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IBM Watson
IBM Watson is a computer system capable of answering questions posed in natural language. It was developed as a part of IBM's DeepQA project by a research team, led by principal investigator David Ferrucci. Watson was named after IBM's founder and first CEO, industrialist Thomas J. Watson.
The computer system was initially developed to answer questions on the popular quiz show Jeopardy! and in 2011, the Watson computer system competed on Jeopardy! against champions Brad Rutter and Ken Jennings, winning the first-place prize of US$1 million.
In February 2013, IBM announced that Watson's first commercial application would be for utilization management decisions in lung cancer treatment, at Memorial Sloan Kettering Cancer Center, New York City, in conjunction with WellPoint (now Elevance Health).
Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering. The system is named DeepQA (though it did not involve the use of deep neural networks).
IBM stated that Watson uses "more than 100 different techniques to analyze natural language, identify sources, find and generate hypotheses, find and score evidence, and merge and rank hypotheses."
In recent years[when?], Watson's capabilities have been extended and the way in which Watson works has been changed to take advantage of new deployment models (Watson on IBM Cloud), evolved machine learning capabilities, and optimized hardware available to developers and researchers. [citation needed]
Watson uses IBM's DeepQA software and the Apache UIMA (Unstructured Information Management Architecture) framework implementation. The system was written in various languages, including Java, C++, and Prolog, and runs on the SUSE Linux Enterprise Server 11 operating system using the Apache Hadoop framework to provide distributed computing.
Other than the DeepQA system, Watson contained several strategy modules. For example, one module calculated the amount to bet for Final Jeopardy, according to the confidence score on getting the answer right, and the current scores of all contestants. One module used the Bayes rule to calculate the probability that each unrevealed question might be the Daily Double, using historical data from the J! Archive as the prior. If a Daily Double is found, the amount to wager is computed by a 2-layered neural network of the same kind as those used by TD-Gammon, a neural network that played backgammon, developed by Gerald Tesauro in the 1990s. The parameters in the strategy modules were tuned by benchmarking against a statistical model of human contestants fitted on data from the J! Archive, and selecting the best one.
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IBM Watson AI simulator
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IBM Watson
IBM Watson is a computer system capable of answering questions posed in natural language. It was developed as a part of IBM's DeepQA project by a research team, led by principal investigator David Ferrucci. Watson was named after IBM's founder and first CEO, industrialist Thomas J. Watson.
The computer system was initially developed to answer questions on the popular quiz show Jeopardy! and in 2011, the Watson computer system competed on Jeopardy! against champions Brad Rutter and Ken Jennings, winning the first-place prize of US$1 million.
In February 2013, IBM announced that Watson's first commercial application would be for utilization management decisions in lung cancer treatment, at Memorial Sloan Kettering Cancer Center, New York City, in conjunction with WellPoint (now Elevance Health).
Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering. The system is named DeepQA (though it did not involve the use of deep neural networks).
IBM stated that Watson uses "more than 100 different techniques to analyze natural language, identify sources, find and generate hypotheses, find and score evidence, and merge and rank hypotheses."
In recent years[when?], Watson's capabilities have been extended and the way in which Watson works has been changed to take advantage of new deployment models (Watson on IBM Cloud), evolved machine learning capabilities, and optimized hardware available to developers and researchers. [citation needed]
Watson uses IBM's DeepQA software and the Apache UIMA (Unstructured Information Management Architecture) framework implementation. The system was written in various languages, including Java, C++, and Prolog, and runs on the SUSE Linux Enterprise Server 11 operating system using the Apache Hadoop framework to provide distributed computing.
Other than the DeepQA system, Watson contained several strategy modules. For example, one module calculated the amount to bet for Final Jeopardy, according to the confidence score on getting the answer right, and the current scores of all contestants. One module used the Bayes rule to calculate the probability that each unrevealed question might be the Daily Double, using historical data from the J! Archive as the prior. If a Daily Double is found, the amount to wager is computed by a 2-layered neural network of the same kind as those used by TD-Gammon, a neural network that played backgammon, developed by Gerald Tesauro in the 1990s. The parameters in the strategy modules were tuned by benchmarking against a statistical model of human contestants fitted on data from the J! Archive, and selecting the best one.