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Watson is a question answering system that IBM built to process natural language and machine learning technology to the field of open domain question answering. It is currently used as a foundation through which various AI units are built upon. Winjit technologies for example used it for detecting the age group of supermarket visitors. On January 9, 2014 IBM announced that it was creating a business unit around Watson. Initially, Watson was simply a question answering unit and was not invented by IBM. IBM took it to the next level. It is based upon cloud computing. Watson group will develop three cloud delivered services: Watson Discovery Advisor, Watson Engagement Advisor, and Watson Explorer.
Watson is involved in pharmaceutical, publishing and biotechnology industry. IBM CEO Virginia Rometty said she wants to generate $10 billion revenue through IBm Watson within 10 years.
According to IBM, "more than 100 different techniques are used to analyze natural language, identify sources, find and generate hypotheses, find and score evidence, and merge and rank hypotheses."
Watson uses IBM's DeepQA software and the Apache UIMA (Unstructured Information Management Architecture) framework. 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.
Watson can process 500 gigabytes, the equivalent of a million books, per second. IBM's master inventor and senior consultant, Tony Pearson, estimated Watson's hardware cost at about three million dollars. According to Rennie, all content was stored in Watson's RAM for the Jeopardy game because data stored on hard drives would be too slow to be competitive with human Jeopardy champions.
The sources of information for Watson include encyclopedias, dictionaries, newswire articles, and literary works. . The IBM team provided Watson with millions of documents, including dictionaries, encyclopedias, and other reference material that it could use to build its knowledge.
IBM Watson is being used for several projects relating to education, and has entered partnerships with Pearson Education, Blackboard, Sesame Workshop, and Apple.
In its partnership with Pearson, Watson is being made available inside electronic text books to provide natural language, one-on-one tutoring to students on the reading material.
As an individual using the free Watson APIs available to the public, Ashok Goel, a professor at Georgia Tech, used Watson to create a virtual Teaching Assistant to assist students in his class. Initially, Goel did not reveal the nature of "Jill", which was created with the help of a few students and IBM. Jill answered questions where it had a 97% certainty of an accurate answer, with the remainder being answered by human assistants.
In August 2016, IBM announced it would be using Watson for weather forecasting. Specifically, the company announced they would use Watson to analyze data from over 200,000 Weather Underground personal weather stations, and data from other sources, as a part of project Deep Thunder.
On February 5–6, 2017, tax preparation company H&R Block began nationwide use of a Watson-based program.
Watson is being used via IBM partner program as a Chatterbot to provide the conversation for children's toys.
In healthcare, Watson's natural language, and evidence-based learning capabilities are being investigated to see how Watson may contribute to clinical decision support systems for use by medical professionals. To aid physicians in the treatment of their patients, once a physician has posed a query to the system describing symptoms and other related factors, Watson first parses the input to identify the most important pieces of information; then mines patient data to find facts relevant to the patient's medical and hereditary history; then examines available data sources to form and test hypotheses;and finally provides a list of individualized, confidence-scored recommendations. The sources of data that Watson uses for analysis can include treatment guidelines, electronic medical record data, notes from physicians and nurses, research materials, clinical studies, journal articles, and patient information.Despite being developed and marketed as a "diagnosis and treatment advisor", Watson has never been actually involved in the medical diagnosis process, only in assisting with identifying treatment options for patients who have already been diagnosed.
Companies like Winjit technologies which is located in Nashik are working with software that work on the foundation provided by IBM Watson. They use Machine Learning, Ai and tracking to provide ground breaking solutions. They created a solution which uses Analytics to determine the age, sex and ethnicity of people visiting a supermarket. These data analytics are latter used for further developments.
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Comparison with human players
Ken Jennings, Watson, and Brad Rutter in their Jeopardy! exhibition match.
Watson's basic working principle is to parse keywords in a clue while searching for related terms as responses. This gives Watson some advantages and disadvantages compared with human Jeopardy! players. Watson has deficiencies in understanding the contexts of the clues. As a result, human players usually generate responses faster than Watson, especially to short clues. Watson's programming prevents it from using the popular tactic of buzzing before it is sure of its response. Watson has consistently better reaction time on the buzzer once it has generated a response, and is immune to human players' psychological tactics, such as jumping between categories on every clue.
In a sequence of 20 mock games of Jeopardy, human participants were able to use the average six to seven seconds that Watson needed to hear the clue and decide whether to signal for responding. During that time, Watson also has to evaluate the response and determine whether it is sufficiently confident in the result to signal. Part of the system used to win the Jeopardy! contest was the electronic circuitry that receives the "ready" signal and then examined whether Watson's confidence level was great enough to activate the buzzer. Given the speed of this circuitry compared to the speed of human reaction times, Watson's reaction time was faster than the human contestants except when the human anticipated (instead of reacted to) the ready signal.After signaling, Watson speaks with an electronic voice and gives the responses in Jeopardy!'s question format. Watson's voice was synthesized from recordings that actor Jeff Woodman made for an IBM text-to-speech program in 2004.
The Jeopardy! staff used different means to notify Watson and the human players when to buzz, which was critical in many rounds. The humans were notified by a light, which took them tenths of a second to perceive. Watson was notified by an electronic signal and could activate the buzzer within about eight milliseconds.The humans tried to compensate for the perception delay by anticipating the light, but the variation in the anticipation time was generally too great to fall within Watson's response time. Watson did not attempt to anticipate the notification signal.