Earlier this week, after the Watson computer defeated human champions in a three-day match on the popular TV show Jeopardy, its creators and a Massachusetts software company prepared to sell a medical version of the smart machine.
IBM has an agreement with Nuance Communications Inc. of Burlington to sell Watson-based products to health care providers. Nuance already offers voice-recognition software for a variety of applications. The companies are pitching forthcoming technology that will, among other things, allow medical providers to describe symptoms orally and get diagnostic information in return.
Watson technology will enable Nuance to add artificial intelligence to its offerings. And that's where this blog comes in. I've been talking about these two topics, albeit separately, for some time now.
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.
For example, a doctor considering a patient’s diagnosis could use Watson’s analytics technology, in conjunction with Nuance’s voice and clinical language understanding solutions, to rapidly consider all the related texts, reference materials, prior cases, and latest knowledge in journals and medical literature to gain evidence from many more potential sources than previously possible, thereby helping the medical professional to confidently determine the most likely diagnosis and treatment options.
For more information about the Watson computing system and the Jeopardy! challenge, click here.
On earlier posts to this blog, I discussed the semantic Web, ontologies, and speech recognition software from Nuance (and Adobe):
See, for example, my November 7, 2009 post "Vagueness, Logic and Ontology: Fuzzy Ontologies"
In traditional ontology theory, concepts and roles are crisp sets. However, there is a great deal of fuzziness in the real world.
For example, one may be interested in finding “a very strong flavored red wine” or in reasoning with concepts such as “a cold place”, “an expensive item”, “a fast motorcycle”, etc.
A possible solution to handling uncertain data is to incorporate fuzzy logic into ontologies. Unfortunately, these fuzzy ontologies have shortcomings – reasoners for fuzzy ontologies are not yet so polished as those for crisp (aka traditional) ontologies.
See, for example, my May 30, 2009 post on "Speech Recognition Software"
The trivial example of one-to-one translation given there can be extended to one-to-many translation: That is, in a matter of seconds, you could “program” Dragon Medical to type out a whole sentence in response to your speaking just a single word or code into the microphone. And, vice versa: you could “program” Dragon Medical to type out just a single word or code in response to your speaking a whole sentence into the microphone.
