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What makes IBM Watson so smart?

Michael Cooney | Feb. 14, 2011
If you have seen any of the video of its preliminary bouts on "Jeopardy!" you know that IBM's Watson computer is pretty amazing. One of the main reasons, it turns out, is that IBM enlisted the intelligence of eight top universities to make sure Watson has superb question answering ability.

University of Southern California: Focused on large-scale Information Extraction, Parsing, and knowledge inference technologies with the goal of converting large amounts of international source materials into the general knowledge resources of the system, and reasoning with this knowledge to find inconsistencies and gaps.

University of Texas at Austin: Worked to extend the capabilities of Watson, with a focus on extensive common sense knowledge. The goal is to help the system answer questions by developing a computational resource of common sense knowledge. In particular, they have developed methods that learn to extract knowledge from text, a key requirement for the Watson system.

Rensselaer Polytechnic Institute: Worked on a visualization component to visually explain to external audiences the massively parallel analytics skills it takes for the Watson computing system to break down a question and formulate a rapid and accurate response to rival a human brain.

University at Albany: When investigating a complex topic, you rarely receive the answer you need by asking just one question; rather, you ask a series of questions to determine the solution. This technological advancement enables a computing system to remember the full interaction, rather than treating every question like the first one -- simulating a real dialogue. While not applicable for the specific "Jeopardy!" challenge given the nature of the quiz format, IBM is working with UAlbany to integrate this capability into the Watson system.

University of Trento (Italy): The aim of their ongoing collaboration with IBM is to explore advanced machine learning techniques along with rich text representations based on syntactic and semantic structures for the optimization of the IBM Watson system. The team has developed technology based on the latest results of the statistical learning theory applied to natural language understanding. This has already increased Watson's ability to learn from the questions it is asked. Learning to handle the uncertainty in the selection of the best answer from those found by Watson's search algorithms also has been one of their main research directions, IBM stated.

University of Massachusetts Amherst: Working on information retrieval, or text search. This important capability of QA technology is the first step taken: looking for and retrieving text that is most likely to contain accurate answers. The system's deep language processing capabilities then analyze the returned information to find the actual answers within that text.

IBM is pitting its natural language Watson supercomputer against two of the quiz show's biggest champion players in a $1 million man vs. machine challenge on Feb. 14, 15 and 16. The "Jeopardy!" format provides the ultimate challenge because the game's clues involve analyzing subtle meaning, irony, riddles and other complexities in which humans excel and computers traditionally do not, IBM stated.


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