VentureBeat (once a critic of Powerset, now more of a believer) covers the story. In summary, Powerset's technology is not some rushed together start-up demo, but the result of many man years of research and development at PARC.
VentureBeat's post contains an interview around the topic of NLP with Google's Peter Norvig. While Norvig gives some insight into the work on NLP at Google, he doesn't mention one of their main areas of focus: machine translation (MT). It is interesting to learn of his caution in the area of NLP for search while they are tasking a number of scientist at MT, possibly the hardest AI problem known to man.
Update: It took this to have John Battelle blog about the story...
Powerset is a late comer and far behind others in the NLP search tech space. NLP has always thrown away context to fit SQL database calls. A fundamentally new database architecture is required (Patents filed as early as 1994) to use every scrap of context expressed by well articulated needs (query). You can experience an award winning NLP enterprise search offering (activated in 2005) at Boston's Children's Hospital's Center for Media and Child Health - www.cmch.tv - go to their "research" page and experience "Smart Search." This NLP engine encourages (for highest precision) an everyday conversational query of unlimited length and complexity including "user jargon" of ten social science professional domains."
The next and final (post Google/Powerset) achievement in breakthrough user experience will be Jarg Corporation’s Semantic Knowledge Indexing Platform (SKIP) launch mastering "NOP" Natural Object Parsing that co-populates "well-understood native object content fragments" in the same master index with NLP-graph fragments. This final step - using conversational style requests (over a cell phone or keyboard) will provide total information awareness associated with the "roll" of the user - as derived on the fly from the full context of the request's information needs. Only relevant knowledge will be considered and the more contexts in the request - the more highly personalized will be the returns-ranking. These returns will be a “collage,” ranked by fit-to-context, of image segments, fragrances, text, structure segments, music segments and all forms of knowledge with precise contextual relation to your on the fly the needs – fit to your “user’s roll” of the moment. Jarg will be seeking its very fist institutional capital starting in March 2007. Jarg has incorporated Semantx Life Science, Inc. Care Commons, Inc and Preemptive Alert Corporation to become best of breed in their verticals.
Posted by: Michael Belanger | February 10, 2007 at 12:50 PM
I have taken a look at NLP technology since 2002 and there was a firm in the Netherlands formed out of Phillips called Tarchon that has been a real pioneer. The problem with the technology so far is that its ability to understand nuance, using advanced contextual pattern recognition, based on clustering has been unable to deliver a high enough level of accuracy, particularly when one is looking for evidence of "events" or triggers through a large unconstructed landscape. Is the way the technology evolving likely in your view to overcome this deficiency, which I don't think that optimization and learning engines based on human cognitive input can completely overcome.
Posted by: Roger Portnoy | April 12, 2007 at 06:36 PM
Uh oh i thought this was Neuro Linguistic Programming..Lolz
Posted by: Tony Mendoza | November 02, 2007 at 02:35 PM