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.
%0 Journal Article
%1 AIMag2303
%A Ferrucci, David
%A Brown, Eric
%A Chu-Carroll, Jennifer
%A Fan, James
%A Gondek, David
%A Kalyanpur, Aditya
%A Lally, Adam
%A Murdock, J.
%A Nyberg, Eric
%A Prager, John
%A Schlaefer, Nico
%A Welty, Chris
%D 2010
%J AI Magazine
%K qa
%N 3
%P 59--79
%R 10.1609/aimag.v31i3.2303
%T Building Watson: An Overview of the DeepQA Project
%U https://www.aaai.org/ojs/index.php/aimagazine/article/view/2303
%V 31
%X 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.
@article{AIMag2303,
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.},
added-at = {2018-01-10T20:31:53.000+0100},
author = {Ferrucci, David and Brown, Eric and Chu-Carroll, Jennifer and Fan, James and Gondek, David and Kalyanpur, Aditya and Lally, Adam and Murdock, J. and Nyberg, Eric and Prager, John and Schlaefer, Nico and Welty, Chris},
biburl = {https://www.bibsonomy.org/bibtex/2a121df3d85a19a14f84238111243c8b6/defeatnelly},
doi = {10.1609/aimag.v31i3.2303},
interhash = {c3fa1d7b2cfb8fc1742b8a4ec0151392},
intrahash = {a121df3d85a19a14f84238111243c8b6},
issn = {0738-4602},
journal = {AI Magazine},
keywords = {qa},
number = 3,
pages = {59--79},
timestamp = {2018-01-10T20:31:53.000+0100},
title = {Building Watson: An Overview of the DeepQA Project},
url = {https://www.aaai.org/ojs/index.php/aimagazine/article/view/2303},
volume = 31,
year = 2010
}