Posts Tagged ‘Collective Intelligence’

From Logic to Ontology: The limit of “The Semantic Web”



(Some post are written in English and Spanish language) 


From Logic to Ontology: The limit of “The Semantic Web” 


If you read the next posts on this blog: 

Semantic Web

The Semantic Web

What is the Semantic Web, Actually?

The Metaweb: Beyond Weblogs. From the Metaweb to the Semantic Web: A Roadmap

Semantics to the people! ontoworld

What’s next for the Internet

Web 3.0: Update

How the Wikipedia 3.0: The End of Google? article reached 2 million people in 4 days!

Google vs Web 3.0

Google dont like Web 3.0 [sic] Why am I not surprised?

Designing a better Web 3.0 search engine

From semantic Web (3.0) to the WebOS (4.0)

Search By Meaning

A Web That Thinks Like You


The long-promised “semantic” web is starting to take shape

Start-Up Aims for Database to Automate Web Searching

Metaweb: a semantic wiki startup


The Semantic Web, Collective Intelligence and Hyperdata.

Informal logic 

Logical argument

Consistency proof 

Consistency proof and completeness: Gödel’s incompleteness theorems

Computability theory (computer science): The halting problem

Gödel’s incompleteness theorems: Relationship with computability

Non-formal or Inconsistency Logic: LACAN’s LOGIC. Gödel’s incompleteness theorems,

You will realize the internal relationship between them linked from Logic to Ontology.  

I am writing from now on an article about the existence of the semantic web.  

I will prove that it does not exist at all, and that it is impossible to build from machines like computers.  

It does not depend on the software and hardware you use to build it: You cannot do that at all! 

You will notice the internal relations among them, and the connecting thread is the title of this post: “Logic to ontology.”   

I will prove that there is no such construction, which can not be done from the machines, and that does not depend on the hardware or software used.  

More precisely, the limits of the semantic web are not set by the use of machines themselves and biological systems could be used to reach this goal, but as the logic that is being used to construct it does not contemplate the concept of time, since it is purely formal logic and metonymic lacks the metaphor, and that is what Gödel’s theorems remark, the final tautology of each construction or metonymic language (mathematical), which leads to inconsistencies. 

This consistent logic is completely opposite to the logic that makes inconsistent use of time, inherent of human unconscious, but the use of time is built on the lack, not on positive things, it is based on denials and absences, and that is impossible to reflect on a machine because of the perceived lack of the required self-awareness is acquired with the absence.  

The problem is we are trying to build an intelligent system to replace our way of thinking, at least in the information search, but the special nature of human mind is the use of time which lets human beings reach a conclusion, therefore does not exist in the human mind the halting problem or stop of calculation.  

So all efforts faced toward semantic web are doomed to failure a priori if the aim is to extend our human way of thinking into machines, they lack the metaphorical speech, because only a mathematical construction, which will always be tautological and metonymic, and lacks the use of the time that is what leads to the conclusion or “stop”.  

As a demonstration of that, if you suppose it is possible to construct the semantic web, as a language with capabilities similar to human language, which has the use of time, should we face it as a theorem, we can prove it to be false with a counter example, and it is given in the particular case of the Turing machine and “the halting problem”.  

Then as the necessary and sufficient condition for the theorem is not fulfilled, we still have the necessary condition that if a language uses time, it lacks formal logic, the logic used is inconsistent and therefore has no stop problem.

This is a necessary condition for the semantic web, but it is not enough and therefore no machine, whether it is a Turing Machine, a computer or a device as random as a black body related to physics field, can deal with any language other than mathematics language hence it is implied that this language is forced to meet the halting problem, a result of Gödel theorem.   

De la lógica a la ontología: El límite de la “web semántica”  

Si lee los siguientes artículos de este blog: 


Wikipedia 3.0: El fin de Google (traducción Spanish)


Lógica Consistente y completitud: Teoremas de la incompletitud de Gödel (Spanish)

Consistencia lógica (Spanish)

Teoría de la computabilidad. Ciencia de la computación.

Teoremas de la incompletitud de Gödel y teoría de la computación: Problema de la parada 

Lógica inconsistente e incompletitud: LOGICAS LACANIANAS y Teoremas de la incompletitud de Gödel (Spanish)  

Jacques Lacan (Encyclopædia Britannica Online)

Usted puede darse cuenta de las relaciones internas entre ellos, y el hilo conductor es el título de este mismo post: “de la lógica a la ontología”.  

Probaré que no existe en absoluto tal construcción, que no se puede hacer desde las máquinas, y que no depende ni del hardware ni del software utilizado.   

Matizando la cuestión, el límite de la web semántica está dado no por las máquinas y/o sistemas biológicos que se pudieran usar, sino porque la lógica con que se intenta construir carece del uso del tiempo, ya que la lógica formal es puramente metonímica y carece de la metáfora, y eso es lo que marcan los teoremas de Gödel, la tautología final de toda construcción y /o lenguaje metonímico (matemático), que lleva a contradicciones.  

Esta lógica consistente es opuesta a la lógica inconsistente que hace uso del tiempo, propia del insconciente humano, pero el uso del tiempo está construido en base a la falta, no en torno a lo positivo sino en base a negaciones y ausencias, y eso es imposible de reflejar en una máquina porque la percepción de la falta necesita de la conciencia de sí mismo que se adquiere con la ausencia.   

El problema está en que pretendemos construir un sistema inteligente que sustituya nuestro pensamiento, al menos en las búsquedas de información, pero la particularidad de nuestro pensamiento humano es el uso del tiempo el que permite concluir, por eso no existe en la mente humana el problema de la parada o detención del cálculo, o lo que es lo mismo ausencia del momento de concluir.  

Así que todos los esfuerzos encaminados a la web semántica están destinados al fracaso a priori si lo que se pretende es prolongar nuestro pensamiento humano en las máquinas, ellas carecen de discurso metafórico, pues sólo son una construcción matemática, que siempre será tautológica y metonímica, ya que además carece del uso del tiempo que es lo que lleva al corte, la conclusión o la “parada”.  

Como demostración vale la del contraejemplo, o sea que si suponemos que es posible construir la web semántica, como un lenguaje con capacidades similares al lenguaje humano, que tiene el uso del tiempo, entonces si ese es un teorema general, con un solo contraejemplo se viene abajo, y el contraejemplo está dado en el caso particular de la máquina de Turing y el “problema de la parada”.  

Luego no se cumple la condición necesaria y suficiente del teorema, nos queda la condición necesaria que es que si un lenguaje tiene el uso del tiempo, carece de lógica formal, usa la lógica inconsistente y por lo tanto no tiene el problema de la parada”, esa es condición necesaria para la web semántica, pero no suficiente y por ello ninguna máquina, sea de Turing, computador o dispositivo aleatorio como un cuerpo negro en física, puede alcanzar el uso de un lenguaje que no sea el matemático con la paradoja de la parada, consecuencia del teorema de Gödel.

Jacques Lacan (Encyclopædia Britannica Online)

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The Semantic Web

A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities

By Tim Berners-Lee, James Hendler and Ora Lassila



The Semantic Web is not a separate Web but an extension of the current one, in which information is given well-defined meaning, better enabling computers and people to work in cooperation. The first steps in weaving the Semantic Web into the structure of the existing Web are already under way. In the near future, these developments will usher in significant new functionality as machines become much better able to process and “understand” the data that they merely display at present.The essential property of the World Wide Web is its universality. The power of a hypertext link is that “anything can link to anything.” Web technology, therefore, must not discriminate between the scribbled draft and the polished performance, between commercial and academic information, or among cultures, languages, media and so on. Information varies along many axes. One of these is the difference between information produced primarily for human consumption and that produced mainly for machines. At one end of the scale we have everything from the five-second TV commercial to poetry. At the other end we have databases, programs and sensor output. To date, the Web has developed most rapidly as a medium of documents for people rather than for data and information that can be processed automatically. The Semantic Web aims to make up for this.

Like the Internet, the Semantic Web will be as decentralized as possible. Such Web-like systems generate a lot of excitement at every level, from major corporation to individual user, and provide benefits that are hard or impossible to predict in advance. Decentralization requires compromises: the Web had to throw away the ideal of total consistency of all of its interconnections, ushering in the infamous message “Error 404: Not Found” but allowing unchecked exponential growth.

Knowledge Representation

For the semantic web to function, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. Artificial-intelligence researchers have studied such systems since long before the Web was developed. Knowledge representation, as this technology is often called, is currently in a state comparable to that of hypertext before the advent of the Web: it is clearly a good idea, and some very nice demonstrations exist, but it has not yet changed the world. It contains the seeds of important applications, but to realize its full potential it must be linked into a single global system.Traditional knowledge-representation systems typically have been centralized, requiring everyone to share exactly the same definition of common concepts such as “parent” or “vehicle.” But central control is stifling, and increasing the size and scope of such a system rapidly becomes unmanageable.

Moreover, these systems usually carefully limit the questions that can be asked so that the computer can answer reliably? or answer at all. The problem is reminiscent of G?del’s theorem from mathematics: any system that is complex enough to be useful also encompasses unanswerable questions, much like sophisticated versions of the basic paradox “This sentence is false.” To avoid such problems, traditional knowledge-representation systems generally each had their own narrow and idiosyncratic set of rules for making inferences about their data. For example, a genealogy system, acting on a database of family trees, might include the rule “a wife of an uncle is an aunt.” Even if the data could be transferred from one system to another, the rules, existing in a completely different form, usually could not.

Semantic Web researchers, in contrast, accept that paradoxes and unanswerable questions are a price that must be paid to achieve versatility. We make the language for the rules as expressive as needed to allow the Web to reason as widely as desired. This philosophy is similar to that of the conventional Web: early in the Web’s development, detractors pointed out that it could never be a well-organized library; without a central database and tree structure, one would never be sure of finding everything. They were right. But the expressive power of the system made vast amounts of information available, and search engines (which would have seemed quite impractical a decade ago) now produce remarkably complete indices of a lot of the material out there. The challenge of the Semantic Web, therefore, is to provide a language that expresses both data and rules for reasoning about the data and that allows rules from any existing knowledge-representation system to be exported onto the Web.

Adding logic to the Web?the means to use rules to make inferences, choose courses of action and answer questions?is the task before the Semantic Web community at the moment. A mixture of mathematical and engineering decisions complicate this task. The logic must be powerful enough to describe complex properties of objects but not so powerful that agents can be tricked by being asked to consider a paradox. Fortunately, a large majority of the information we want to express is along the lines of “a hex-head bolt is a type of machine bolt,” which is readily written in existing languages with a little extra vocabulary.

Two important technologies for developing the Semantic Web are already in place: eXtensible Markup Language (XML) and the Resource Description Framework (RDF). XML lets everyone create their own tags?hidden labels such as or that annotate Web pages or sections of text on a page. Scripts, or programs, can make use of these tags in sophisticated ways, but the script writer has to know what the page writer uses each tag for. In short, XML allows users to add arbitrary structure to their documents but says nothing about what the structures mean.

The Semantic Web will enable machines to COMPREHEND semantic documents and data, not human speech and writings.

Meaning is expressed by RDF, which encodes it in sets of triples, each triple being rather like the subject, verb and object of an elementary sentence. These triples can be written using XML tags. In RDF, a document makes assertions that particular things (people, Web pages or whatever) have properties (such as “is a sister of,” “is the author of”) with certain values (another person, another Web page). This structure turns out to be a natural way to describe the vast majority of the data processed by machines. Subject and object are each identified by a Universal Resource Identifier (URI), just as used in a link on a Web page. (URLs, Uniform Resource Locators, are the most common type of URI.) The verbs are also identified by URIs, which enables anyone to define a new concept, a new verb, just by defining a URI for it somewhere on the Web.Human language thrives when using the same term to mean somewhat different things, but automation does not. Imagine that I hire a clown messenger service to deliver balloons to my customers on their birthdays. Unfortunately, the service transfers the addresses from my database to its database, not knowing that the “addresses” in mine are where bills are sent and that many of them are post office boxes. My hired clowns end up entertaining a number of postal workers?not necessarily a bad thing but certainly not the intended effect. Using a different URI for each specific concept solves that problem. An address that is a mailing address can be distinguished from one that is a street address, and both can be distinguished from an address that is a speech.

The triples of RDF form webs of information about related things. Because RDF uses URIs to encode this information in a document, the URIs ensure that concepts are not just words in a document but are tied to a unique definition that everyone can find on the Web. For example, imagine that we have access to a variety of databases with information about people, including their addresses. If we want to find people living in a specific zip code, we need to know which fields in each database represent names and which represent zip codes. RDF can specify that “(field 5 in database A) (is a field of type) (zip code),” using URIs rather than phrases for each term.


Of course, this is not the end of the story, because two databases may use different identifiers for what is in fact the same concept, such as zip code. A program that wants to compare or combine information across the two databases has to know that these two terms are being used to mean the same thing. Ideally, the program must have a way to discover such common meanings for whatever databases it encounters.

A solution to this problem is provided by the third basic component of the Semantic Web, collections of information called ontologies. In philosophy, an ontology is a theory about the nature of existence, of what types of things exist; ontology as a discipline studies such theories. Artificial-intelligence and Web researchers have co-opted the term for their own jargon, and for them an ontology is a document or file that formally defines the relations among terms. The most typical kind of ontology for the Web has a taxonomy and a set of inference rules.

The taxonomy defines classes of objects and relations among them. For example, an address may be defined as a type of location, and city codes may be defined to apply only to locations, and so on. Classes, subclasses and relations among entities are a very powerful tool for Web use. We can express a large number of relations among entities by assigning properties to classes and allowing subclasses to inherit such properties. If city codes must be of type city and cities generally have Web sites, we can discuss the Web site associated with a city code even if no database links a city code directly to a Web site.

Inference rules in ontologies supply further power. An ontology may express the rule “If a city code is associated with a state code, and an address uses that city code, then that address has the associated state code.” A program could then readily deduce, for instance, that a Cornell University address, being in Ithaca, must be in New York State, which is in the U.S., and therefore should be formatted to U.S. standards. The computer doesn’t truly “understand” any of this information, but it can now manipulate the terms much more effectively in ways that are useful and meaningful to the human user.

With ontology pages on the Web, solutions to terminology (and other) problems begin to emerge. The meaning of terms or XML codes used on a Web page can be defined by pointers from the page to an ontology. Of course, the same problems as before now arise if I point to an ontology that defines addresses as containing a zip code and you point to one that uses postal code. This kind of confusion can be resolved if ontologies (or other Web services) provide equivalence relations: one or both of our ontologies may contain the information that my zip code is equivalent to your postal code.

Our scheme for sending in the clowns to entertain my customers is partially solved when the two databases point to different definitions of address. The program, using distinct URIs for different concepts of address, will not confuse them and in fact will need to discover that the concepts are related at all. The program could then use a service that takes a list of postal addresses (defined in the first ontology) and converts it into a list of physical addresses (the second ontology) by recognizing and removing post office boxes and other unsuitable addresses. The structure and semantics provided by ontologies make it easier for an entrepreneur to provide such a service and can make its use completely transparent.

Ontologies can enhance the functioning of the Web in many ways. They can be used in a simple fashion to improve the accuracy of Web searches?the search program can look for only those pages that refer to a precise concept instead of all the ones using ambiguous keywords. More advanced applications will use ontologies to relate the information on a page to the associated knowledge structures and inference rules. An example of a page marked up for such use is online at http://www.cs.umd.edu/~hendler. If you send your Web browser to that page, you will see the normal Web page entitled “Dr. James A. Hendler.” As a human, you can readily find the link to a short biographical note and read there that Hendler received his Ph.D. from Brown University. A computer program trying to find such information, however, would have to be very complex to guess that this information might be in a biography and to understand the English language used there.

For computers, the page is linked to an ontology page that defines information about computer science departments. For instance, professors work at universities and they generally have doctorates. Further markup on the page (not displayed by the typical Web browser) uses the ontology’s concepts to specify that Hendler received his Ph.D. from the entity described at the URI http://www. brown.edu ? the Web page for Brown. Computers can also find that Hendler is a member of a particular research project, has a particular e-mail address, and so on. All that information is readily processed by a computer and could be used to answer queries (such as where Dr. Hendler received his degree) that currently would require a human to sift through the content of various pages turned up by a search engine.

In addition, this markup makes it much easier to develop programs that can tackle complicated questions whose answers do not reside on a single Web page. Suppose you wish to find the Ms. Cook you met at a trade conference last year. You don’t remember her first name, but you remember that she worked for one of your clients and that her son was a student at your alma mater. An intelligent search program can sift through all the pages of people whose name is “Cook” (sidestepping all the pages relating to cooks, cooking, the Cook Islands and so forth), find the ones that mention working for a company that’s on your list of clients and follow links to Web pages of their children to track down if any are in school at the right place.


The real power of the Semantic Web will be realized when people create many programs that collect Web content from diverse sources, process the information and exchange the results with other programs. The effectiveness of such software agents will increase exponentially as more machine-readable Web content and automated services (including other agents) become available. The Semantic Web promotes this synergy: even agents that were not expressly designed to work together can transfer data among themselves when the data come with semantics.

An important facet of agents’ functioning will be the exchange of “proofs” written in the Semantic Web’s unifying language (the language that expresses logical inferences made using rules and information such as those specified by ontologies). For example, suppose Ms. Cook’s contact information has been located by an online service, and to your great surprise it places her in Johannesburg. Naturally, you want to check this, so your computer asks the service for a proof of its answer, which it promptly provides by translating its internal reasoning into the Semantic Web’s unifying language. An inference engine in your computer readily verifies that this Ms. Cook indeed matches the one you were seeking, and it can show you the relevant Web pages if you still have doubts. Although they are still far from plumbing the depths of the Semantic Web’s potential, some programs can already exchange proofs in this way, using the current preliminary versions of the unifying language.

Another vital feature will be digital signatures, which are encrypted blocks of data that computers and agents can use to verify that the attached information has been provided by a specific trusted source. You want to be quite sure that a statement sent to your accounting program that you owe money to an online retailer is not a forgery generated by the computer-savvy teenager next door. Agents should be skeptical of assertions that they read on the Semantic Web until they have checked the sources of information. (We wish more people would learn to do this on the Web as it is!)

Many automated Web-based services already exist without semantics, but other programs such as agents have no way to locate one that will perform a specific function. This process, called service discovery, can happen only when there is a common language to describe a service in a way that lets other agents “understand” both the function offered and how to take advantage of it. Services and agents can advertise their function by, for example, depositing such descriptions in directories analogous to the Yellow Pages.

Some low-level service-discovery schemes are currently available, such as Microsoft’s Universal Plug and Play, which focuses on connecting different types of devices, and Sun Microsystems’s Jini, which aims to connect services. These initiatives, however, attack the problem at a structural or syntactic level and rely heavily on standardization of a predetermined set of functionality descriptions. Standardization can only go so far, because we can’t anticipate all possible future needs.

Properly designed, the Semantic Web can assist the evolution of human knowledge as a whole.

The Semantic Web, in contrast, is more flexible. The consumer and producer agents can reach a shared understanding by exchanging ontologies, which provide the vocabulary needed for discussion. Agents can even “bootstrap” new reasoning capabilities when they discover new ontologies. Semantics also makes it easier to take advantage of a service that only partially matches a request.A typical process will involve the creation of a “value chain” in which subassemblies of information are passed from one agent to another, each one “adding value,” to construct the final product requested by the end user. Make no mistake: to create complicated value chains automatically on demand, some agents will exploit artificial-intelligence technologies in addition to the Semantic Web. But the Semantic Web will provide the foundations and the framework to make such technologies more feasible.

Putting all these features together results in the abilities exhibited by Pete’s and Lucy’s agents in the scenario that opened this article. Their agents would have delegated the task in piecemeal fashion to other services and agents discovered through service advertisements. For example, they could have used a trusted service to take a list of providers and determine which of them are in-plan for a specified insurance plan and course of treatment. The list of providers would have been supplied by another search service, et cetera. These activities formed chains in which a large amount of data distributed across the Web (and almost worthless in that form) was progressively reduced to the small amount of data of high value to Pete and Lucy?a plan of appointments to fit their schedules and other requirements.

In the next step, the Semantic Web will break out of the virtual realm and extend into our physical world. URIs can point to anything, including physical entities, which means we can use the RDF language to describe devices such as cell phones and TVs. Such devices can advertise their functionality?what they can do and how they are controlled?much like software agents. Being much more flexible than low-level schemes such as Universal Plug and Play, such a semantic approach opens up a world of exciting possibilities.

For instance, what today is called home automation requires careful configuration for appliances to work together. Semantic descriptions of device capabilities and functionality will let us achieve such automation with minimal human intervention. A trivial example occurs when Pete answers his phone and the stereo sound is turned down. Instead of having to program each specific appliance, he could program such a function once and for all to cover every local device that advertises having a volume control ? the TV, the DVD player and even the media players on the laptop that he brought home from work this one evening.

The first concrete steps have already been taken in this area, with work on developing a standard for describing functional capabilities of devices (such as screen sizes) and user preferences. Built on RDF, this standard is called Composite Capability/Preference Profile (CC/PP). Initially it will let cell phones and other nonstandard Web clients describe their characteristics so that Web content can be tailored for them on the fly. Later, when we add the full versatility of languages for handling ontologies and logic, devices could automatically seek out and employ services and other devices for added information or functionality. It is not hard to imagine your Web-enabled microwave oven consulting the frozen-food manufacturer’s Web site for optimal cooking parameters.

Evolution of Knowledge

The semantic web is not “merely” the tool for conducting individual tasks that we have discussed so far. In addition, if properly designed, the Semantic Web can assist the evolution of human knowledge as a whole.

Human endeavor is caught in an eternal tension between the effectiveness of small groups acting independently and the need to mesh with the wider community. A small group can innovate rapidly and efficiently, but this produces a subculture whose concepts are not understood by others. Coordinating actions across a large group, however, is painfully slow and takes an enormous amount of communication. The world works across the spectrum between these extremes, with a tendency to start small?from the personal idea?and move toward a wider understanding over time.

An essential process is the joining together of subcultures when a wider common language is needed. Often two groups independently develop very similar concepts, and describing the relation between them brings great benefits. Like a Finnish-English dictionary, or a weights-and-measures conversion table, the relations allow communication and collaboration even when the commonality of concept has not (yet) led to a commonality of terms.

The Semantic Web, in naming every concept simply by a URI, lets anyone express new concepts that they invent with minimal effort. Its unifying logical language will enable these concepts to be progressively linked into a universal Web. This structure will open up the knowledge and workings of humankind to meaningful analysis by software agents, providing a new class of tools by which we can live, work and learn together.

Further Information:Weaving the Web: The Original Design and Ultimate Destiny of the World Wide Web by Its Inventor.
Tim Berners-Lee, with Mark Fischetti. Harper San Francisco, 1999.
An enhanced version of this article is on the Scientific American Web site, with additional material and links.

World Wide Web Consortium (W3C): http://www.w3.org/

W3C Semantic Web Activity: http://www.w3.org/2001/sw/

An introduction to ontologies: http://www.SemanticWeb.org/knowmarkup.html

Simple HTML Ontology Extensions Frequently Asked Questions (SHOE FAQ): http://www.cs.umd.edu/projects/plus/SHOE/faq.html

DARPA Agent Markup Language (DAML) home page: http://www.daml.org/ 

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Collective intelligence

From Wikipedia, the free encyclopedia

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Collective intelligence is a form of intelligence that emerges from the collaboration and competition of many individuals. Collective intelligence appears in a wide variety of forms of consensus decision making in bacteria, animals, humans, and computers. The study of collective intelligence may properly be considered a subfield of sociology, of business, of computer science, and of mass behavior — a field that studies collective behavior from the level of quarks to the level of bacterial, plant, animal, and human societies.

The above definition has emerged from the writings of Peter Russell (1983), Tom Atlee (1993), Pierre Lévy (1994), Howard Bloom (1995), Francis Heylighen (1995), Douglas Engelbart, Cliff Joslyn, Ron Dembo, Gottfried Mayer-Kress (2003) and other theorists. Collective intelligence is referred to as Symbiotic intelligence by Norman L. Johnson.

Some figures like Tom Atlee prefer to focus on collective intelligence primarily in humans and actively work to upgrade what Howard Bloom calls “the group IQ”. Atlee feels that collective intelligence can be encouraged “to overcome ‘groupthink‘ and individual cognitive bias in order to allow a collective to cooperate on one process—while achieving enhanced intellectual performance.”

One CI pioneer, George Pór, defined the collective intelligence phenomenon as “the capacity of human communities to evolve towards higher order complexity and harmony, through such innovation mechanisms as differentiation and integration, competition and collaboration.”[1] Tom Atlee and George Pór state that “collective intelligence also involves achieving a single focus of attention and standard of metrics which provide an appropriate threshold of action”. Their approach is rooted in Scientific Community Metaphor.




[edit] General concepts

Howard Bloom traces the evolution of collective intelligence from the days of our bacterial ancestors 3.5 billion years ago to the present and demonstrates how a multi-species intelligence has worked since the beginning of life. [2]

Tom Atlee and George Pór, on the other hand, feel that while group theory and artificial intelligence have something to offer, the field of collective intelligence should be seen by some as primarily a human enterprise in which mind-sets, a willingness to share, and an openness to the value of distributed intelligence for the common good are paramount. Individuals who respect collective intelligence, say Atlee and Pór, are confident of their own abilities and recognize that the whole is indeed greater than the sum of any individual parts.[citation needed]

From Pór and Atlee’s point of view, maximizing collective intelligence relies on the ability of an organization to accept and develop “The Golden Suggestion”, which is any potentially useful input from any member. Groupthink often hampers collective intelligence by limiting input to a select few individuals or filtering potential Golden Suggestions without fully developing them to implementation.

Knowledge focusing through various voting methods has the potential for many unique perspectives to converge through the assumption that uninformed voting is to some degree random and can be filtered from the decision process leaving only a residue of informed consensus. Critics point out that often bad ideas, misunderstandings, and misconceptions are widely held, and that structuring of the decision process must favor experts who are presumably less prone to random or misinformed voting in a given context.

While these are the views of experts like Atlee and Pór, other founding fathers of collective intelligence see the field differently. Francis Heylighen, Valerie Turchin, and Gottfried Mayer-Kress view collective intelligence through the lens of computer science and cybernetics. Howard Bloom stresses the biological adaptations that have turned most of this earth’s living beings into components of what he calls “a learning machine”. And Peter Russell, Elisabet Sahtouris, and Barbara Marx Hubbard (originator of the term “conscious evolution”) are inspired by the visions of a noosphere–a transcendent, rapidly evolving collective intelligence–an informational cortex of the planet.

[edit] History

An early precursor of the concept of collective intelligence was entomologist William Morton Wheeler‘s observation that seemingly independent individuals can cooperate so closely as to become indistinguishable from a single organism. In 1911 Wheeler saw this collaborative process at work in ants, who acted like the cells of a single beast with a collective mind. He called the larger creature that the colony seemed to form a “superorganism”.

In 1912, Émile Durkheim identified society as the sole source of human logical thought. He argues in The Elementary Forms of Religious Life that society constitutes a higher intelligence because it transcends the individual over space and time. [3]

Collective intelligence, which has antecedents in Pierre Teilhard de Chardin‘s concept of “noosphere” as well as H.G. Wells‘s concept of “world brain,” has more recently been examined in depth by Pierre Lévy in a book by the same name, by Howard Bloom in Global Brain (see also the term global brain), by Howard Rheingold in Smart Mobs, and by Robert David Steele Vivas in The New Craft of Intelligence. The latter introduces the concept of all citizens as “intelligence minutemen,” drawing only on legal and ethical sources of information, as able to create a “public intelligence” that keeps public officials and corporate managers honest, turning the concept of “national intelligence” on its head (previously concerned about spies and secrecy).

In 1986, Howard Bloom combined the concepts of apoptosis, parallel distributed processing, group selection, and the superorganism to produce a theory of how a collective intelligence works [4]. Later, he went further and showed how collective intelligences like those of competing bacterial colonies and of competing human societies can be explained in terms of computer-generated “complex adaptive systems” and the “genetic algorithms”, concepts pioneered by John Holland. [2]

David Skrbina [5] cites the concept of a ‘group mind’ as being derived from Plato’s concept of panpsychism (that mind or consciousness is omnipresent and exists in all matter). He follows the development of the concept of a ‘group mind’ as articulated by Hobbes in relation to his Leviathan which functioned as a coherent entity and Fechner’s arguments for a collective consciousness of mankind. He cites Durkheim as the most notable advocate of a ‘collective consciousness” and Teilhard as the thinker who has developed the philosophical implications of the group mind more than any other.

Collective intelligence is an amplification of the precepts of the Founding Fathers, as represented by Thomas Jefferson in his statement, “A Nation’s best defense is an educated citizenry.” During the industrial era, schools and corporations took a turn toward separating elites from the people they expected to follow them. Both government and private sector organizations glorified bureaucracy and, with bureaucracy, secrecy and compartmentalized knowledge. In the past twenty years, a body of knowledge has emerged which demonstrates that secrecy is actually pathological, and enables selfish decisions against the public interest. Collective intelligence restores the power of the people over their society, and neutralizes the power of vested interests that manipulate information to concentrate wealth.

[edit] Types of collective intelligence


[edit] Examples of collective intelligence

The best-known collective intelligence projects are political parties, which mobilize large numbers of people to form policy, select candidates and to finance and run election campaigns. Military units, trade unions, and corporations are focused on more narrow concerns but would satisfy some definitions of a genuine “C.I.”—the most rigorous definition would require a capacity to respond to very arbitrary conditions without orders or guidance from “law” or “customers” that tightly constrain actions. Another example is in which online advertising companies like BootB are using collective intelligence in order to bypass marketing agencies.

Improvisational actors also experience a type of collective intelligence, which they term ‘Group Mind’.

[edit] Mathematical techniques

One measure sometimes applied, especially by more artificial intelligence focused theorists, is a “collective intelligence quotient” (or “cooperation quotient”)—which presumably can be measured like the “individual” intelligence quotient (IQ)—thus making it possible to determine the marginal extra intelligence added by each new individual participating in the collective, thus using metrics to avoid the hazards of group think and stupidity.

In 2001, Tadeusz (Ted) Szuba from AGH University in Poland proposed a formal model for the phenomenon of Collective Intelligence. It is assumed to be an unconscious, random, parallel, and distributed computational process, run in mathematical logic by the social structure. [6].

In this model, beings and information are modeled as abstract information molecules carrying expressions of mathematical logic. They are quasi-randomly displacing due to their interaction with their environments with their intended displacements. Their interaction in abstract computational space creates multithread inference process which we perceive as Collective Intelligence. Thus, a non-Turing model of computation is used. This theory allows simple formal definition of Collective Intelligence as the property of social structure and seems to be working well for a wide spectrum of beings, from bacterial colonies up to human social structures. Collective Intelligence considered as a specific computational process is providing a straightforward explanation of several social phenomena. For this model of Collective Intelligence, the formal definition of IQS (IQ Social) was proposed and was defined as “the probability function over the time and domain of N-element inferences which are reflecting inference activity of the social structure.” While IQS seems to be computationally hard, modeling of social structure in terms of a computational process as described above gives a chance for approximation. Prospective applications are optimization of companies through the maximization of their IQS, and the analysis of drug resistance against Collective Intelligence of bacterial colonies.[6]

[edit] Opposing views

Skeptics, especially those critical of artificial intelligence and more inclined to believe that risk of bodily harm and bodily action are the basis of all unity between people, are more likely to emphasize the capacity of a group to take action and withstand harm as one fluid mass mobilization, shrugging off harms the way a body shrugs off the loss of a few cells. This strain of thought is most obvious in the anti-globalization movement and characterized by the works of John Zerzan, Carol Moore, and Starhawk, who typically shun academics. These theorists are more likely to refer to ecological and collective wisdom and to the role of consensus process in making ontological distinctions than to any form of “intelligence” as such, which they often argue does not exist, or is mere “cleverness”.

Harsh critics of artificial intelligence on ethical grounds are likely to promote collective wisdom-building methods, such as the new tribalists and the Gaians. Whether these can be said to be collective intelligence systems is an open question. Some, e.g. Bill Joy, simply wish to avoid any form of autonomous artificial intelligence and seem willing to work on rigorous collective intelligence in order to remove any possible niche for AI.

[edit] Recent developments

Growth of the Internet and mobile telecom has also highlighted “swarming” or “rendezvous” technologies that enable meetings or even dates on demand. The full impact of such technology on collective intelligence and political effort has yet to be felt, but the anti-globalization movement relies heavily on e-mail, cell phones, pagers, SMS, and other means of organizing before, during, and after events. One theorist involved in both political and theoretical activity, Tom Atlee, codifies on a disciplined basis the connections between these events and the political imperatives that drive them. The Indymedia organization does this in a more journalistic way, and there is some coverage of such current events even here at Wikipedia.

It seems likely that such resources could combine in future into a form of collective intelligence accountable only to the current participants but with some strong moral or linguistic guidance from generations of contributors – or even take on a more obviously political form, to advance some shared goals.

[edit] See also

[edit] Notes

  1. ^ George Pór, Blog of Collective Intelligence
  2. ^ a b Howard Bloom, Global Brain: The Evolution of Mass Mind from the Big Bang to the 21st Century, 2000
  3. ^ Émile Durkheim, The Elementary Forms of Religious Life, 1912.
  4. ^ Howard Bloom, The Lucifer Principle: A Scientific Expedition Into the Forces of History, 1995
  5. ^ Skrbina, D., 2001, Participation, Organization, and Mind: Toward a Participatory Worldview [1], ch. 8, Doctoral Thesis, Centre for Action Research in Professional Practice, School of Management, University of Bath: England
  6. ^ a b Szuba T., Computational Collective Intelligence, 420 pages, Wiley NY, 2001

[edit] References

Sun, Ron, (2006). “Cognition and Multi-Agent Interaction”. Cambridge University Press.

[edit] External links

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