Monday, February 21, 2011

Watson and IBM’s Deep Question Answering (QA)

Earlier last week, we watched IBM's Watson super computer beat two human contestants at Jeopardy. I actually think Watson won because it was faster at pushing the buzzer. Albeit, the performance was an impressive display of IBM’s Deep Question Answering (QA). On Thursday, IBM announced a partnership to apply this technology to medicine. The partner, Nuance Communications, Inc., also developed Dragon Dictation for iOS devices, and voice recognition for other mobile phones. Imagine "Watson-to-go" on a smart phone: speak the symptoms along with text-based data, and have Watson produce a diagnosis. As explained:
Watson's ability to analyze the meaning and context of human language, and quickly process information to find precise answers can assist decision makers, such as physicians and nurses, unlock important knowledge and facts buried within huge volumes of information, and offer answers they may not have considered to help validate their own ideas or hypotheses.
How could this technology be applied in education? Could we feed in the URL to a student's rich e-portfolio, along with the criteria we would like to assess, and could Watson give us feedback around a variety of criteria? Or would we want this capability? Would it help teachers pinpoint areas for development in written language? What about analysis of some of the 21st Century Skills, especially Inventive Thinking—Intellectual Capital: Adaptability/Managing Complexity and Self-Direction; Curiosity, Creativity and Risk-taking; Higher Order Thinking and Sound Reasoning. Is this the type of complexity that the QA technology could be designed to analyze? Or is this assessment and analysis task too difficult, even for Watson? Perhaps with the Gates Foundation's emphasis on Learning Analytics, and the upcoming Learning Analytics & Knowledge Conference, February 27-March 1, 2011 in Banff, Alberta, there will be some development in this area.
Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.

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