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Levesque saves his most damning criticism for the end of his paper. It’s not just that contemporary A.I. hasn’t solved these kinds of problems yet; it’s that co
by dvt 13y ago
Levesque saves his most damning criticism for the end of his paper. It’s not just that contemporary A.I. hasn’t solved these kinds of problems yet; it’s that contemporary A.I. has largely forgotten about them. In Levesque’s view, the field of artificial intelligence has fallen into a trap of “serial silver bulletism,” always looking to the next big thing, whether it’s expert systems or Big Data, but never painstakingly analyzing all of the subtle and deep knowledge that ordinary human beings possess.
This is very true. But I think that recent AI experts (as opposed to those doing this work in the 70s) have realized that trying to tackle linguistic analysis is very very (very) hard. The problem with language (or more correctly, discourse) analysis is that even outside the realm of computing, it still hasn't been fully explicated.
A couple of months ago I took a graduate philosophy of language seminar (taught by the brilliant Sam Cumming at UCLA) in which we looked at various theories of discourse. It would be an understatement to say that these theories vary wildly. We have the classical RST (Rhetorical Structure Theory) by Mann and Thompson[0] (renowned linguists at USC & UCSB), Jan Van Kuppevelt's erotetic model[1], Andrew Kehler's Theory of Grammar[2] and a half-dozen or so more that I don't even remember.
So let's forget about computers for a second. We don't even know how humans process discourse. My term paper was about the parallel relation which is a very talked-about topic (almost as much as anaphora; see The New Yorker article) in the academic community; not only are such linguistic phenomena difficult to theoretically model, they are nigh impossible to practically implement (say, in some sort of AI schema).
So I'm not even surprised most AI folks just started doing work on SVM's or ANN's, or Markov Chains, or what-have-you. It seems more practical to do work on stuff that could actually benefit from machine learning, as opposed to trying to solve incredibly difficult (and mostly theoretical) problems like discourse analysis.
The bottom line is that we're still a ways off from having computers like those in Star Trek - computers that understand anaphora, parallelism, ellipses, etc, etc.
[0] http://www.sfu.ca/rst/ http://www.sfu.ca/rst/
[1] http://www.jstor.org/stable/4176301 http://www.jstor.org/stable/4176301
[2] http://www.amazon.com/Coherence-Reference-Theory-Grammar-Andrew/dp/1575862158 http://www.amazon.com/Coherence-Reference-Theory-Grammar-And...
- sgt101 13y agoThanks for your post - very specific and informative. I believe that it will not be necessary to cope with the corner cases of discourse for a machine to be regarded as intelligent, and I believe that machines that can cope with those corner cases (if realisable ever) may not be regarded as intelligent (I mean, doing this is not sufficient). I think that in this sense discourse analysis is rather like computer chess - a goal that if attained may seem rather irrelevant to the grander challenge of creating machines that are capable of significant contributions to our culture. I think that we risk attacking the problems that appear to us to be interesting.
- jliechti1 13y ago>> So let's forget about computers for a second. We don't even know how humans process discourse... >> It seems more practical to do work on stuff that could actually benefit from machine learning, as opposed to trying to solve incredibly difficult (and mostly theoretical) problems like discourse analysis. The thing I am not sure about is how much it makes sense to attempt to create discourse analyses vs doing more "practical" work. The reason is that a complete and accurate discourse analysis seems nearly impossible. Having read through a few reference grammars and phonological analysis of various languages myself, it seems any rules linguists create are always wrought with copious exceptions and special cases (though certainly not all! There are examples of phonological rules that are very very regular across languages). Some of these analyses were extremely intricate. I got the impression that many of them suffered from the mistake of trying to prescribe patterns to data that does not have any patterns.
- deleted 13y ago[deleted]
- dvt 13y agoI completely agree. Having worked with some AI systems in the past (mostly ANNs although I'd love to even begin to understand SVMs), I think that there's more "practicality" in just building robots/software that (based on various pattern analyses/etc/etc) figures out where a malignant tumor is, for example, rather than a robot that can understand coherence relations or what-have-you. So I don't fault the CS community from stepping away from linguistic puzzles and getting on with more useful stuff. After all, that's what engineering is. It looks like Levesque disagrees, but I'm not sure he's right.
- textminer 13y agoSVMs are lickety-split simple. You're drawing the best line/plane/hyperplane between some points. You can turn this into a nice convex optimization program, given some conditions. If this thing isn't fully separable, you can fudge it a little with some penalty terms, or you can cheaply project these points into some space where such a separation does exist. The hard part will always be your feature extraction, labeled data collection, and the parameter tuning for everything I just waved my hand at.
- Houshalter 13y agoI wonder if we could make a computer that could understand a simple and made up language like lojban. The grammar is consistent and well defined, the hard part would be trying to get it to learn what words actually mean.