Government Computer News has a good round-up of ongoing defense research, some of which (as we all know) is related in one way or another to AI. Nothing too Earth-shattering, but a nice collection of news for the casual reader (GCN is not known for hard-hitting technical content). The coverage includes the DARPA Urban Challenge, machine translation and cognitive computing initiatives.
via AAAI.org News
Showing posts with label translation. Show all posts
Showing posts with label translation. Show all posts
Monday, August 6, 2007
Thursday, July 26, 2007
Google’s Future
MIT Technology Review recently posted an interview with Peter Norvig, director of research at Google, regarding the future of search. An AI expert, Norvig sees machine translation and speech recognition as the next big things to improve Google's search and advertising. He also identifies understanding the contents of documents as one of the two biggest problems in search... leading to much NLP work ongoing at Google.
via AAAI.org News
via AAAI.org News
Monday, June 18, 2007
Delusions of Grandeur
An article has been floating around the web that overstates the importance of video game artificial intelligence.
I don't agree. I think this (just like everything else, right?) is a problem of semantics. Consider this quote:
Researchers all over the world are doing exciting and relevant work in AI, yet we don't recognize it at such. Consider the work by Google in machine translation, by Stanford and other in autonomous navigation, and by scores of groups in data mining. I would argue that these are monumentally important advancements, far greater than the incrementally improved enemy combat tactics in Halo, yet most people don't recognize them as artificial intelligence.
AI has a PR issue. Perhaps AAAI needs an advocacy arm?
"A lot of the most interesting work in artificial intelligence is being done by game developers," says Bruce Blumberg, senior scientist at Blue Fang Games in Waltham, and formerly a professor at MIT's Media Lab.
I don't agree. I think this (just like everything else, right?) is a problem of semantics. Consider this quote:
"As soon as we solve a problem, instead of looking at the solution as AI, we come to view it as just another computer system." - Martha Pollack
Researchers all over the world are doing exciting and relevant work in AI, yet we don't recognize it at such. Consider the work by Google in machine translation, by Stanford and other in autonomous navigation, and by scores of groups in data mining. I would argue that these are monumentally important advancements, far greater than the incrementally improved enemy combat tactics in Halo, yet most people don't recognize them as artificial intelligence.
AI has a PR issue. Perhaps AAAI needs an advocacy arm?
Monday, April 2, 2007
Google Speaks 12 Languages
I just read an interesting article about the great success Google has been having using statistical machine translation for automatic translation of foreign language documents, as opposed to the rule & grammar based approaches used before.
I, for one, am very much persuaded by the idea that human language is so complex (we don't even fully understand it, just ask a linguist!) that it can never be fully hard-coded into a machine, but rather, a machine must "learn" it on it's own for the most part (we'll guide it and help it where needed). I like the approach that Google is taking, but without a conceptual framework or knowledge representation model, the system really isn't "understanding" or "comprehening" anything--it's just doing a "dumb" translation using statistical references. Still quite an accomplishment, but entirely different from my ultimate objective.
I, for one, am very much persuaded by the idea that human language is so complex (we don't even fully understand it, just ask a linguist!) that it can never be fully hard-coded into a machine, but rather, a machine must "learn" it on it's own for the most part (we'll guide it and help it where needed). I like the approach that Google is taking, but without a conceptual framework or knowledge representation model, the system really isn't "understanding" or "comprehening" anything--it's just doing a "dumb" translation using statistical references. Still quite an accomplishment, but entirely different from my ultimate objective.
Tuesday, March 20, 2007
Machine Analysis of Scientific Papers
There's a lot of exciting work going on in NLP right now, and it's hard to keep up...and even harder to maintain a blog about all of it! Larry pointed me to an article from last year detailing an automated tool for analyzing and comparing experimental reports.
This project sounds like some sort of XML markup scheme for outlining scientific papers, similar to the ontologies powering the semantic web initiative. It probably involves too much overhead to be widely adopted and therefore be useful, as it likely requires the author to spend an awful lot of additional time constructing papers such that the EXPO system could parse it. A much more elegant method would be for the system to perform an automatic analysis and markup of the text, however that would require NLP technology beyond what's currently available.
As you might imagine, a similar hurdle exists for the adoption of the semantic web in general. However, the analysis & synthesis of peer-reviewed journals presents us with yet another "killer app" for NLP. There is simply far too much information covering any given topic being generated for a human being to digest, even experts in a particular field...let alone a renaissance man or polymath to synthesize from diverse fields. Only a machine with advanced NLP capabilities would be able to make sense of it all and create new knowledge from what's already available.
This project sounds like some sort of XML markup scheme for outlining scientific papers, similar to the ontologies powering the semantic web initiative. It probably involves too much overhead to be widely adopted and therefore be useful, as it likely requires the author to spend an awful lot of additional time constructing papers such that the EXPO system could parse it. A much more elegant method would be for the system to perform an automatic analysis and markup of the text, however that would require NLP technology beyond what's currently available.
As you might imagine, a similar hurdle exists for the adoption of the semantic web in general. However, the analysis & synthesis of peer-reviewed journals presents us with yet another "killer app" for NLP. There is simply far too much information covering any given topic being generated for a human being to digest, even experts in a particular field...let alone a renaissance man or polymath to synthesize from diverse fields. Only a machine with advanced NLP capabilities would be able to make sense of it all and create new knowledge from what's already available.
Tuesday, August 23, 2005
Google Tops in Machine Translation
News.com.com reports that Google scored highest in a recent language translation competition run by the U.S. Government.
Google beat out competitors such as IBM and the University of Southern California on the Arabic-to-English and Chinese-to-English translation tests. Interestingly enough, although Google offers an Arabic homepage, Google language tools does not currently offer Arabic translation. Looks like we've stumbled across some more top-secret Google software...
Google beat out competitors such as IBM and the University of Southern California on the Arabic-to-English and Chinese-to-English translation tests. Interestingly enough, although Google offers an Arabic homepage, Google language tools does not currently offer Arabic translation. Looks like we've stumbled across some more top-secret Google software...
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