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Evolving Trends

July 10, 2006

Is Google a Monopoly?

(this post was last updated on Jan 11, ‘07)Given the growing feeling that Google holds too much power over our future without any proof that they can handle such responsibility wisely, and with plenty of proof to the opposite1, it is clear why people find themselves breathing a sigh of relief at the prospect of a new Web order, where Google will not be as powerful and dominant.

In the software industry, economies of scale do not derive from production capacity but rather from the size of the installed user base, as software is made of electrical pulses that can be downloaded by the users, at a relatively small cost to the producer (or virtually no cost if using the P2P model of the Web.) This means that the size of the installed user base replaces production capacity in classical economic terms.

Just as Microsoft used its economies of scale (i.e. its installed user base) as part of a copy-and-co-opt strategy to dominate the desktop, Google has shifted from a strategy of genuine innovation, which is expensive and risky, to a lower-risk copy-and-co-opt strategy in which it uses its economies of scale (i.e. its installed user base) to eliminate competition and dominate the Web.

The combination of the ability to copy and co-opt innovations across broad segments of the market together with existing and growing economies of scale is what makes Google a monopoly.

Consider the following example: DabbleDB (among other companies) beat Google to market with their online, collaborative spreadsheet application, but Google acquired their competitor and produced a similar (yet inferior) product that is now threatening to kill DabbleDB’s chances for growth.

One way to think of what’s happening is in terms of the first law of thermodynamics (aka conservation of energy): if Google grows then many smaller companies will die. And as Google grows, many smaller companies are dying.

It is not any better or worse than it used to be under the Microsoft monopoly for companies that have to compete with Google . But it’s much worse for us the people because what is at stake now is much bigger. It’s no longer about our PCs and LANs, it’s about the future of the entire Web.

You could argue that the patent system protects smaller companies from having their products and innovations copied and co-opted by bigger competitors like Google. However, during the Microsoft dominated era, very few companies succeeded in suing them for patent infringement. I happen to know of one former PC software company and their ex CEO who succeeded in suing Microsoft for $120M. But that’s a rare exception to a common rule: the one with the deeper pockets always has the advantage in court (they can drag the lawsuit for years and make it too costly for others to sue them.)

Therefore, given that Google is perceived as a growing monopoly that many see as having acquired too much power, too fast, without the wisdom to use that power responsibly, I’m not too surprised that many people have welcomed the Wikipedia 3.0 vision.


1. What leaps to mind as far as Google’s lack of wisdom is how they had sold the world on their “Do No Evil” mantra only to “Do Evil” when it came to oppressing the already-oppressed (see: Google Chinese censorship.)

Related

  1. Wikipedia 3.0: The End of Google?
  2. P2P 3.0: The People’s Google
  3. Google 2.71828: Analysis of Google’s Web 2.0 Strategy

Posted by Marc Fawzi

Tags:

Web 2.0, Google, Adam Smith, Monopoly, Trends, imperialism, Anti-Americanism, economies of scale, innovation, Startup, Google Writely, Google spreadsheets, DabbleDB, Google Base, Web 3.0

8 Comments »

  1. Google is large and influential. That doesn’t make it a monopoly.They have 39% market share in Search in the US – http://searchenginewatch.com/reports/article.php/3099931 – a lot more than their closest competitor, but it’s wrong to describe them as a monopoly. A monopoly has a legal entitlement to be the only provider of a product or service. More loosely, it can be used to describe a company with such dominance in the market that it makes no sense to try to compete with them. Neither apply to Google. I think your correspondents are simply reacting against the biggest player because they are the biggest, the same way people knock Microsoft, Symantec, Adobe, etc.

    Certainly, Yahoo!, MSN and Ask Jeeves, etc. aren’t ready to throw in the towel yet. Arguably, if they were struggling, and I don’t know if they are, DabbleDB would need to differentiate a little more against Google to make their model work as a business. I am not sure they need to.

    One last point, there isn’t a finite number of people looking for spreadsheets, etc., online. It’s a growing market with enormous untapped potential. The winners will be those best able to overcome the serious objections people have towards online apps – security & stability. Spreadsheets and databases are business apps – it will not be good enough to throw up something that is marked beta and sometimes works and might be secure. I think people dealing with business data *want* to pay for such products, because it guarantees them levels of service and the likelihood that the company will still be around in a year.

    Comment by Ian — July 11, 2006 @ 4:52 am

  2. I don’t think any company can compete against Google, especially not small companies. If MS and Interactive Corp. are having to struggle against Google then how can any small company compete against them? They have economies of scale that cannot be undone so easily, except through P2P subversion of the central search model (See my Web 3.0 article), which is going to happen on its own (I don’t need to advocate it.)Having said that I did specify ways to compete with Google in SaaS in the post titled Google 2.71828: Analysis of Google’s Web 2.0 Strategy

    But in gerenal, it’s getting tough out there because of Google’s economies of scale and their ability/willingess to copy-and-co-opt innovations across a broad segment of the market.

    Ian wrote:

    “A monopoly has a legal entitlement to be the only provider of a product or service.”

    Response:
    The definition of Monopoly in the US does not equate to state run companies or any such concept from the EU domain. It simply equates to economies of scale and ability to copy and co-opt innovations in a broad sector of the market. Monopolies that exist in market niches are a natural result of free markets but ones that exist in broad segments are problematic to free markets.

    Marc

    Comment by evolvingtrends — July 11, 2006 @ 5:21 am

  3. Don’t forget that Google prevents AdSense publishers from using other context-based advertising services on the same pages that have AdSense ads.Comment by drew — July 12, 2006 @ 12:23 am
  4. That’s a sure sign that they’re a monopoly. Just like MS used to force PC makers to do the same.Marc

    Comment by evolvingtrends — July 12, 2006 @ 5:05 am

  5. […] impact it has over a worldwide, super-connected tool like the Internet. An article by Marc Fawzi on Evolving Trends expressed this effectively […]Pingback by What Evil Lurks in the Heart of Google | Phil Butler Unplugged — November 6, 2007 @ 11:46 pm
  6. […] with existing and growing economies of scale is what makes Google a monopoly,” states Evolving Trends. As Google grows, many smaller companies will die. In order to set up its monopoly, Google is used […]Pingback by Google: pro’s and con’s « E-culture & communication: open your mind — November 14, 2007 @ 6:42 am
  7. Contrary to what the Google fan club and the Google propoganda machine would have you believe, Here are some real facts:- People do have a choice with operating systems. They can buy a MAC or use Linux.

    – Google has a terrible tack record of abusing its power:
    – click fraud lawsuit where they used a grubby lawyer and tricks to pay almost nothing.
    – they pass on a very small share to adsense publishers and make them sign a confidentiality agreement.
    – They tried to prevent publishers from showing other ads.

    – Google Adsense is responsible for the majority of spam on the Internet.

    – Google has a PR machine which includes Matt Cutts and others, who suppress criticism and even make personal attacks on people who are critical of them. They are also constantly releasing a barrage press releases with gimmicks to improve their image with the public.

    Wake up people. Excessive power leads to abuse.

    Comment by Pete — January 26, 2008 @ 1:25 pm

  8. I think this is a really important discussion which has been started here, thank you Marc.
    I got suspicious today when I heard about MS wanting to take over Yahoo! – or will it be Google…Anyway, this is really crucial stuff here, it’s the much praised freedom of the information age and hence the real hope for a truly open world which is at stake here, I hope there’s some degree of acknowledgment on this.

    So to feed the discussion more,

    – what can we do as users?
    – are there alternative independent search engines out there?
    – should we think of starting new strategies in information retrieval?
    – what ideas are around?

    Is there a good active community somewhere discussing these issues? would be interested in participating…

    thank you
    fabio

    Comment by Fabio — February 4, 2008 @ 11:42 am

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Evolving Trends

July 11, 2006

P2P 3.0: The People’s Google

/*

This is a more extensive version of the Web 3.0 article with extra sections about the implications of Web 3.0 to Google.

See this follow up article  for the more disruptive ‘decentralized kowledgebase’ version of the model discussed in article.

Also see this non-Web3.0 version: P2P to Destroy Google, Yahoo, eBay et al 

Web 3.0 Developers:

Feb 5, ‘07: The following reference should provide some context regarding the use of rule-based inference engines and ontologies in implementing the Semantic Web + AI vision (aka Web 3.0) but there are better, simpler ways of doing it. 

  1. Description Logic Programs: Combining Logic Programs with Description Logic

*/

In Web 3.0 (aka Semantic Web) P2P Inference Engines running on millions of users’ PCs and working with standardized domain-specific ontologies (created by Wikipedia, Ontoworld, other organizations or individuals) using Semantic Web tools, including Semantic MediaWiki, will produce an infomration infrastructure far more powerful than Google (or any current search engine.)

The availability of standardized ontologies that are being created by people, organizations, swarms, smart mobs, e-societies, etc, and the near-future availability of P2P Semantic Web Inference Engines that work with those ontologies means that we will be able to build an intelligent, decentralized, “P2P” version of Google.

Thus, the emergence of P2P Inference Engines and domain-specific ontologies in Web 3.0 (aka Semantic Web) will present a major threat to the central “search” engine model.

Basic Web 3.0 Concepts

Knowledge domains

A knowledge domain is something like Physics, Chemistry, Biology, Politics, the Web, Sociology, Psychology, History, etc. There can be many sub-domains under each domain each having their own sub-domains and so on.

Information vs Knowledge

To a machine, knowledge is comprehended information (aka new information produced through the application of deductive reasoning to exiting information). To a machine, information is only data, until it is processed and comprehended.

Ontologies

For each domain of human knowledge, an ontology must be constructed, partly by hand [or rather by brain] and partly with the aid of automation tools.

Ontologies are not knowledge nor are they information. They are meta-information. In other words, ontologies are information about information. In the context of the Semantic Web, they encode, using an ontology language, the relationships between the various terms within the information. Those relationships, which may be thought of as the axioms (basic assumptions), together with the rules governing the inference process, both enable as well as constrain the interpretation (and well-formed use) of those terms by the Info Agents to reason new conclusions based on existing information, i.e. to think. In other words, theorems (formal deductive propositions that are provable based on the axioms and the rules of inference) may be generated by the software, thus allowing formal deductive reasoning at the machine level. And given that an ontology, as described here, is a statement of Logic Theory, two or more independent Info Agents processing the same domain-specific ontology will be able to collaborate and deduce an answer to a query, without being driven by the same software.

Inference Engines

In the context of Web 3.0, Inference engines will be combining the latest innovations from the artificial intelligence (AI) field together with domain-specific ontologies (created as formal or informal ontologies by, say, Wikipedia, as well as others), domain inference rules, and query structures to enable deductive reasoning on the machine level.

Info Agents

Info Agents are instances of an Inference Engine, each working with a domain-specific ontology. Two or more agents working with a shared ontology may collaborate to deduce answers to questions. Such collaborating agents may be based on differently designed Inference Engines and they would still be able to collaborate.

Proofs and Answers

The interesting thing about Info Agents that I did not clarify in the original post is that they will be capable of not only deducing answers from existing information (i.e. generating new information [and gaining knowledge in the process, for those agents with a learning function]) but they will also be able to formally test propositions (represented in some query logic) that are made directly or implied by the user. For example, instead of the example I gave previously (in the Wikipedia 3.0 article) where the user asks “Where is the nearest restaurant that serves Italian cuisine” and the machine deduces that a pizza restaurant serves Italian cuisine, the user may ask “Is the moon blue?” or say that the “moon is blue” to get a true or false answer from the machine. In this case, a simple Info Agent may answer with “No” but a more sophisticated one may say “the moon is not blue but some humans are fond of saying ‘once in a blue moon’ which seems illogical to me.”

This test-of-truth feature assumes the use of an ontology language (as a formal logic system) and an ontology where all propositions (or formal statements) that can be made can be computed (i.e. proved true or false) and were all such computations are decidable in finite time. The language may be OWL-DL or any language that, together with the ontology in question, satisfy the completeness and decidability conditions.

P2P 3.0 vs Google

If you think of how many processes currently run on all the computers and devices connected to the Internet then that should give you an idea of how many Info Agents can be running at once (as of today), all reasoning collaboratively across the different domains of human knowledge, processing and reasoning about heaps of information, deducing answers and deciding truthfulness or falsehood of user-stated or system-generated propositions.

Web 3.0 will bring with it a shift from centralized search engines to P2P Semantic Web Inference Engines, which will collectively have vastly more deductive power, in both quality and quantity, than Google can ever have (included in this exclusion is any future AI-enabled version of Google, as it will not be able to keep up with the distributed P2P AI matrix that will be enabled by millions of users running free P2P Semantic Web Inference Engine software on their home PCs.)

Thus, P2P Semantic Web Inference Engines will pose a huge and escalating threat to Google and other search engines and will expectedly do to them what P2P file sharing and BitTorrent did to FTP (central-server file transfer) and centralized file hosting in general (e.g. Amazon’s S3 use of BitTorrent.)

In other words, the coming of P2P Semantic Web Inference Engines, as an integral part of the still-emerging Web 3.0, will threaten to wipe out Google and other existing search engines. It’s hard to imagine how any one company could compete with 2 billion Web users (and counting), all of whom are potential users of the disruptive P2P model described here.

“The Future Has Arrived But It’s Not Evenly Distributed”

Currently, Semantic Web (aka Web 3.0) researchers are working out the technology and human resource issues and folks like Tim Berners-Lee, the Noble prize recipient and father of the Web, are battling critics and enlightening minds about the coming human-machine revolution.

The Semantic Web (aka Web 3.0) has already arrived, and Inference Engines are working with prototypical ontologies, but this effort is a massive one, which is why I was suggesting that its most likely enabler will be a social, collaborative movement such as Wikipedia, which has the human resources (in the form of the thousands of knowledgeable volunteers) to help create the ontologies (most likely as informal ontologies based on semantic annotations) that, when combined with inference rules for each domain of knowledge and the query structures for the particular schema, enable deductive reasoning at the machine level.

Addendum

On AI and Natural Language Processing

I believe that the first generation of AI that will be used by Web 3.0 (aka Semantic Web) will be based on relatively simple inference engines (employing both algorithmic and heuristic approaches) that will not attempt to perform natural language processing. However, they will still have the formal deductive reasoning capabilities described earlier in this article.

Related

  1. Wikipedia 3.0: The End of Google?
  2. Intelligence (Not Content) is King in Web 3.0
  3. Get Your DBin
  4. All About Web 3.0

Posted by Marc Fawzi

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Evolving Trends

June 11, 2006

P2P Semantic Web Engines

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    June 30, 2006

    Web 3.0: Basic Concepts

    /*(this post was last updated at 1:20pm EST, July 19, ‘06)

    You may also wish to see Wikipedia 3.0: The End of Google? (The original ‘Web 3.0/Semantic Web’ article) and P2P 3.0: The People’s Google (a more extensive version of this article showing the implication of P2P Semantic Web Engines to Google.)

    Web 3.0 Developers:

    Feb 5, ‘07: The following reference should provide some context regarding the use of rule-based inference engines and ontologies in implementing the Semantic Web + AI vision (aka Web 3.0) but there are better, simpler ways of doing it. 

    1. Description Logic Programs: Combining Logic Programs with Description Logic

    */

    Basic Web 3.0 Concepts

    Knowledge domains

    A knowledge domain is something like Physics, Chemistry, Biology, Politics, the Web, Sociology, Psychology, History, etc. There can be many sub-domains under each domain each having their own sub-domains and so on.

    Information vs Knowledge

    To a machine, knowledge is comprehended information (aka new information produced through the application of deductive reasoning to exiting information). To a machine, information is only data, until it is processed and comprehended.

    Ontologies

    For each domain of human knowledge, an ontology must be constructed, partly by hand [or rather by brain] and partly with the aid of automation tools.

    Ontologies are not knowledge nor are they information. They are meta-information. In other words, ontologies are information about information. In the context of the Semantic Web, they encode, using an ontology language, the relationships between the various terms within the information. Those relationships, which may be thought of as the axioms (basic assumptions), together with the rules governing the inference process, both enable as well as constrain the interpretation (and well-formed use) of those terms by the Info Agents to reason new conclusions based on existing information, i.e. to think. In other words, theorems (formal deductive propositions that are provable based on the axioms and the rules of inference) may be generated by the software, thus allowing formal deductive reasoning at the machine level. And given that an ontology, as described here, is a statement of Logic Theory, two or more independent Info Agents processing the same domain-specific ontology will be able to collaborate and deduce an answer to a query, without being driven by the same software.

    Inference Engines

    In the context of Web 3.0, Inference engines will be combining the latest innovations from the artificial intelligence (AI) field together with domain-specific ontologies (created as formal or informal ontologies by, say, Wikipedia, as well as others), domain inference rules, and query structures to enable deductive reasoning on the machine level.

    Info Agents

    Info Agents are instances of an Inference Engine, each working with a domain-specific ontology. Two or more agents working with a shared ontology may collaborate to deduce answers to questions. Such collaborating agents may be based on differently designed Inference Engines and they would still be able to collaborate.

    Proofs and Answers

    The interesting thing about Info Agents that I did not clarify in the original post is that they will be capable of not only deducing answers from existing information (i.e. generating new information [and gaining knowledge in the process, for those agents with a learning function]) but they will also be able to formally test propositions (represented in some query logic) that are made directly or implied by the user. For example, instead of the example I gave previously (in the Wikipedia 3.0 article) where the user asks “Where is the nearest restaurant that serves Italian cuisine” and the machine deduces that a pizza restaurant serves Italian cuisine, the user may ask “Is the moon blue?” or say that the “moon is blue” to get a true or false answer from the machine. In this case, a simple Info Agent may answer with “No” but a more sophisticated one may say “the moon is not blue but some humans are fond of saying ‘once in a blue moon’ which seems illogical to me.”

    This test-of-truth feature assumes the use of an ontology language (as a formal logic system) and an ontology where all propositions (or formal statements) that can be made can be computed (i.e. proved true or false) and were all such computations are decidable in finite time. The language may be OWL-DL or any language that, together with the ontology in question, satisfy the completeness and decidability conditions.

    “The Future Has Arrived But It’s Not Evenly Distributed”

    Currently, Semantic Web (aka Web 3.0) researchers are working out the technology and human resource issues and folks like Tim Berners-Lee, the Noble prize recipient and father of the Web, are battling critics and enlightening minds about the coming human-machine revolution.

    The Semantic Web (aka Web 3.0) has already arrived, and Inference Engines are working with prototypical ontologies, but this effort is a massive one, which is why I was suggesting that its most likely enabler will be a social, collaborative movement such as Wikipedia, which has the human resources (in the form of the thousands of knowledgeable volunteers) to help create the ontologies (most likely as informal ontologies based on semantic annotations) that, when combined with inference rules for each domain of knowledge and the query structures for the particular schema, enable deductive reasoning at the machine level.

    Addendum

    On AI and Natural Language Processing

    I believe that the first generation of artficial intelligence (AI) that will be used by Web 3.0 (aka Semantic Web) will be based on relatively simple inference engines (employing both algorithmic and heuristic approaches) that will not attempt to perform natural language processing. However, they will still have the formal deductive reasoning capabilities described earlier in this article.

    Related

    1. Wikipedia 3.0: The End of Google?
    2. P2P 3.0: The People’s Google
    3. All About Web 3.0
    4. Semantic MediaWiki
    5. Get Your DBin

    Posted by Marc Fawzi

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    Evolving Trends

    July 9, 2006

    Open Source Your Mind

    Any idea that you come up with that can bring a lot of power to someone and is realistic enough to attempt will inevitably get built by someone.It doesn’t matter that you thought of it first. So it’s better to put your ideas out there in the open, be them good ideas like Wikipedia 3.0, P2P 3.0 (The People’s Google) and Google GoodSense or “potentially” concern-causing ones like the Tagging People in the Real World and the e-Society ideas.In today’s world, if anyone can think of a powerful idea that is realistic enough to attempt then chances are someone is already working on it or someone will be working on it within months.

    Therefore, it is wise to get both good and potentially concern-causing ideas out there and let people be aware of them so that the good ones like the vision for Wikipedia 3.0 and the debate about the ‘Unwisdom of Crowds‘ can be of benefit to all and so that potentially concern-causing ones like the Tagging People in the Real World and the e-Society ideas can be debated in the open.

    It is in a way similar to the one aspect of the patent system. If someone comes up with the cure to cancer or with an important new technology then we, as a society, would want them to describe how it’s made or how it works so we can be sure we have access to it. However, given the availability of blogs and the connectivity we have today, wise innovators, including those in the open source movement, are putting their deas out there in the open so that society as a whole may learn about them, debate them, and decide whether to embrace them, fight them or do something in between (moderate their effect.)

    For some, it can be a lot of fun, especially the unpredictability element.

    So open source your blue sky vision and let the world here about it.

    And for the potentially concern-causing ideas, it’s better to bring them out in the open than to work on them (or risk others working on them) in the dark.

    In other words, open source your mind.

    Posted by Marc Fawzi

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    Evolving Trends

    July 29, 2006

    Search By Meaning

    I’ve been working on a pretty detailed technical scheme for a “search by meaning” search engine (as opposed to [dumb] Google-like search by keyword) and I have to say that in conquering the workability challenge in my limited scope I can see the huge problem facing Google and other Web search engines in transitioning to a “search by meaning” model.

    However, I also do see the solution!

    Related

    1. Wikipedia 3.0: The End of Google?
    2. P2P 3.0: The People’s Google
    3. Intelligence (Not Content) is King in Web 3.0
    4. Web 3.0 Blog Application
    5. Towards Intelligent Findability
    6. All About Web 3.0

    Beats

    42. Grey Cell Green

    Posted by Marc Fawzi

    Tags:

    Semantic Web, Web strandards, Trends, OWL, innovation, Startup, Evolution, Google, GData, inference, inference engine, AI, ontology, Semanticweb, Web 2.0, Web 2.0, Web 3.0, Web 3.0, Google Base, artificial intelligence, AI, Wikipedia, Wikipedia 3.0, collective consciousness, Ontoworld, Wikipedia AI, Info Agent, Semantic MediaWiki, DBin, P2P 3.0, P2P AI, AI Matrix, P2P Semantic Web inference Engine, Global Brain, semantic blog, intelligent findability, search by meaning

    5 Comments »

    1. context is a kind of meaning, innit?

      Comment by qxcvz — July 30, 2006 @ 3:24 am

    2. You’re one piece short of Lego Land.

      I have to make the trek down to San Diego and see what it’s all about.

      How do you like that for context!? 🙂

      Yesterday I got burnt real bad at Crane beach in Ipswich (not to be confused with Cisco’s IP Switch.) The water was freezing. Anyway, on the way there I was told about the one time when the kids (my nieces) asked their dad (who is a Cisco engineer) why Ipswich is called Ipswich. He said he didn’t know. They said “just make up a reason!!!!!!” (since they can’t take “I don’t know” for an answer) So he said they initially wanted to call it PI (pie) but decided it to switch the letters so it became IPSWICH. The kids loved that answer and kept asking him whenever they had their friends on a beach trip to explain why Ipswich is called Ipswich. I don’t get the humor. My logic circuits are not that sensitive. Somehow they see the illogic of it and they think it’s hilarious.

      Engineers and scientists tend to approach the problem through the most complex path possible because that’s dictated by the context of their thinking, but genetic algorithms could do a better job at that, yet that’s absolutely not what I’m hinting is the answer.

      The answer is a lot more simple (but the way simple answers are derived is often thru deep thought that abstracts/hides all the complexity)

      I’ll stop one piece short cuz that will get people to take a shot at it and thereby create more discussion around the subject, in general, which will inevitably get more people to coalesce around the Web 3.0 idea.

      [badly sun burnt face] + ] … It’s time to experiment with a digi cam … i.e. towards a photo + audio + web 3.0 blog!

      An 8-mega pixel camera phone will do just fine! (see my post on tagging people in the real world.. it is another very simple idea but I like this one much much better.)

      Marc

      p.s. my neurons are still in perfectly good order but I can’t wear my socks!!!

      Comment by evolvingtrends — July 30, 2006 @ 10:19 am

    3. Hey there, Marc.
      Have talked to people about semantic web a bit more now, and will get my thoughts together on the subject before too long. The big issue, basically, is buy-in from the gazillions of content producers we have now. My impression is the big business will lead on semantic web, because it’s more useful to them right now, rather than you or I as ‘opinion/journalist’ types.

      Comment by Ian — August 7, 2006 @ 5:06 pm

    4. Luckily, I’m not an opinion journalist although I could easily pass for one.

      You’ll see a lot of ‘doing’ from us now that we’re talking less 🙂

      BTW, just started as Chief Architect with a VC funded Silicon Valley startup so that’s keeping me busy, but I’m recruiting developers and orchestrating a P2P 3.0 / Web 3.0 / Semantic Web (AI-enabled) open source project consistent with the vision we’ev outlined. 

      :] … dzzt.

      Marc

      Comment by evolvingtrends — August 7, 2006 @ 5:10 pm

    5. Congratulations on the job, Marc. I know you’re a big thinker and I’m delighted to hear about that.

      Hope we’ll still be able to do a little “fencing” around this subject!

      Comment by Ian — August 7, 2006 @ 7:01 pm

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    July 20, 2006

    Google dont like Web 3.0 [sic]

    (this post was last updated at 9:50am EST, July 24, ‘06)

    Why am I not surprised?

    Google exec challenges Berners-Lee

    The idea is that the Semantic Web will allow people to run AI-enabled P2P Search Engines that will collectively be more powerful than Google can ever be, which will relegate Google to just another source of information, especially as Wikipedia [not Google] is positioned to lead the creation of domain-specific ontologies, which are the foundation for machine-reasoning [about information] in the Semantic Web.

    Additionally, we could see content producers (including bloggers) creating informal ontologies on top of the information they produce using a standard language like RDF. This would have the same effect as far as P2P AI Search Engines and Google’s anticipated slide into the commodity layer (unless of course they develop something like GWorld)

    In summary, any attempt to arrive at widely adopted Semantic Web standards would significantly lower the value of Google’s investment in the current non-semantic Web by commoditizing “findability” and allowing for intelligent info agents to be built that could collaborate with each other to find answers more effectively than the current version of Google, using “search by meaning” as opposed to “search by keyword”, as well as more cost-efficiently than any future AI-enabled version of Google, using disruptive P2P AI technology.

    For more information, see the articles below.

    Related

    1. Wikipedia 3.0: The End of Google?
    2. Wikipedia 3.0: El fin de Google (traducción)
    3. All About Web 3.0
    4. Web 3.0: Basic Concepts
    5. P2P 3.0: The People’s Google
    6. Intelligence (Not Content) is King in Web 3.0
    7. Web 3.0 Blog Application
    8. Towards Intelligent Findability
    9. Why Net Neutrality is Good for Web 3.0
    10. Semantic MediaWiki
    11. Get Your DBin

    Somewhat Related

    1. Unwisdom of Crowds
    2. Reality as a Service (RaaS): The Case for GWorld
    3. Google 2.71828: Analysis of Google’s Web 2.0 Strategy
    4. Is Google a Monopoly?
    5. Self-Aware e-Society

    Beats

    1. In the Hearts of the Wildmen

    Posted by Marc Fawzi

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    Semantic Web, Web strandards, Trends, OWL, innovation, Startup, Evolution, Google, GData, inference, inference engine, AI, ontology, Semanticweb, Web 2.0, Web 2.0, Web 3.0, Web 3.0, Google Base, artificial intelligence, AI, Wikipedia, Wikipedia 3.0, collective consciousness, Ontoworld, Wikipedia AI, Info Agent, Semantic MediaWiki, DBin, P2P 3.0, P2P AI, AI Matrix, P2P Semantic Web inference Engine, semantic blog, intelligent findability, RDF

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    Evolving Trends

    July 19, 2006

    Towards Intelligent Findability

    (This post was last updated at 12:45pm EST, July 22, 06)

    By Eric Noam Rodriguez (versión original en español CMS Semántico)

    Editing and Addendum by Marc Fawzi

    A lot of buzz about Web 3.0 and Wikipedia 3.0 has been generagted lately by Marc Fawzi through this blog, so I’ve decided that for my first post here I’d like to dive into this idea and take a look at how to build a Semantic Content Management System (CMS). I know this blog has had a more of a visionary, psychological and sociological theme (i.e., the vision for the future and the Web’s effect on society, human relationships and the individual himself), but I’d like to show the feasibility of this vision by providing some technical details.

    Objective

     

    We want a CMS capable of building a knowledge base (that is a set of domain-specific ontologies) with formal deductive reasoning capabilities.

    Requirements

     

    1. A semantic CMS framework.
    2. An ontology API.
    3. An inference engine.
    4. A framework for building info-agents.

    HOW-TO

     

    The general idea would be something like this:

    1. Users use a semantic CMS like Semantic MediaWiki to enter information as well as semantic annotations (to establish semantic links between concepts in the given domain on top of the content) This typically produces an informal ontology on top of the information, which, when combined with domain inference rules and the query structures (for the particular schema) that are implemented in an independent info agent or built into the CMS, would give us a Domain Knowledge Database. (Alternatively, we can have users enter information into a non-semantic CMS to create content based on a given doctype or content schema and then front-end it with an info agent that works with a formal ontology of the given domain, but we would then need to perform natural language processing, including using statistical semantic models, since we would lose the certainty that would normally be provided by the semantic annotations that, in a Semantic CMS, would break down the natural language in the information to a definite semantic structure.)
    2. Another set of info agents adds to our knowledge base inferencing-based querying services for information on the Web or other domain-specific databases. User entered information plus information obtained from the web makes up our Global Knowledge Database.
    3. We provide a Web-based interface for querying the inference engine.

    Each doctype or schema (depending on the CMS of your choice) will have a more or less direct correspondence with our ontologies (i.e. one schema or doctype maps with one ontology). The sum of all the content of a particular schema makes up a knowledge-domain which when transformed into a semantic language like (RDF or more specifically OWL) and combined with the domain inference rules and the query structures (for the particular schema) constitute our knowledge database. The choice of CMS is not relevant as long as you can query its contents while being able to define schemas. What is important is the need for an API to access the ontology. Luckily projects like JENA fills this void perfectly providing both an RDF and an OWL API for Java.

    In addition, we may want an agent to add or complete our knowledge base using available Web Services (WS). I’ll assume you’re familiarized with WS so I won’t go into details.

     

    Now, the inference engine would seem like a very hard part. It is. But not for lack of existing technology: the W3C already have a recommendation language for querying RDF (viz. a semantic language) known as SPARQL (http://www.w3.org/TR/rdf-sparql-query/) and JENA already has a SPARQL query engine.

    The difficulty lies in the construction of ontologies which would have to be formal (i.e. consistent, complete, and thoroughly studied by experts in each knowledge-domain) in order to obtain powerful deductive capabilities (i.e. reasoning).

    Conclusion

    We already have technology powerful enough to build projects such as this: solid CMS, standards such as RDF, OWL, and SPARQL as well as a stable framework for using them such as JENA. There are also many frameworks for building info-agents but you don’t necessarily need a specialized framework, a general software framework like J2EE is good enough for the tasks described in this post.

    All we need to move forward with delivering on the Web 3.0 vision (see 1, 2, 3) is the will of the people and your imagination.

    Addendum

    In the diagram below, the domain-specific ontologies (OWL 1 … N) could be all built by Wikipedia (see Wikipedia 3.0) since they already have the largest online database of human knowledge and the domain experts among their volunteers to build the ontologies for each domain of human knowledge. One possible way is for Wikipedia will build informal ontologies using Semantic MediaWiki (as Ontoworld is doing for the Semantic Web domain of knowledge) but Wikipedia may wish to wait until they have the ability to build formal ontologies, which would enable more powerful machine-reasoning capabilities.

    [Note: The ontologies simply allow machines to reason about information. They are not information but meta-information. They have to be formally consistent and complete for best results as far as machine-based reasoning is concerned.]

    However, individuals, teams, organizations and corporations do not have to wait for Wikipedia to build the ontologies. They can start building their own domain-specific ontologies (for their own domains of knowledge) and use Google, Wikipedia, MySpace, etc as sources of information. But as stated in my latest edit to Eric’s post, we would have to use natural language processing in that case, including statistical semantic models, as the information won’t be pre-semanticized (or semantically annotated), which makes the task more dificult (for us and for the machine …)

    What was envisioned in the Wikipedia 3.0: The End of Google? article was that since Wikipedia has the volunteer resources and the world’s largest database of human knowledge then it will be in the powerful position of being the developer and maintainer of the ontologies (including the semantic annotations/statements embedded in each page) which will become the foundation for intelligence (and “Intelligent Findability”) in Web 3.0.

    This vision is also compatible with the vision for P2P AI (or P2P 3.0), where people will run P2P inference engines on their PCs that communicate and collaborate with each other and that tap into information form Google, Wikipedia, etc, which will ultimately push Google and central search engines down to the commodity layer (eventually making them a utility business just like ISPs.)

    Diagram

    Related

    1. Wikipedia 3.0: The End of Google? June 26, 2006
    2. Wikipedia 3.0: El fin de Google (traducción) July 12, 2006
    3. Web 3.0: Basic Concepts June 30, 2006
    4. P2P 3.0: The People’s Google July 11, 2006
    5. Why Net Neutrality is Good for Web 3.0 July 15, 2006
    6. Intelligence (Not Content) is King in Web 3.0 July 17, 2006
    7. Web 3.0 Blog Application July 18, 2006
    8. Semantic MediaWiki July 12, 2006
    9. Get Your DBin July 12, 2006

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    Semantic Web, Web strandards, Trends, OWL, innovation, Startup, Google, GData, inference engine, AI, ontology, Semantic Web, Web 2.0, Web 2.0, Web 3.0, Web 3.0, Google Base, artificial intelligence, AI, Wikipedia, Wikipedia 3.0, Ontoworld, Wikipedia AI, Info Agent, Semantic MediaWiki, DBin, P2P 3.0, P2P AI, AI Matrix, P2P Semantic Web inference Engine, semantic blog, intelligent findability, JENA, SPARQL, RDF, OWL

     

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  • Evolving Trends
  • June 30, 2006

    Web 3.0: Basic Concepts

    /*

    (this post was last updated at 1:20pm EST, July 19, ‘06)

    You may also wish to see Wikipedia 3.0: The End of Google? (The original ‘Web 3.0/Semantic Web’ article) and P2P 3.0: The People’s Google (a more extensive version of this article showing the implication of P2P Semantic Web Engines to Google.)

    Web 3.0 Developers:

    Feb 5, ‘07: The following reference should provide some context regarding the use of rule-based inference engines and ontologies in implementing the Semantic Web + AI vision (aka Web 3.0) but there are better, simpler ways of doing it. 

    1. Description Logic Programs: Combining Logic Programs with Description Logic

    */

    Basic Web 3.0 Concepts

    Knowledge domains

    A knowledge domain is something like Physics, Chemistry, Biology, Politics, the Web, Sociology, Psychology, History, etc. There can be many sub-domains under each domain each having their own sub-domains and so on.

    Information vs Knowledge

    To a machine, knowledge is comprehended information (aka new information produced through the application of deductive reasoning to exiting information). To a machine, information is only data, until it is processed and comprehended.

    Ontologies

    For each domain of human knowledge, an ontology must be constructed, partly by hand [or rather by brain] and partly with the aid of automation tools.

    Ontologies are not knowledge nor are they information. They are meta-information. In other words, ontologies are information about information. In the context of the Semantic Web, they encode, using an ontology language, the relationships between the various terms within the information. Those relationships, which may be thought of as the axioms (basic assumptions), together with the rules governing the inference process, both enable as well as constrain the interpretation (and well-formed use) of those terms by the Info Agents to reason new conclusions based on existing information, i.e. to think. In other words, theorems (formal deductive propositions that are provable based on the axioms and the rules of inference) may be generated by the software, thus allowing formal deductive reasoning at the machine level. And given that an ontology, as described here, is a statement of Logic Theory, two or more independent Info Agents processing the same domain-specific ontology will be able to collaborate and deduce an answer to a query, without being driven by the same software.

    Inference Engines

    In the context of Web 3.0, Inference engines will be combining the latest innovations from the artificial intelligence (AI) field together with domain-specific ontologies (created as formal or informal ontologies by, say, Wikipedia, as well as others), domain inference rules, and query structures to enable deductive reasoning on the machine level.

    Info Agents

    Info Agents are instances of an Inference Engine, each working with a domain-specific ontology. Two or more agents working with a shared ontology may collaborate to deduce answers to questions. Such collaborating agents may be based on differently designed Inference Engines and they would still be able to collaborate.

    Proofs and Answers

    The interesting thing about Info Agents that I did not clarify in the original post is that they will be capable of not only deducing answers from existing information (i.e. generating new information [and gaining knowledge in the process, for those agents with a learning function]) but they will also be able to formally test propositions (represented in some query logic) that are made directly or implied by the user. For example, instead of the example I gave previously (in the Wikipedia 3.0 article) where the user asks “Where is the nearest restaurant that serves Italian cuisine” and the machine deduces that a pizza restaurant serves Italian cuisine, the user may ask “Is the moon blue?” or say that the “moon is blue” to get a true or false answer from the machine. In this case, a simple Info Agent may answer with “No” but a more sophisticated one may say “the moon is not blue but some humans are fond of saying ‘once in a blue moon’ which seems illogical to me.”

    This test-of-truth feature assumes the use of an ontology language (as a formal logic system) and an ontology where all propositions (or formal statements) that can be made can be computed (i.e. proved true or false) and were all such computations are decidable in finite time. The language may be OWL-DL or any language that, together with the ontology in question, satisfy the completeness and decidability conditions.

    “The Future Has Arrived But It’s Not Evenly Distributed”

    Currently, Semantic Web (aka Web 3.0) researchers are working out the technology and human resource issues and folks like Tim Berners-Lee, the Noble prize recipient and father of the Web, are battling critics and enlightening minds about the coming human-machine revolution.

    The Semantic Web (aka Web 3.0) has already arrived, and Inference Engines are working with prototypical ontologies, but this effort is a massive one, which is why I was suggesting that its most likely enabler will be a social, collaborative movement such as Wikipedia, which has the human resources (in the form of the thousands of knowledgeable volunteers) to help create the ontologies (most likely as informal ontologies based on semantic annotations) that, when combined with inference rules for each domain of knowledge and the query structures for the particular schema, enable deductive reasoning at the machine level.

    Addendum

    On AI and Natural Language Processing

    I believe that the first generation of artficial intelligence (AI) that will be used by Web 3.0 (aka Semantic Web) will be based on relatively simple inference engines (employing both algorithmic and heuristic approaches) that will not attempt to perform natural language processing. However, they will still have the formal deductive reasoning capabilities described earlier in this article.

    Related

    1. Wikipedia 3.0: The End of Google?
    2. P2P 3.0: The People’s Google
    3. All About Web 3.0
    4. Semantic MediaWiki
    5. Get Your DBin

    Posted by Marc Fawzi

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    Evolving Trends

    June 9, 2006

    Google 2.71828: Analysis of Google’s Web 2.0 Strategy

    The title of this post should read Google 2.0, but in the tradition of Google’s college campus ads I decided to use 2.71828, which is the value of e (the base of the natural system of logarithms), in place of just plain 2.0.

    So what’s all the fuss about? Google throws a bucket of AJAX (not the Colgate-Palmolive variety) on some empty Web page and people go “they must be doing something genius!” and then on and on goes the blogosphere in its analysis of Google’s latest move.

    The Problem with Google’s Spreadsheets and Writely

    First of all, for those who are not acquainted with the decades old concept of CVS (concurrent versioning system), being able to have multiple users edit the same document and enjoy all the benefits of version control is nothing new. Programmers have been relying on such systems for several decades now. And Wikipedia’s multi-user document editing and version control capabilities is just one popular example of CVS being applied it to Web documents.

    What I find unusual/weird is that fact that Google’s Spreadsheet and Writely do not follow the established CVS model where a given document is protected from being edited at the same time by more than one user.

    In CVS-like applications, like Wikipedia, many users may edit a given document but not all at the same time. Having multiple users edit a document, a program’s source file or an Excel spreadsheet at the same time (where there are many logical and semantic inter-dependencies across the document) can result in conflicting changes that create a mess. Changing an input value or formula in one cell or changing a global variable or a method in a program file (that’s called by other methods elsewhere in the program) will have effects elsewhere in the spreadsheet or program. If one person makes the changes then they’ll be able to track all places in the code that would get affected and make sure that their changes do not create bugs in the program or the spreadsheet. They would also comment it so that others understand what the change is. But if two people are making changes at the same time to the same spreadsheet or program (or document) and one person makes changes in one area that affects another area that the other person happens to be making changes to directly or indirectly, then you could see how that would lead to bugs being created due conflicting changes.

    For example, let’s say I change A from 1 to 2 in area AA which I expect to change the value of B (which is defined as A + C and has an acceptable range of 0-4) from 3 to 4 in area BB but at the same time you (the second person editing the document) change C from 2 to 3 in area CC which you expect to change the value of B from 3 to 4 in area BB, then we would end up with B = 5, which neither of us would expect and which is outside the acceptable range of B, i.e. any value for B larger than 4 would break the calculation/program, thus creating a bug. This is the most simple case. In real practice the “multiple simultaneous changes” scenarios would be much more complicated and involve more logical and semantic inter-connections. In the scenario where two or more people can edit the same spreadsheet, document or program at the same time, if one cannot see the changes that the other person is making and understand their effect on all related parts of the program, spreadsheet or document then one should not be making any changes of their own at the same time, or else they would be creating havoc, especially as the number of people editing at the same time goes beyond two people. With two people it is conceivable that a system or process can be worked out where people can synchronize their editing. With three or more people the interaction process grows in complexity so much that it would require implementing a strict synchronization process (or a more evolved model) in the editing application to coordinate the work of the simultaneous editors so that mess can be avoided.

    The CVS model, which is used by Wikipedia and other multi-user editing applications, avoids the mess that would be created by “too many chefs in the kitchen” by letting each document (or each “dish” in this case) be worked on by only one chef at a time, with each chef leaving notes for the others as to what he did, and with rollback and labeling capabilities. I find it unbelievable that a company like Google with such concentration of rocket scientists cannot see the silliness in letting “too many chefs work on the same dish” (not just in the same kitchen) without implementing the synchronization process that would be needed to avoid a certain, predictable mess.

    What people really want for multi-user document editing is what Wikipedia already offers, which is consistent with the CVS scheme that has been so successful in the context of software development teams.

    What people really want for multi-user spreadsheet editing is what DabbleDB already offers, i.e. to be able to create multiple views of the same dataset, and I would say also to be able to leverage the CVS scheme for multi-user editing where users may edit the same document but not at the same time!

    The Problem with Google’s Strategy

    In trying to understand Google’s strategy, I can think of three possible theories:

    1. The “short range chaos vs. long range order” theory. This implies that Google is striving towards more and more order, i.e. decreasing entropy, as a long range trend, just like the process of evolution in nature. This means that Google leverages evolving strategies (as in self-organization, complextity and chaos) to generate long range order. This is a plausible way to explain Google’s moves so far. Knowing what we know about the creative people they’ve hired, I’m tempted to conclude that this is what they’re doing.

    2. The “complicated but orderly” theory. Think of this as a parametric function vs. a chaotic pattern or a linear or cyclical one. This type of strategy has the property of being both confusing as well as constructive. Several bloggers have pointed to this possibility. But who are they distracting? Microsoft? Do they really think Microsoft is so naive to fall for it? I doubt it. So I don’t understand why they would prefer complicated over simple when it comes to their strategy.

    3. The “total uninhibited decline” theory. This implies chaos in both the short and the long ranges of their strategy. Few people would consider this a possibility at this time.

    It would seem that Google’s strategy falls under theory number one, i.e. they work with a large set of evolving strategies, trying to mimic nature itself. But whether or not they recognize this about themselves I have no clue.

    So what if I was to convince a bunch of kids to take the Firefox source code and ideas from the GreaseMonkey and Platypus plugins and produce a version of FireFox that automatically removes all AdSense ads from Web pages and reformats the page so that there would be no empty white areas (where the Ads are removed from) … What would that do to Google’s ad-suported business model? I think it could do a lot of damage to Google’s model as most users hate having their view of the Web cluttered with mostly useless ads.

    However, some say that Google Base is the Next Big Thing, and that’s an ad-supported model where people actually want to see the ads. In that case, it would seem that those bloggers who say Google’s strategy fits under theory number two (complicated but predictable) are correct.

    Personally, I believe that Google’s strategy is a mix of both complex as well as complicated behaviors (i.e. theories number one and two), which is a sure way to get lost in the game.

    Beating Google in Software as a Service (Saas)

    As far as I can tell, Google 2.0 (or Google 2.71828 as I like to call it) has been mostly about SaaS.

    Google is now to the SaaS industry what Microsoft has been to the desktop software industry. VCs are afraid to invest in something that Google may copy and co-opt.

    However, just like how MS had failed to beat Quicken with MS Money and how they’ve failed to beat Adobe and Macromedia (which are one now) Google will fail to beat those who know how to run circles around it. One exit strategy for such companies may be a sale to Yahoo (just kidding, but why not!)

    In general here is what SaaS companies can do to run circles around the likes of Google and Yahoo.

    1. Let Google and Yahoo spend billions to expand their share of the general consumer market. You can’t beat them there. Focus on a market niche.

    2. Provide unique value through product differentiation.

    3. Provide higher value through integration with partners’ products and services.

    4. Cater to the needs of your niche market on much more personal basis than Google or Yahoo can ever hope to accomplish.

    5. Offer vertical applications that Google and Yahoo would not be interested in offering (too small a market for them) to enhance your offering.

    Posted by Marc Fawzi

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    Tags:

    Web 2.0, Ajax, Flex 2, Web strandards, Trends, RIA, Rich Internet applications, product management, innovation, tagging, social networking, user generated content, Software as a Service (Saas), chaos theory, Startup, Evolution, Google Writely, Google Spreadsheets, Google, DabbleDB, Google Base

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