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February 21, 2012

Super Portable Analytics

Robert Gelber

A year after IBM Watson beat the two most accomplished Jeopardy players at their own game, the lead researcher behind the technology says that future computers will learn through the data they receive, rather than have to be programmed for each individual problem.

In a Computerworld UK article, David Ferrucci, IBM Fellow and principal investigator for Watson technologies, explained that if computers could learn “through continuous interaction with humans they will start to understand the kind of data and the kind of computation we need” instead of someone sitting down and programming them.

In 1997, IBM’s Deep Blue, battled chess master Gary Kasparov in a 6 game match that resulted in 2 wins for Deep Blue, 1 win for Kasparov and 3 draws.  It was an achievement for IBM and Deep Blue, but that super was not designed to play a game like Jeopardy. The differences between the game of chess and Jeopardy explains Ferrucci’s prediction. Chess has a large set number of mathematical equations requiring only mathematical answers to be returned. However, Jeopardy requires interaction with a game show host and an understanding of context and language that is not found in chess.

Watson was able to win the Jeopardy match not just through processing horsepower and the data it had stored, but because it could interpret questions given in multiple contexts to achieve the best answer. This incorporation of big data sets and contextual linguistic analytics explains Watson’s entrance into other areas of research.

The platform has evolved beyond the classic computer model of single answers to single questions. “The Watson technology is not about question-in and answer-out, but rather it is understanding a problem,” explains Ferrucci. Rather, he says, it asks itself and users for feedback so it can more confidently answer questions that are presented to it, while capturing new information that can be used.

Medical researchers from Columbia University are working with IBM, adapting Watson to offer medical diagnosis and treatment. One of the biggest medical issues today is the growing amount of scientific knowledge about the human body.

Herbert Chase, a Columbia professor of clinical medicine, recalled a case that took nearly a month for him to solve. A woman was bedridden from weakening muscles and he spent most of his time asking colleagues and reading medical literature to eventually find that she had rickets. 35 years after the diagnosis, Chase fed the symptoms to Watson, whose analysis almost immediately provided the correct diagnosis.

Given the system’s adeptness at medical diagnoses, it will be interesting to see where Watson plans to take his talents next.

Full story at Computerworld UK

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