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Post-Googleism At IBM With Piquant
kamesh writes "James Fallows of the New York Times reports an interesting search technology that IBM is developing. IBM demonstrated a system called Piquant, which analyzed the semantic structure of a passage and therefore exposed 'knowledge' that wasn't explicitly there. After scanning a news article about Canadian politics, the system responded correctly to the question, 'Who is Canada's prime minister?' even though those exact words didn't appear in the article. What do you think?"
They don't come out and say it, but it sounds like it's just a big ol' LSI System. It works really well for some types of searching, but I'm not sure if such a thing would out perform google for a general purpose search engine.
"Latent semantic indexing adds an important step to the document indexing process. In addition to recording which keywords a document contains, the method examines the document collection as a whole, to see which other documents contain some of those same words. LSI considers documents that have many words in common to be semantically close, and ones with few words in common to be semantically distant. This simple method correlates surprisingly well with how a human being, looking at content, might classify a document collection. Although the LSI algorithm doesn't understand anything about what the words mean, the patterns it notices can make it seem astonishingly intelligent."
"...the system responded correctly to the question, 'Who is Canada's prime minister?' even though those exact words didn't appear in the article. What do you think?" (emphasis mine)
That's pretty impressive. It takes quite a clever AI to read between lines and connect concepts, but I have to wonder how much of its 'understanding' was hard-coded rather than purely abstract. Would it be trivial to just stick in another language database and have it read translations of the article the same way?
Nevertheless it makes me feel like all the programming and design I've ever done is pathetic and I will never amount to anything. That's how it is in the software industry - always someone out there who makes you look bad.
Sam ty sig.
Reg-free link
n/t
Till you realise the computer answered 'some asshole' which could be any prime minister in the world really.
Do not try to read the dupe, thats impossible. Instead, only try to realize the truth
What truth?
There is no dupe
We are closer to build a real enterprise...
"Computer, tell me the diference between a male and a shemale"
ajf
i remember that it used to buff itself as an answerer to such questions back in the day..
must have been pre-google since i used it sometimes
world was created 5 seconds before this post as it is.
What if 2 sites said the Prime Minister of Canada was Santa? explicity said it, would that overwrite the linked information? How would the system know what is right? You can't always just pick the majority answer, so you need to set up little areas of trust "I trust www.thisplace.com and everything it says" and that site in turn will say "I trust www.overhere.com" but who allocates the trust, couldn't those people be biased?
The semantic web will have a fantastic impact on the world, but the trust issue is something that needs to be addressed, and I don't see how it can ever, globally be done.
More likely we would have systems like this for individual sites, or intranets, trusted circles that would be unlikely to contradict themselves.
hopefully one day, if we truely get a global semantic web, we can see if the answer really is 42 :]
Using google means that this would have to contend with a lot of noise - looking for one nugget of information on the internet will tend to yield a low signal-to-noise ratio. I wonder what would happen if instead, you were to run it using Wikipedia as a back end (full discosure - I'm a wikipedia admin). There'd be less information, but I suspect the quality of the results would be better.
To make laws that man cannot, and will not obey, serves to bring all law into contempt.
--E.C. Stanton
One example is meaningless. To get a realistic idea of how useful this system is, we'd like to see what it says if you ask several dozen questions. For all we know this was the one question out of 100 that it answered correctly.
We must integrate ourselves with computers to a point at which the living being and computer cannot be separated anymore. The perfect union of the biological component (wetware) and computer (hardware) will mark the end of the human race - and the birth of something new and wonderful.
Obviously this will face strong, religious and quasi-religious (ethics) resistance from the old guard but it will pass with the fools themselves.
I for one congratulate Canadian Prime Minister Tim Horton for running a great campaign and his wife Wendy for her fantastic chain of restaurants!
Does that system capable of searching for Paris Hilton when searched for the letter "P" instead?
This reminds me of the famous quote "Artificial Intelligence usually beats real stupidity"
While this is pretty impressive stuff, I think we should be wary of how it gets "information" to digest and correlate. If it gets high quality, well researched articles, it will potentially be a great tool to get the "highlights" on a subject and provide a starting point for your own research. However, if it is given less qualified articles to index, it will develop a poor and possibly perverse view of a given subject. Poorly informed people tend to talk the loudest and longest, so I'm concerned about a "finder of fact" set loose on the internet. Likewise I'm concerned about that same "finder of fact" given a limited set of information filtered by people, even if they're well meaning.
I suppose this is true of all information gathering, computerized or not. It's the potential efficiency of a system like this that scares me.
If the article doesn't come out and state that Paul Martin is the Prime Minister then how could anyone--including a computer--know that for sure? I think the submitter was stretching the truth a bit when he said the words "Prime Minister" don't appear in the article. Can you imagine an article about George Bush that didn't use the word President?
A human also extracts contextual information from articles and reaches conclusions or "beliefs" that it then gives you in response to a question.
You may believe the human because you trust its judgement and because hell, it can't remember exactly where it heard that the PM was santa.
You don't have to believe a machine as a badge of friendship the way a human will ask you to. The machine will also be able to tell you exactly where it read something that made it think the PM was Santa.
You can then go to that site and say "Ah, it's a joke," or "Ah, the machine was too stupid to understand."
Moreover, the primary purpose of such a machine is not to answer questions straight out, but to point you to sites. So if you ask for sites about the biography of the Prime Minister it will hand you an article which might imply to a stupid person that the PM is Santa. You, a not stupid person, will then say "This isn't the article I'm looking for, please give me another."
This is little different than googling "Canada Prime Minister biography" and rejecting a biography of Lester B. Pearson because his bio starts many years earlier than would be reasonable for a current PM.
The advantage is that you could say to the LSI "Who is Canada's current Prime Minister" and it could point you to a site that answers the question *even though the word current isn't used in the article.*
It just cuts down on the search term juggling we now do to get an answer that makes sense. Most of the Ask Slashdot questions that make people angry are of this nature. Someone will ask "How do I stream media of this nature to this sort of device" and many people will respond "Just google it!"
You have to know enough about streaming and the devices to give Google a good set of search terms.
Scientist: "Is there a God?"
Computer: "There is now."
/can't remember what movie/book this was from
...in the long term it may be even more important for translation between languages -- being able to discern both implicit and explicit meaning in a passage will make accurate translations easier -- and perhaps in combination with Cycorps "Cyc" (or similar project) in the extreme long term to create an artificial intelligence capable of understanding human communication.
There are other interesting possibilities. In the tradition of Esperanto and Lojban, it can also be used to gather the common aspects of natural language and create a universal second language (one much easier in grammar and spelling, more compact in expression, and more complete in meaning). This wouldn't have the cultural baggage of English, which is at present the only thing coming close to a universal second language.
There *must* be something better than the same old dumb string matching.
However, this sort of thing might be better employed as a knowledge engineer's assistant, doing the rough work of attaching useful metadata to documents drawn from the enormous piles that we've accumulated.
It sounds like an artificial intelligence (AI).
An AI is really sophisticated when it can ask its own questions of the user.
I think the prime minister of Canada is Paul Martin.
Feed it the news about Iraq. Then ask it what the war was about.
Good bye, new system, too dangerous for "national security".
45 5F E1 04 22 CA 29 C4 93 3F 95 05 2B 79 2A B2
I for one welcome our superintelligent big blue overlords.
Isaac Asimov's "The Last Question"
e +last+question%22&btnG=Google+Search
http://www.google.com/search?hl=en&q=asimov+%22th
I for one congratulate Canadian Prime Minister Tim Horton for running a great campaign and his wife Wendy for her fantastic chain of restaurants!
... he's related to the Horton of "Horton Hears a Hoo" fame, right?
Prime Minister Horton
-kgj
-kgj
No, that's not it.
Disclaimer: I haven't read the article; however, I was somewhat involved in research in this field in late 2003 and early 2004.
What the summary of the article claims IBM is developing-- a technology for getting the semantics behind an arbitrary sentence on the web-- is the Holy Grail of the discipline of Natural Language Processing (NLP) and very, very, very, _very_ far away at this point. Many people believe that we cannot ever get there (that's the point of a Holy Grail, after all), but I don't want to be quite as pessimistic (or realistic?) at this point.
The problem here is that English (or any other natural language, for that matter) isn't SML, or Haskell, or some other language with a well-defined denotational semantics. Natural language suffers from at least three problems that make it very tough to gather anything useful from a given piece of text:
(1) Grammar. Natural language isn't typechecked, and frequently uses incomplete sentences, which makes it hard to develop grammars (context-free, context-free probabilistic, lambek-style/proofnet-style or whatever else people have come up with) for it.
(2) Anaphora resolution. "I saw a dog on the street this morning. It was barking". So who's barking, street or dog? Gramatically, both would be possible; only with prior knowledge we can see that we're talking about the dog here.
(3) Polysemy. What does "play" mean, taken by itself? It can be used for different meanings in "to play a game", "a play of words", "a terrific shakespearian play" etc.; you might want to have a look at wordnet one of these days to get a feeling for this. Not knowing which meaning an arbitrary occurence of "play" refers to means that you have to try lots of options when parsing, LSIing or whatever else you do (though most people simply ignore this problem in research today-- it's too hard to disambiguate words in practice).
That's not all, of course-- try thinking of the need to deal with irony/sarcasm, metaphors, foreign words, the credibility of whichever sources you're using etc., and you'll get a pretty good feeling for why this is beyond merely being "hard". Of course, for very small problem domains (a "command language for naval vessels" was investigated in one paper I read a while ago-- those DARPA people definitely have too much money on their hands, but I digress), this can be solved, but general-purpose open-domain NLP is what you need to do a web search.
It might happen in my lifetime, but I won't hold my breath for it.
-- Christoph
Is it just me who would, if designing an AI, would have have a trivial off switch. Probably a few backups, like wire cutters next to the main power cable, a jug of water near the PSU, things like that.
It is just me, isn't it...
As some of you still remember, the original technology behind this was developed at CMU in the mid 90's when Corey Kosak, Andrej Bauer and a bunch of other talented people created the first ever natural language based neural network with a measurable IQ. People could even post questions to certain personae emulated by the neural network through the web site CGI at forum2000.org. This neural network was really fun and witty, but what you probably do not know is that all the technology in fact consisted of bored postgraduate students answering your questions.
Greetings to Kosak, Bauer and all the anonymous people who tried their best to pretend they're a software based neural network.
Can you imagine when a system of this kind is capable of reading google's online library? If knowledge is power, we are looking towards creating a very powerful entity.
Semantic analysis of text has been the holy grail of AI for decades. It's useful for all sorts of things, including information retrieval, translation, speech recognition, and summarization. IBM is hardly the only research lab working on this, or the only company on using it for enhancing search.
Of all the advanced mathematics classes that I took, one thing that stands out for me is that out of many possible solutions it was hard to just jump to the right one - what always had to be done was to select a "seed" and then improve upon the feedback that was provided ...
Google suggest is one step in that direction. You key in the first alphabet and then you get feedback ... some day it might anticipate your question itself because so many other people have asked the same question - that to me is a more realistic goal than trying to anticipate the answer.
In other words the direction of the research should be to anticipate quickly (like google suggest does) what the person is trying to ask rather than what answer the person is expecting. I know the differnce is subtle enough to raise the question of if I am saying anything different. Yes, it is - just like 2 isomers are essentially the same in construction but very different in effects -
there is a big difference in trying to anticipate the question that someone wants to ask versus anticipating the answer they are expecting.
To see a world in a grain of sand, and then to step back and see the beach where the sand lies
The genius being google's success was paying *less* attention to the content of a page when categorizing it, and relying on links *to* the page instead. Why? Because of spammers.
"Genius" would imply some sort of brand new insight, but citation analysis has had a long tradition before Google appeared on the scene as a search engine. Google's biggest achievement is probably in implementing citation analysis on a very large scale, but they didn't break completely new ground in how people search.
And, in the long run, semantics-based analysis, like IBM's Piquant, is probably going to be the better technology: citation analysis for determining relevance to a query is really just a limited substitute for understanding of the content.
User: Who is Canada's Prime Minister?
PIQUANT: WHY DO YOU ASK, USER?
Because I want to know.
WHAT IS THE REAL REASON, USER?
I want to test you.
WHAT WOULD IT MEAN TO YOU IF YOU GOT TO TEST ME?
It would mean I can determine whether or not you know who Canada's Prime Minister is.
WHAT MAKES YOU THINK I COULD TELL YOU THAT?
I programmed you to figure it out.
LET'S TALK ABOUT YOU -- NOT ME.
I want to know the name of Canada's Prime Minister.
WHY DO YOU WANT TO KNOW THE NAME OF CANADA'S PRIME MINISTER?
Are you going to tell me his name or not?
WHY ARE YOU INTERESTED IN WHETHER OR NOT I AM GOING TO TELL YOU HIS NAME OR NOT?
Stop avoiding the question! Just tell me his name!
NAMES DON'T INTEREST ME.
Fuck you!
TELL ME MORE ABOUT YOUR OWN PROBLEM, USER.
Shit!
GO ON.
You suck.
YOU'RE NOT REALLY STILL TALKING ABOUT ME, ARE YOU?
... is whot bwings os tugevza tsuzay.
I for one, welcome our new semantic web overlords! It's really great to hear that something based on semantic technologies is finally breaking through. This could be the dawn of a new era :)
The term "semantic web" refers to technologies that let authors provide markup indicating the semantics of content. That is, the "semantic web" places a burden on the authors of pages.
What natural language analysis is doing is a completely different approach: instead of burdening authors with marking up their pages to become part of a semantic web, it is taking the existing content and inferring semantics for it.
All knowledge available everywhere, any time, that would be a great thing. Heck, it's even quite scary to think about it.
That's been the AI vision for half a century. But implementing it is still way off (and IBM is only one of many institutions working on it).
does AI technology follow a similar pattern too?! thanks...
IBM researchers are right that AI techniques are getting powerful enough to allow unstructured information retrieval based on semantic content. But what IBM researchers are trying to do here is take credit for technologies and ideas developed by thousands of scientists over decades.
I don't know whether this is arrogance on the part of the IBM researchers, dishonesty, or ignorance, but either way, public statements like that on IBM are not a recommendation for the quality of their research or products.
In fact, this seems to be getting more and more common: while this has always been a problem, companies like IBM, Sun, and Microsoft are increasingly trying to take credit for entire fields of research that they contributed, if anything at all, only a miniscule amount of new work to.
Google has an unfair advantage over potential rivals. I'm talking about their ownership of the entire Usenet archive (effectively so) in the form of google-groups. No matter how good any potential rival becomes, people will always have to turn to them for access to past Usenet archives.
/. ) is proof of the power they hold by virtue of ownership of the Usenet archive, which they acquired when they bought out deja-news. Some legislation should be enacted to address this issue. Otherwise what is to stop them from one day offering pay-per-view or "premium access" to their archive ? After all Usenet is a public resource that shouldn't be at the mercy of any single corp. - no matter how large.
Google's recent mangling of google-groups (mentioned already on
As it happens, The Economist recently ran an article addressing some of these issues. The article also provides context and perspective that should be of interest to those participating in this discussion. For convenience, the full text is reproduced below; it is also accessible online (may require paid subscription).
----
Computing
From factoids to facts
Aug 26th 2004 | REDMOND, WASHINGTON
From The Economist print edition
At last, a way of getting answers from the web
WHAT is the next stage in the evolution of internet search engines? AltaVista demonstrated that indexing the entire world wide web was feasible. Google's success stems from its uncanny ability to sort useful web pages from dross. But the real prize will surely go to whoever can use the web to deliver a straight answer to a straight question. And Eric Brill, a researcher at Microsoft, intends that his firm will be the first to do that.
Dr Brill's initial crack at the problem is a system called "Ask MSR" (MSR stands for Microsoft Research). This program uses information on web pages to respond to questions to which the answer is a single word or phrase--such as "When was Marilyn Monroe born?" Ask MSR starts by manipulating the question in various ways: by identifying the verb, for example, and then changing its tense or moving it into different positions in the sentence ("Marilyn was Monroe born", "Marilyn Monroe was born" and so on). The resulting phrases are then fed into a search engine, and documents containing matching strings of words are retrieved. It sounds a promiscuous strategy, but gibberish phrases produce few matches, so, as Dr Brill puts it, "being wrong is very cheap."
Once accumulated, the pile of documents is scanned for possible answers, and these are ranked by frequency. In practice, the correct answer appears in one of the first three places around 75% of the time. That might not sound very good, but human intelligence provides a second filter, since wrong answers are often obvious. If you ask how many times Bjorn Borg won Wimbledon, for example, "1980" is not a plausible answer, but "5" is. If in doubt, clicking on an answer produces a list of links to pages which provide support for that answer.
Ask MSR is still a prototype, although Microsoft is trying to improve it and it may be launched commercially under the name AnswerBot. Dr Brill, meanwhile, has moved to a more difficult task. One of his most recent papers, written jointly with Radu Soricut of the University of Southern California, is entitled "Beyond the Factoid". It describes his efforts to build a system capable of providing 50-word answers to questions such as "What are the rules for qualifying for the Academy Awards?" This is harder than finding a single-word answer, but Dr Brill thinks it should be possible using something called a "noisy channel" model.
Such models are already employed in spell-checking and speech-recognition systems. They work by modelling the transformation between what a user means (in spell-checking, the word he intended to type) and what he does (the garbled word actually typed). Just as a telephone line distorts the voice of the person at the other end of the line, this process can be thought of as being a noisy channel that transforms the user's intention into something rather different.
By analysing many pairs of correct and mis-spelled words using statistical techniques, it is possible to predict how such transformations work in general cases. A system can then be designed to work the process backwards. Given a mis-spelled word, it can guess what that word is most likely to be a mis-spelling of.
Dr Brill's question-answering system does something similar. Many question-and-answer pairs exist on the web, in the form of "frequently asked questions" (FAQ) pages. Dr Brill trained his system using a million such pairs, to create a model that, given
The extreme centre is the paper's historical position. --Geoffrey Crowther
What is the meaning of life, the universe, and everything? My 386 says 41 but I need confirmation.
Wow, 90% of US kids can't do that. I say hail our Paragraph-COmprehending-Candian-Prime-Minister-kno wing LSI-based overlords!
meh
I dont know that a large scale semantic web is "impossible". Certainly what Ibm is accomplishing is nowhere close to the Semantic web utopia we imagine. From what i gather however All it would take is a really effective learning algorithm and the aforementioned "trust system" which i bet could be similiar to trust system of say wikipedia . eventually certain standards could be hardcoded after review by open commmunities. things such as gravity laws languages etc standards that dont change
Everybody knows the Canadian Prime Minister is Jean Poutine.
Exposed 'knowledge' that wasn't explicitly there is called Contetnt Analysis.
It was developed by British linguist professors during World War II., when the British invented RADAR, but the intelligence services could not verify how well it worked, because the the targetted subs could sink without much visual proof.
When presented with the problem, a few professor valunteered the idea to "expose 'knowledge' that wasn't explicitly there".
They could provide positive proof analysing German news of all kind that the RADAR worked.
Content analysis is now routinly used by all intelligence services, Robert Redford was doing it in the 3 Days of the Condor (or something like that...)
Just-a-random-idea
xxxxxxx
now sit back and watch how Anonymous Coward gets 0 posting point - regardless of the content.
Searching using keywords driving near-synonym lists has been done for more than a decade now.
The hot research right now are keywords driving a state machine composed of encyclopedic dictionaries, real-time text production as on the Internet (used similar to citations in the Oxford English Dictionary), and feedback nudges from the keyword originator (after all the concept the keyword originator is seeking may rapidly be evolving for *them*).
You want to use a dictionary rather than thesaurus because for the same reason you don't a priori page rank Google indices -- you don't want to selectively exclude dilute links that always exist between one concept and another.
It makes a wonderful living dance.
Using a translation engine to compare how the same text looks in two languages might be a good way for a system to "learn" context.. which does, after all, rely upon understanding the other possible meanings of a word
I have been a user for about 10 years. This ends Feb 2014. The site's been ruined. I'm off. Dice, FU
Phenominal technology, IBM built the desktop search that everybody is pushing now, way back when. Cutting edge search and indexing capabilities, fully extendable, you could write your own plugins to deal with your data (use JPEG meta tags to label pictures from your digicam? Write a little plug in so you can search through your photos) and it had semantic and linguisitic searching.
For a long time SM/2 was kind of the poster child for IBM's inability to take remarkably cool technology to the consumer. Everyone that used it thought it was cool, nobody ever knew about it. They had trouble getting the word out within the company about it. Last I heard anything about it, they were turing the technology into some kind of intranet spider. It was the shit, it might have even had primitive cross referencing, like you could search for president and it would find references to Clinton because a third article may have referred to him as the president. They seemed to have some foresight into this area, web searching has to cut out some much bullshit, you wouldn't want to contaminate your semantic searches with all of it, keeping it in intranet space might be a good idea. Local search is hot right now too though so maybe it'll come back.
After scanning a news article about Canadian politics, the system responded correctly to the question, 'Who is Canada's prime minister?'
Everyone knows he is Tim Horton!
2bits.com, Inc: Drupal, WordPress, and LAMP performance tuning.
The data annotating technology used by OmniFind (UIMA) is available for download at IBM's Alphaworks site.
In ordinary search, the text is parsed and a giant index is created. UIMA allows you to write annotators that look for additonal information, for example names of elected officials, and add those entires to the index as well.
Wikipedia isn't exactly a bastion of accuracy, either. Look at your entry on atheism; you had to lock it because the editors who wanted to work on the topic don't agree on what is even is, though the etymology of the word is crystal clear. Many of the other articles I've seen trivially fail the "neutral point of view" and tend toward either the pompous or are just plain wrong. NPOV takes a concerted effort by a thinking person. Yet you let anyone edit the articles -- so I can't say I'm surprised by this. But in the end, we're not looking at something that is anywhere near as good a reference as it could be if Wikipedia put some effort into vetting and controlling its editors.
IMHO. As someone who enjoys, and contributes, to wikipedia. :)
I've fallen off your lawn, and I can't get up.
NLP and semantic extraction and conceputal indexing is nothing new; admittedly, practical implmentations have been few and far between.
;)
However, as I'm often fond of pointing out, the problem is not getting the 80 - 90% accuracy in translation and interpretation that I'm sure these systems can attain.
The challenge quickly becomes how to deal with idioms and idiosyncratic constructions. Is this system even ready to deal with sentences like "The criminal was shot dead by police"? If it is, great. How about "The trolley rumbled through town"? Or the idiomatic "time flies"?
This is what, so far as I know, the field of computational linguistics is now facing in textual interpretation and translation. Coming up with a system to effectively identify what appear to be three-argument verbs ("Mary hammered the metal flat") or constructions or idioms above may well be something that traditional systematic recursive grammars aren't yet up to handling.
Somehow these situations have to be identified, and separated in the parsing process so that they don't get processed like standard grammatical expressions.
Hopefully these problems are how I'll make my living
You mean he was still awake after he read the article on Canadian politics?
Wow, he's got me beat.
Bush is a room temperature (and we're talking degrees C here) IQ. Live with it.
nice one
That's the 1954 short story "Answer", by Fredric Brown.
Well ok maybe not that great of a feat, but it's a start.
"Jean Poutine"
sulli
RTFJ.
But this is a godsend for what's called "desktop" search right now. If it really works as advertised, that is, which I really doubt.
However, if Intel delivers the promised 10x boost in performance in the next 3 years (which I really doubt, too), who knows, we might see this in a centralized search engine, too.
I've worked for a company making a system that could easily answer a question like that. It really isn't hard to do. If you want to know how much of this is "black magic"/AI and how much is statistics, compare the results of the following two queries:
If the system really understand the semantics of the indexed documents, the two result sets should be very different, and both should have a fair number of relevant documents.
If the system is just based on clever use of statistis, the two result sets will include a lot of the same documents, and the result set for the second query will probably have very few relevant documents.
Acts@core.mailboks.com Acrux@core.mailboks.com Adam@core.mailboks.com Adar@core.mailboks.com Ada@core.mailboks.com
Oh Great Oracle and all knowing interface, answer me this.... "What is the meaning of the universe and everything!"
Will I get the right answer?
i have a feeling Google is getting out of date....??
But it is a very nice story nonetheless.
Personally. I would build my AI as a clustered rack of servers, for processing, raid backups, etc. Fully self-contained.
However, it would not be plugged into the internet. It would only learn through a cd-rom. an non-burnable sole cd-rom drive. So information is only 1 way.
I wouldn't want it to be able to spider the web. Learn how to make itself into a virus and spread itself to every PC on the planet.
Then again, I would also have it be a self-healing self-administrating Linux system. With remote viewing abiltiy(so we can keep an eye on it) and backup/restore... Almost like a groundhog's day, once it has a certain level of 'intelligence' it decides what and how things are placed and installed. kernel up. When it kernel panics or crashes, we take it 1 day back in history. show it the fuck up, and let it try again.
Talk about natural evolution.
Can it recognize duplicate stories and not allow the primates to submit them again and again?
"The ideal search engine is like the mind of God". Here we come, a hundred million "semantics aware" PCs and we get something resembling someone's mind.
is the article suggesting that Google is going anywhere? Or that they have finished innovating? I doubt it.
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