

USC researchers use software to mine blogs for cause-and-effect data - dctoedt
http://www.economist.com/science-technology/displaystory.cfm?story_id=15660874

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_delirium
Andrew Gordon has a history of pretty interesting work this area, and is
probably one of the best researchers in the overlap of symbolic knowledge
representation, data-mining, and pragmatic approaches to commonsense-reasoning
problems--- areas that often like to resist overlapping.

This sounds like the sort of work he's as well placed as anyone to do. As a
commenter on the linked article points out, it's "epistemologically unsound"
to mine data in this way, and not even humans really understand causality. And
yet, as that comment misses, humans are able to make routine use of casual
inferences, even though we don't "really" understand causality, and we acquire
our causal information in ways that are almost certainly epistemologically
unsound (people do not usually use careful scientific experiments in building
their everyday models of how the world works). Even uneducated and very young
humans can do stuff along those lines that'd be amazing for AI to be able to
do! It'll be interesting to see if approaches like this one can help computers
pragmatically acquire similar levels of proficiency about causal
relationships, as an alternative to, say, large databases of consistent,
carefully hand-authored facts like Cyc.

His page: <http://people.ict.usc.edu/~gordon/>

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elblanco
Dupe: <http://news.ycombinator.com/item?id=1186429>

