for addressing different kinds of problems in QA and plan to publish many of them in more detail in the future.

KC Sealock had not taken a standardized test since college. But here he was at 39 years old, long black beard flecked with grey, sitting in front of a computer at Jacksonville, Florida's Herlong Air Field, with a proctor peering on from behind a glass door. He spent two hours clicking at multiple choice questions about latitudes and longitudes, Class C airspace regulations, wing load factors, and more--60 in all. Finally, Sealock hovered his mouse hovered over the submit button. "I didn't know if I wanted to click," he says.