Given my interests, the most interesting part of the eWeek article is:
Natural language was billed as a replacement for the GUI, but it failed to achieve that. It also failed as a query language for databases, as a calculation language for spreadsheets and as a document creation language, Bosworth said. "Humans expect a human level of comprehension," he said, noting that database queries and spreadsheet formulas have to be exact.
But natural language got a second life, too, triggered in part by Microsoft Help, and the next step turned out to be Google, Bosworth said. The trick to being successful with natural language is to "start with a fuzzy problem, one no human can resolve anyway…orient it around search, and the magic is just in the ranking," he said.
Exactly. Another example that I'm familiar with is bioinformatics. All the methods we use for sequence comparison, gene prediction, regulatory network reconstruction, protein structure prediction, and so on, are fallible. If exact answers are required, nothing could be done. But inexact answers are still useful, and in any case there is no alternative. Natural language may be useless for database queries where exact answers are expected, like the standard database examples (employee, department, ...), but in bioinformatics databases, some of the core relations are uncertain anyway.
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