Adam Gartenberg's Blog

Business Analytics and Optimization, IBM and Social Marketing

Craig Rhinehart: 10 Things About the Tech Behind Watson


I just came across my colleague Craig Rhinehart's terrific post summarizing what makes Watson, IBM's Jeopardy-playing computer, tick.  (10 Things You Need to Know About the Technology Behind Watson)

He describes the Natural Language Processing (NLP), DeepQA and Unstructured Information Management Architecture (UIMA) technologies that help make up the brains behind Watson, and also compares Watson to previous supercomputers like Deep Blue, and all of it makes for compelling reading.

The part that I wanted to call out in particular was Craig's answer on the business benefits of a computer like Watson:

7.  How would this QA technology be used in a business setting?
DeepQA technology provides humans with a powerful tool for their information gathering and decision support.  One of many possible scenarios could be for the end user to enter their question in natural language form, much as if they were asking another person, and for the system to sift through vast amounts of potential evidence to return a ranked list of the most compelling, precise answers along with links to supporting or refuting evidence.  Other important scenarios will use DeepQA to analyze a collection of content and data representing a problem, for example a technical support problem or a medical case.  DeepQA will start to search for solution gathering and assessing evidence from many disparate data sources engaging human users to help provide the missing pieces of information that can help arrive at a solution or for example a differential diagnosis, in the case of medicine.
In addition, these answers would include summaries of their justifying or supporting evidence, allowing the user to quickly assess the evidence and select the correct answer.
Business applications include Customer Relationship Management, Regulatory Compliance, Contact Centers, Help Desks, Web Self-Service, Business Intelligence and more.  These applications will demand a deep understanding of users’ questions and analysis of huge volumes of natural language, structured and semi-structured content to rapidly deliver and justify precise, succinct, and high-confidence answers.


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