IBM's Watson Jeopardy! System

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An IBM's Watson Jeopardy! System was a IBM Watson System that competed in and won a Jeopardy! Contest in February 2010.



  • Jo Best. (2013). “IBM Watson: How the Jeopardy-winning supercomputer was born, and what it wants to do next.” In:, 2013-09-09.
    • QUOTE: "The system today compared to the Jeopardy system is approximately 240 percent faster and it is one-sixteenth the size. The system that was the size of a master bedroom will now run in a system the size of the vegetable drawer in your double-drawer refrigerator."

      To get Watson from Jeopardy to oncology, there were three processes that the Watson team went through: content adaptation, training adaptation, and functional adaptation – or, to put it another way, feeding it medical information and having it weighted appropriately; testing it out with some practice questions; then making any technical adjustments needed – tweaking taxonomies, for example.




  • (Ferruci et al., 2010) ⇒ David Ferrucci, Eric Brown, Jennifer Chu-Carroll, James Fan, David Gondek, Aditya A. Kalyanpur, Adam Lally, J. William Murdock, Eric Nyberg, John Prager, Nico Schlaefer, Chris Welty(2011). “Building Watson: An overview of the DeepQA project." In: AI Magazine, 31(3).
    • 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.
    • QUOTE: As a measure of the Jeopardy Challenge’s breadth of domain, we analyzed a random sample of 20,000 questions extracting the lexical answer type (LAT) when present. We define a LAT to be a word in the clue that indicates the type of the answer, independent of assigning semantics to that word.