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- http://www.wired.com/wired/archive/8.02/autonomy_pr.html (Print version)
- The broad sweep of [Peter] Rayner's academic and cultural interests was a powerful influence on the young engineer, who says his mentor's insistence on problem solving over "hand-waving, headline-grabbing rubbish" encouraged him to think of innovative and practical applications for Bayes' work. It was over morning coffee with Rayner and other graduate students, says Lynch, that he first considered applying the 250-year-old theorem to the task of training computers to recognize patterns of meaning.
- Susan Dumais, senior researcher of adaptive systems and interaction for Microsoft, notes that a Web surfer who types printer into a search engine or help system is probably not seeking information on writing code for printer-driver software - even if the word appears 100 times in such a document, yielding a strong keyword match. The average person is probably looking for information on setting up a printer, trying to figure out why a printer isn't working, or looking for a good price on equipment. The prior knowledge of what most users are searching for can be factored into Bayesian information-retrieval strategies. The ability of Bayes nets to snare relationships among words that elude keyword-matching schemes "points to the rich way that human discourse is generated," Dumais observes, "out of words not said and all the finely shaded ways of saying things."
- Lynch sees the marriage of Bayes' ideas and modern processing power as characteristic of a new, more mature phase of technology - an era in which humanity will no longer believe it's standing at the center of the universe.
"Rules-based, Boolean computing assumes that we know best how to solve a problem," he says. "My background comes completely the other way. The problem tells you how to solve the problem. That's what the next generation of computing is going to be about: listening to the world."
- w: Michael Richard Lynch
- w: Autonomy Corporation
- w: Interwoven
- w: Ronald Cohen
- w: Susan Dumais
- w: Latent semantic analysis
- w: Bayes' theorem
- w: Bayesian inference
- w: Bayesian probability
In the 1980s, there was a dramatic growth in research and applications of Bayesian methods, mostly attributed to the discovery of Markov chain Monte Carlo methods, which removed many of the computational problems, and an increasing interest in nonstandard, complex applications. Despite the growth of Bayesian research, most undergraduate teaching is still based on frequentist statistics. Nonetheless, Bayesian methods are widely accepted and used, such as for example in the field of machine learning.
- w: Bayesian network
- w: Information theory
See also 
- Cherry, Colin (1957). On Human Communication: A Review, a Survey, and a Criticism . The M.I.T. Press, 1966. [+]