Archives for posts with tag: Semantic Sphere

What is IEML?

  • IEML (Information Economy MetaLanguage) is an open (GPL3) and free artificial metalanguage that is simultaneously a programming language, a pivot between natural languages and a semantic coordinate system. When data are categorized in IEML, the metalanguage compute their semantic relationships and distances.
  • From a “social” point of view, on line communities categorizing data in IEML generate explorable ecosystems of ideas that represent their collective intelligence.
  • Github.

What problems does IEML solve?

  • Decompartmentalization of tags, folksonomies, taxonomies, ontologies and languages (french and english for now).
  • Semantic search, automatic computing and visualization of semantic relations and distances between data.
  • Giving back to the users the information that they produce, enabling reflexive collective intelligence.

Who is IEML for?

Content curators

  • knowledge management
  • marketing
  • curation of open data from museums and libraries, crowdsourced curation
  • education, collaborative learning, connectionists MOOCs
  • watch, intelligence

Self-organizing on line communities

  • smart cities
  • collaborative teams
  • communities of practice…

Researchers

  • artificial intelligence
  • data analytics
  • humanities and social sciences, digital humanities

What motivates people to adopt IEML?

  • IEML users participate in the leading edge of digital innovation, big data analytics and collective intelligence.
  • IEML can enhance other AI techniques like machine learning, deep learning, natural language processing and rule-based inference.

IEML tools

IEML v.0

IEML v.0 includes…

  • A dictionary of  concepts whose edition is restricted to specialists but navigation and use is open to all.
  • A library of tags – called USLs (Uniform Semantic Locators) – whose edition, navigation and use is open to all.
  • An API allowing access to the dictionary, the library and their functionalities (semantic computing).

Intellect v.0

Intellect v.0 is a Twitter client (using the IEML API) that allows the categorization of data in IEML and their semantic computing.

Subsequent versions will address other social media.

When will it ship?

December 2017

Who made it?

The IEML project is designed and led by Pierre Lévy.

It has been financed by the Canada Research Chair in Collective Intelligence at the University of Ottawa (2002-2016).

At an early stage (2004-2011) Steve Newcomb and Michel Biezunski have contributed to the design and implementation (parser, dictionary). Christian Desjardins implemented a second version of the dictionary. Andrew Roczniak helped for the first mathematical formalization, implemented a second version of the parser and a third version of the dictionary (2004-2016).

The 2016 version has been implemented by Louis van Beurden, Hadrien Titeux (chief engineers), Candide Kemmler (project management, interface), Zakaria Soliman and Alice Ribaucourt.

The 2017 version (1.0) has been implemented by Louis van Beurden (chief engineer), Eric Waldman (IEML edition interface, visualization), Sylvain Aube (Drupal), Ludovic Carré and Vincent Lefoulon (collections and tags management).

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Dice sculpture by Tony Cragg

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FIGURE 1

La figure 1 présente les six catégories primitives de la sémantique et schématise leurs relations d’interdépendance. On peut comparer ce diagramme à celui d’une main. Alors que l’organe du corps humain possède cinq doigts et une opposition entre le pouce et les autres doigts, la main sémantique comporte six doigts – les catégories – et quatre systèmes d’opposition emboîtés – les relations –. La pince du virtuel et de l’actuel commande la relation d’interdépendance pragmatique. La pince ternaire entre le signe, l’être et la chose prend en charge l’interdépendance sémiotique. L’opposition de la pince pragmatique et de la pince sémiotique actionne la tenaille de la relation formelle. La méta-pince rationnelle, enfin, oppose le vide et la forme.

Le vide, le virtuel, l’actuel, le signe, l’être et la chose ne sont pas de mon invention, je me suis contenté de recueillir puis de mettre en forme un très ancien héritage. La plupart des catégories sémantiques remontent au moins à Aristote et elles ont été raffinées par une longue tradition de philosophes, de logiciens, de sémioticiens, de grammairiens et de linguistes. On trouvera une importante bibliographie à ce sujet dans La Sphère sémantique (2011). Je me contenterai ici de signaler quelques-unes de mes sources.

La relation pragmatique entre virtuel et actuel

On catégorise comme actuel ce qui possède un caractère concret ou sensible et qui se localise facilement dans l’espace et le temps. Par contraste, le virtuel catégorise ce qui possède un caractère intelligible, abstrait et auquel il est difficile d’attribuer une adresse spatio-temporelle bien déterminée.

Le couple virtuel/actuel décrit une dialectique pragmatique, que l’on peut retrouver notamment dans la dynamique entre potentialités (virtuelles) et réalisations (actuelles), problèmes (virtuels) et solutions (actuelles), stratégies (relation aux finalités virtuelles) et tactiques (relations aux moyens actuels), etc.

La dialectique du virtuel et de l’actuel fait écho au Ciel et à la Terre des premières nations, au yang et au yin de la pensée chinoise, à l’âme et au corps, à la transcendance et à l’immanence, à l’intelligible et au sensible (Platon), à la puissance et à l’acte (Aristote), à la pensée et à l’étendue (Descartes), au transcendantal et à l’empirique (Kant)… J’ai par ailleurs consacré un livre entier à cette question.

Virtuel et actuel sont des notions relatives, qui se définissent l’une par l’autre. Dans l’analyse d’un cycle pragmatique complexe, la catégorisation des phases virtuelle et actuelle relève de l’interprétation en contexte. Par ailleurs, virtualité et actualité peuvent définir les deux pôles d’un continuum. Ainsi, un caillou rêvé est plus virtuel qu’un caillou perçu, mais plus actuel qu’un rêve de caillou dans un roman.

La relation sémiotique entre signe, être et chose

Le signe catégorise ce qui est de l’ordre du code, du message et de la connaissance. L’être indique les personnes, leurs rapports et leurs intentions, ce qui est de l’ordre de l’esprit, la capacité de concevoir ou d’interpréter. Enfin, la chose catégorise ce dont on parle, les objets (abstraits ou concrets), les corps, les outils, le pouvoir.

Le ternarité signe/être/chose exprime une dialectique sémiotique, que l’on retrouve notamment dans la distribution des rôles de communication : on pourra par exemple distinguer entre les messages (signes), les personnes (êtres) et les éléments de contexte qui sont en jeu (choses). En logique, la proposition (signe), le jugement (être) et l’état de chose (chose) sont inséparables puisqu’un jugement logique détermine si un état de chose correspond, oui ou non, à une proposition.

La dialectique sémiotique du signe, de l’être et de la chose renvoie…

  • à la distinction aristotélicienne du symbole, de l’état d’esprit et de la chose ;
  • à la triade « vox, conceptus, res » de la philosophie médiévale ;
  • au fondement du signe, à l’interprétant et à l’objet de C. S. Peirce ;
  • au noème, à l’intention et à l’objet de Husserl (ou bien, à un niveau d’analyse plus fin décomposant le noème de cette dernière triade, la noèse – signe –, le noème au sens restreint – être – et la hylè sensible – chose – ;
  • au logos, à l’ethos et au pathos de la rhétorique ;
  • aux actes locutoire, illocutoire et perlocutoire de la pragmatique (tels que définis par Austin), et ainsi de suite.

De même que dans le cas de la dialectique virtuel/actuel, la dialectique signe/être/chose est hautement relative. Il s’agit surtout de déterminer le rôle joué par ce que l’on veut catégoriser selon les points de vue, les circonstances et les contextes. Par exemple, une personne pourra être catégorisée signe, être ou chose selon la fonction qu’elle joue dans une situation : messager (signe), interlocuteur (être) ou sujet de la conversation (chose).

La relation formelle entre pragmatique et sémiotique

Les dialectiques pragmatique et sémiotique sont elles-mêmes en relation dynamique d’interdépendance. L’interdépendance de la figure et du fond – de la représentation sémiotique et de l’interaction pragmatique – fait émerger une forme. A l’intérieur de cette dialectique, la sémiotique décrit les opérations de construction ou de production d’entités tandis que la pragmatique rend compte de l’exploration et de la reconnaissance de processus. C’est ainsi que les mouvements ou les verbes seront catégorisés comme pragmatiques et que les entités ou les noms seront catégorisés comme sémiotiques.

Sur un plan épistémologique, cette dialectique permet d’articuler deux faces complémentaires de la connaissance : le « savoir produire » de l’intérieur (sémiotique) et le « savoir reconnaître » à l’extérieur (pragmatique). Par exemple, maîtriser le langage suppose d’être capable de produire des phrases, ce qui se termine par une activation motrice, mais aussi de les reconnaître, ce qui commence par une activation sensorielle. Les deux aspects sont à la fois distincts et complémentaires. Pour prendre un autre exemple, nous savons marcher ou manger ; nous savons aussi reconnaître la marche ou la manducation chez les autres, donc ailleurs que dans notre propre activité ; finalement, nous savons qu’il s’agit de la même chose, même si savoir faire et savoir reconnaître mettent en oeuvre des sensations et des mouvements différents.

La relation rationnelle entre vide et forme

La dialectique rationnelle, enfin, met en relation la forme qui comprend les cinq catégories pleines (virtuel, actuel, signe, être, chose) et le vide. Le vide rend compte du zéro, du silence, du blanc, de l’indétermination sémantique et de l’espace ouvert où se déploie l’expérience humaine. A ce titre, il s’oppose aux catégories pleines qu’il exclut en tant que néant… mais qu’il contient en tant qu’espace. Le contraste entre le pattern et le bruit dans le signal. relève justement de cette dialectique rationnelle entre le vide (le bruit) et le plein (le pattern).

Une théorie sémantique de la cognition doit rendre compte de la distinction fondamentale entre fait et fiction, c’est-à-dire entre les idées dont les objets seront réputés inventés ou simulés (vide) et celles dont les objets seront supposés réels (plein) : il y va de la survie de l’être intelligent. C’est pourquoi, en fonction des circonstances, la dialectique rationnelle distinguera entre le vrai et le faux, le connu et l’inconnu, la réalité et l’imitation, les idées claires ou confuses…

Abstract

IEML is an artificial language that allows the automatic computing of (a) the semantic relationships internal to its texts and of (b) the semantic relationships between its texts. Such an innovation could have a positive impact on the development of human collective intelligence. While we are currently limited to logical and statistical analytics, semantic coding could allow large scale computing on the meaning of data, provided that these data are categorized in IEML. Moreover “big data” algorithms are currently monopolized by big companies and big governemnts. But according to the perspective adopted here, the algorithmic tools of the future will put data-anaytics, machine learning and reflexive collective intelligence in the hands of the majority of Internet users.
I will first describe the main components of an algorithm (code, operators, containers, instructions), then I will show that the growth of the algorithmic medium has been shaped by innovations in coding and containers addressing. The current limitations of the web (absence of semantic interoperability and statistical positivism) could be overcomed by the invention of a new coding system aimed at making the meaning computable. Finally I will describe the cognitive gains that can be secured from this innovation.

This paper has been published by Spanda Journal special issue on “Creativity & Collective Enlightenment”,  VI, 2, December 2015, p. 59-66

Our communications—transmission and reception of data—are based on an increasingly complex infrastructure for the automatic manipulation of symbols, which I call the algorithmic medium because it automates the transformation of data, and not only their conservation, reproduction and dissemination (as with previous media). Both our data-centric society and the algorithmic medium that provides its tools are still at their tentative beginnings. Although it is still hard to imagine today, a huge space will open up for the transformation and analysis of the deluge of data we produce daily. But our minds are still fascinated by the Internet’s power of dissemination of messages, which has almost reached its maximum.
In the vanguard of the new algorithmic episteme, IEML (or any other system that has the same properties) will democratize the categorization and automatic analysis of the ocean of data. The use of IEML to categorize data will create a techno-social environment that is even more favourable for collaborative learning and the distributed production of knowledge. In so doing, it will contribute to the emergence of the algorithmic medium of the future and reflect collective intelligence in the form of ecosystems of ideas.
This text begins by analyzing the structure and functioning of algorithms and shows that the major stages in the evolution of the new medium correspond to the appearance of new systems for encoding and addressing data: the Internet is a universal addressing system for computers and the Web, a universal addressing system for data. However, the Web, in 2016, has many limitations. Levels of digital literacy are still low. Interoperability and semantic transparency are sorely lacking. The majority of its users see the Web only as a big multimedia library or a means of communication, and pay no attention to its capacities for data transformation and analysis. As for those concerned with the processing of big data, they are hindered by statistical positivism. In providing a universal addressing system for concepts, IEML takes a decisive step toward the algorithmic medium of the future. The ecosystems of ideas based on this metalanguage will give rise to cognitive augmentations that are even more powerful than those we already enjoy.

What is an algorithm?

To help understand the nature of the new medium and its evolution, let us represent as clearly as possible what an algorithm is and how it functions. In simplified explanations of programming, the algorithm is often reduced to a series of instructions or a “recipe.” But no series of instructions can play its role without the three following elements: first, an adequate encoding of the data; second, a well-defined set of reified operators or functions that act as black boxes; third, a system of precisely addressed containers capable of recording initial data, intermediate results and the end result. The rules—or instructions—have no meaning except in relation to the code, the operators and the memory addresses.
I will now detail these aspects of the algorithm and use that analysis to periodize the evolution of the algorithmic medium. We will see that the major stages in the growth of this medium are precisely related to the appearance of new systems of addressing and encoding, both for the containers of data and for the operators. Based on IEML, the coming stage of development of the algorithmic medium will provide simultaneously a new type of encoding (semantic encoding) and a new system of virtual containers (semantic addressing).

Encoding of data

For automatic processing, data must first be encoded appropriately and uniformly. This involves not only binary encoding (zero and one), but more specialized types of encoding such as encoding of numbers (in base two, eight, ten, sixteen, etc.), that of characters used in writing, that of images (pixels), that of sounds (sampling), and so on.

Operators

We must then imagine a set of tools or specialized micro-machines for carrying out certain tasks on the data. Let us call these specialized tools “operators.” The operators are precisely identified, and they act in a determined or mechanical way, always the same way. There obviously has to be a correspondence or a match between the encoding of the data and the functioning of the operators.
The operators were first identified insider computers: they are the elementary electronic circuits that make up processors. But we can consider any processor of data—however complex it is—as a “black box” serving as a macro-operator. Thus the protocol of the Internet, in addressing the computers in the network, at the same time set up a universal addressing system for operators.

Containers

In addition to a code for the data and a set of operators, we have to imagine a storehouse of data whose basic boxes or “containers” are completely addressed: a logical system of recording with a smooth surface for writing, erasing and reading. It is clear that the encoding of data, the operations applied to them and the mode of recording them—and therefore their addressing—must be harmonized to optimize processing.
The first addressing system of the containers is internal to computers, and it is therefore managed by the various operating systems (for example, UNIX, Windows, Apple OS, etc.). But at the beginning of the 1990s, a universal addressing system for containers was established above that layer of internal addressing: the URLs of the World Wide Web.

Instructions

The fourth and last aspect of an algorithm is an ordered set of rules—or a control mechanism—that organizes the recursive circulation of data between the containers and the operators. The circulation is initiated by a data flow that goes from containers to the appropriate operators and then directs the results of the operations to precisely addressed containers. A set of tests (if . . . , then . . .) determines the choice of containers from which the data to be processed are drawn, the choice of operators and the choice of containers in which the results are recorded. The circulation of data ends when a test has determined that processing is complete. At that point, the result of the processing—a set of encoded data—is located at a precise address in the system of containers.

The growth of the new medium

To shape the future development of the algorithmic medium, we have to first look at its historical evolution.

Automatic calculation (1940-1970)

From when can we date the advent of the algorithmic medium? We might be tempted to give its date of birth as 1937, since it was in that year that Alan Turing (1912-1954) published his famous article introducing the concept of the universal machine, that is, the formal structure of a computer. The article represents calculable functions as programs of the universal machine, that is, essentially, algorithms. We could also choose 1945, because in June of that year, John von Neumann (1903-1957) published his “First draft of a report on the EDVAC,” in which he presented the basic architecture of computers: 1) a memory containing data and programs (the latter encoding algorithms), 2) an arithmetic, logical calculation unit and 3) a control unit capable of interpreting the instructions of the programs contained in the memory. Since the seminal texts of Alan Turing and John von Neumann represent only theoretical advances, we could date the new era from the construction and actual use of the first computers, in the 1950s. It is clear, however, that (in spite of the prescience of a few visionaries ) until the end of the 1970s, it was still hard to talk about an algorithmic medium. One of the main reasons is that the computers at that time were still big, costly, closed machines whose input and output interfaces could only be manipulated by experts. Although already in its infancy, the algorithmic medium was not yet socially prevalent.
It should be noted that between 1950 and 1980 (before Internet connections became the norm), data flows circulated mainly between containers and operators with local addresses enclosed in a single machine.

The Internet and personal computers (1970-1995)

A new trend emerged in the 1970s and became dominant in the 1980s: the interconnection of computers. The Internet protocol (invented in 1969) won out over its competitors in addressing machines in telecommunication networks. This was also the period when computing became personal. The digital was now seen as a vector of transformation and communication of all symbols, not only numbers. The activities of mail, telecommunications, publishing, the press, and radio and television broadcasting began to converge.
At the stage of the Internet and personal computers, data processed by algorithms were always stored in containers with local addresses, but—in addition to those addresses—operators now had universal physical addresses in the global network. Consequently, algorithmic operators could “collaborate,” and the range of types of processing and applications expanded significantly.

The World Wide Web (1995-2020)

It was only with the arrival of the Web, around 1995, however, that the Internet became the medium of most communication—to the point of irreversibly affecting the functioning of the traditional media and most economic, political and cultural institutions.
The revolution of the Web can be explained essentially as the creation of a universal system of physical addresses for containers. This system, of course, is URLs. It should be noted that—like the Internet protocol for operators—this universal system is added to the local addresses of the containers of data, it does not eliminate them. Tim Berners-Lee’s ingenious idea may be described as follows: by inventing a universal addressing system for data, he made possible the shift from a multitude of actual databases (each controlled by one computer) to a single virtual database for all computers. One of the main benefits is the possibility of creating hyperlinks among any of the data of that universal virtual database: “the Web.”
From then on, the effective power and the capacity for collaboration—or inter-operation—between algorithms increased and diversified enormously, since both operators and containers now possessed universal addresses. The basic programmable machine became the network itself, as is shown by the spread of cloud computing.
The decade 2010-2020 is seeing the beginning of the transition to a data-centric society. Indeed, starting with this phase of social utilization of the new medium, the majority of interactions among people take place through the Internet, whether purely for socialization or for information, work, research, learning, consumption, political action, gaming, watches, and so on. At the same time, algorithms increasingly serve as the interface for relationships between people, relationships among data, and relationships between people and data. The increase in conflicts around ownership and free accessibility of data, and around openness and transparency of algorithms, are clear signs of a transition to a data-centric society. However, in spite of their already decisive role, algorithms are not yet perceived in the collective consciousness as the new medium of human communication and thought. People were still fascinated by the logic of dissemination of previous media.
The next stage in the evolution of the algorithmic medium—the semantic sphere based on IEML—will provide a conceptual addressing system for data. But before we look at the future, we need to think about the limitations of the contemporary Web. Indeed, the Web was invented to help solve problems in interconnecting data that arose around 1990, at a time when one percent of the world’s population (mainly anglophone) was connected. But now in 2014, new problems have arisen involving the difficulties of translating and processing data, as well as the low level of digital literacy. When these problems become too pronounced (probably around 2020, when more than half the world’s population will be connected), we will be obliged to adopt a conceptual addressing system on top of the layer of physical addressing of the WWW.

The limitations of the Web in 2016

The inadequacy of the logic of dissemination

From Gutenberg until the middle of the twentieth century, the main technical effect of the media was the mechanical recording, reproduction and transmission of the symbols of human communication. Examples include printing (newspapers, magazines, books), the recording industry, movies, telephone, radio and television. While there were also technologies for calculation, or automatic transformation of symbols, the automatic calculators available before computers were not very powerful and their usefulness was limited.
The first computers had little impact on social communication because of their cost, the complexity of using them and the small number of owners (essentially big corporations, some scientific laboratories and the government administrations of rich countries). It was only beginning in the 1980s that the development of personal computing provided a growing proportion of the population with powerful tools for producing messages, whether these were texts, tables of numbers, images or music. From then on, the democratization of printers and the development of communication networks among computers, as well as the increased number of radio and television networks, gradually undermined the monopoly on the massive dissemination of messages that had traditionally belonged to publishers, professional journalists and the major television networks. This revolution in dissemination accelerated with the arrival of the World Wide Web in the mid-1990s and blossomed into the new kind of global multimedia public sphere that prevails now at the beginning of the twenty-first century.
In terms of the structure of social communication, the essential characteristic of the new public sphere is that it permits anyone to produce messages, to transmit to a community without borders and to access messages produced and transmitted by others. This freedom of communication is all the more effective since its exercise is practically free and does not require any prior technical knowledge. In spite of the limits I will describe below, we have to welcome the new horizon of communication that is now offered to us: at the rate at which the number of connections is growing, almost all human beings in the next generation will be able to disseminate their messages to the entire planet for free and effortlessly.
It is certain that automatic manipulation—or transformation—of symbols has been practiced since the 1960s and 1970s. I have also already noted that a large proportion of personal computing was used to produce information and not only to disseminate it. Finally, the major corporations of the Web such as Google, Amazon, eBay, Apple, Facebook, Twitter and Netflix daily process huge masses of data in veritable “information factories” that are entirely automated. In spite of that, the majority of people still see and use the Internet as a tool for the dissemination and reception of information, in continuity with the mass media since printing and, later, television. It is a little as if the Web gave every individual the power of a publishing house, a television network and a multimedia postal service in real time, as well as access to an omnipresent global multimedia library. Just as the first printed books—incunabula—closely copied the form of manuscripts, we still use the Internet to achieve or maximize the power of dissemination of previous media. Everyone can transmit universally. Everyone can receive from anywhere.
No doubt we will have to exhaust the technical possibilities of automatic dissemination—the power of the media of the last four centuries—in order to experience and begin to assimilate intellectually and culturally the almost unexploited potential of automatic transformation—the power of the media of centuries to come. That is why I am again speaking of the algorithmic medium: to emphasize digital communication’s capacity for automatic transformation. Of course, the transformation or processing power of the new medium can only be actualized on the basis of the irreversible achievement of the previous medium, the universal dissemination or ubiquity of information. That was nearly fully achieved at the beginning of the twenty-first century, and coming generations will gradually adapt to automatic processing of the massive flow of global data, with all its unpredictable cultural consequences. There are at this time three limits to this process of adaptation: users’ literacy, the absence of semantic interoperability and the statistical positivism that today governs data analysis.

The problem of digital literacy

The first limit of the contemporary algorithmic medium is related to the skills of social groups and individuals: the higher their education level (elementary, secondary, university), the better developed their critical thinking, the greater their mastery of the new tools for manipulation of symbols and the more capable they are of turning the algorithmic medium to their advantage. As access points and mobile devices increase in number, the thorny question of the digital divide is less and less related to the availability of hardware and increasingly concerns problems of print literacy, media literacy and education. Without any particular skills in programming or even in using digital tools, the power provided by ordinary reading and writing is greatly increased by the algorithmic medium: we gain access to possibilities for expression, social relationships and information such as we could not even have dreamed of in the nineteenth century. This power will be further increased when, in the schools of the future, traditional literacy, digital literacy and understanding of ecosystems of ideas are integrated. Then, starting at a very young age, children will be introduced to categorization and evaluation of data, collection and analysis of large masses information and programming of semantic circuits.

The absence of semantic interoperability

The second limit is semantic, since, while technical connection is tending to become universal, the communication of meaning still remains fragmented according to the boundaries of languages, systems of classification, disciplines and other cultural worlds that are more or less unconnected. The “semantic Web” promoted by Tim Berners-Lee since the late 1990s is very useful for translating logical relationships among data. But it has not fulfilled its promise with regard to the interoperability of meaning, in spite of the authority of its promoter and the contributions of many teams of engineers. As I showed in the first volume of The Semantic Sphere, it is impossible to fully process semantic problems while remaining within the narrow limits of logic. Moreover, the essentially statistical methods used by Google and the numerous systems of automatic translation available provide tools to assist with translation, but they have not succeeded any better than the “semantic Web” in opening up a true space of translinguistic communication. Statistics are no more effective than logic in automating the processing of meaning. Here again, we lack a coding of linguistic meaning that would make it truly calculable in all its complexity. It is to meet this need that IEML is automatically translated into natural languages in semantic networks.

Statistical positivism

The general public’s access to the power of dissemination of the Web and the flows of digital data that now result from all human activities confront us with the following problem: how to transform the torrents of data into rivers of knowledge? The solution to this problem will determine the next stage in the evolution of the algorithmic medium. Certain enthusiastic observers of the statistical processing of big data, such as Chris Anderson, the former editor-in-chief of Wired, were quick to declare that scientific theories—in general!—were now obsolete. In this view, we now need only flows of data and powerful statistical algorithms operating in the computing centres of the cloud: theories—and therefore the hypotheses they propose and the reflections from which they emerge—belong to a bygone stage of the scientific method. It appears that numbers speak for themselves. But this obviously involves forgetting that it is necessary, before any calculation, to determine the relevant data, to know exactly what is being counted and to name—that is, to categorize—the emerging patterns. In addition, no statistical correlation directly provides causal relationships. These are necessarily hypotheses to explain the correlations revealed by statistical calculations. Under the guise of revolutionary thought, Chris Anderson and his like are reviving the old positivist, empiricist epistemology that was fashionable in the nineteenth century, according to which only inductive reasoning (that is, reasoning based solely on data) is scientific. This position amounts to repressing or ignoring the theories—and therefore the risky hypotheses based on individual thought—that are necessarily at work in any process of data analysis and that are expressed in decisions of selection, identification and categorization. One cannot undertake statistical processing and interpret its results without any theory. Once again, the only choice we have is to leave the theories implicit or to explicate them. Explicating a theory allows us to put it in perspective, compare it with other theories, share it, generalize from it, criticize it and improve it. This is even one of the main components of what is known as critical thinking, which secondary and university education is supposed to develop in students.
Beyond empirical observation, scientific knowledge has always been concerned with the categorization and correct description of phenomenal data, description that is necessarily consistent with more or less formalized theories. By describing functional relationships between variables, theory offers a conceptual grasp of the phenomenal world that make it possible (at least partially) to predict and control it. The data of today correspond to what the epistemology of past centuries called phenomena. To extend this metaphor, the algorithms for analyzing flows of data of today correspond to the observation tools of traditional science. These algorithms show us patterns, that is, ultimately, images. But the fact that we are capable of using the power of the algorithmic medium to observe data does not mean we should stop here on this promising path. We now need to use the calculating power of the Internet to theorize (categorize, model, explain, share, discuss) our observations, without forgetting to make our theorizing available to the rich collective intelligence.
In their 2013 book on big data, Viktor Mayer-Schonberger and Kenneth Cukier, while emphasizing the distinction between correlation and causality, predicted that we would take more and more interest in correlations and less and less in causality, which put them firmly in the empiricist camp. Their book nevertheless provides an excellent argument against statistical positivism. Indeed, they recount the very beautiful story of Matthew Maury, an American naval officer who in the mid-nineteenth century compiled data from log books in the official archives to establish reliable maps of winds and currents. Those maps were constructed from an accumulation of empirical data. But with all due respect for Cukier and Mayer-Schonberger, I would point out that such an accumulation would never have been useful, or even feasible, without the system of geographic coordinates of meridians and parallels, which is anything but empirical and based on data. Similarly, it is only by adopting a system of semantic coordinates such as IEML that we will be able to organize and share data flows in a useful way.
Today, most of the algorithms that manage routing of recommendations and searching of data are opaque, since they are protected trade secrets of major corporations of the Web. As for the analytic algorithms, they are, for the most part, not only opaque but also beyond the reach of most Internet users for both technical and economic reasons. However, it is impossible to produce reliable knowledge using secret methods. We must obviously consider the contemporary state of the algorithmic medium to be transitory.
What is more, if we want to solve the problem of the extraction of useful information from the deluge of big data, we will not be able to eternally limit ourselves to statistical algorithms working on the type of organization of digital memory that exists in 2016. We will sooner or later, and the sooner the better, have to implement an organization of memory designed from the start for semantic processing. We will only be able to adapt culturally to the exponential growth of data—and therefore transform these data into reflected knowledge—through a qualitative change of the algorithmic medium, including the adoption of a system of semantic coordinates such as IEML.

The semantic sphere and its conceptual addressing (2020…)

It is notoriously difficult to observe or recognize what does not yet exist, and even more, the absence of what does not yet exist. However, what is blocking the development of the algorithmic medium—and with it, the advent of a new civilization—is precisely the absence of a universal, calculable system of semantic metadata. I would like to point out that the IEML metalanguage is the first, and to my knowledge (in 2016) the only, candidate for this new role of a system of semantic coordinates for data.
We already have a universal physical addressing system for data (the Web) and a universal physical addressing system for operators (the Internet). In its full deployment phase, the algorithmic medium will also include a universal semantic code: IEML. This system of metadata—conceived from the outset to optimize the calculability of meaning while multiplying its differentiation infinitely—will open the algorithmic medium to semantic interoperability and lead to new types of symbolic manipulation. Just as the Web made it possible to go from a great many actual databases to one universal virtual database (but based on a physical addressing system), IEML will make it possible to go from a universal physical addressing system to a universal conceptual addressing system. The semantic sphere continues the process of virtualization of containers to its final conclusion, because its semantic circuits—which are generated by an algebra—act as data containers. It will be possible to use the same conceptual addressing system in operations as varied as communication, translation, exploration, searching and three-dimensional display of semantic relationships.
Today’s data correspond to the phenomena of traditional science, and we need calculable, interoperable metadata that correspond to scientific theories and models. IEML is precisely an algorithmic tool for theorization and categorization capable of exploiting the calculating power of the cloud and providing an indispensable complement to the statistical tools for observing patterns. The situation of data analysis before and after IEML can be compared to that of cartography before and after the adoption of a universal system of geometric coordinates. The data that will be categorized in IEML will be able to be processed much more efficiently than today, because the categories and the semantic relationships between categories will then become not only calculable but automatically translatable from one language to another. In addition, IEML will permit comparison of the results of the analysis of the same set of data according to different categorization rules (theories!).

Algo-medium

FIGURE 1 – The four interdependent levels of the algorithmic medium

When this symbolic system for conceptual analysis and synthesis is democratically accessible to everyone, translated automatically into all languages and easily manipulated by means of a simple tablet, then it will be possible to navigate the ocean of data, and the algorithmic medium will be tested directly as a tool for cognitive augmentation—personal and social—and not only for dissemination. Then a positive feedback loop between the collective testing and creation of tools will lead to a take-off of the algorithmic intelligence of the future.
In Figure 1, the increasingly powerful levels of automatic calculation are represented by rectangles. Each level is based on the “lower” levels that precede it in order of historical emergence. Each level is therefore influenced by the lower levels. But, conversely, each new level gives the lower levels an additional socio-technical determination, since it uses them for a new purpose.
The addressing systems, which are represented under the rectangles, can be considered the successive solutions—influenced by different socio-technical contexts—to the perennial problem of increasing the power of automatic calculation. An addressing system thus plays the role of a step on a stairway that lets you go from one level of calculation to a higher level. The last addressing system, that of metadata, is supplied by IEML or any other system of encoding of linguistic meaning that makes that meaning calculable, exactly as the system of pixels made images manipulable by means of algorithms.

The cognitive revolution of semantic encoding

We know that the algorithmic medium is not only a medium of communication or dissemination of information but also, especially, a ubiquitous environment for the automatic transformation of symbols. We also know that a society’s capacities for analysis, synthesis and prediction are based ultimately on the structure of its memory, and in particular its system for encoding and organizing data. As we saw in the previous section, the only thing the algorithmic medium now in construction lacks to become the matrix of a new episteme that is more powerful than today’s, which has not yet broken its ties to the typographical era, is a system of semantic metadata that is equal to the calculating power of algorithms.

Memory, communication and intuition

It is now accepted that computers increase our memory capacities, in which I include not only capacities for recording and recall, but also those for analysis, synthesis and prediction. The algorithmic medium also increases our capacities for communication, in particular in terms of the breadth of the network of contacts and the reception, transmission and volume of flows of messages. Finally, the new medium increases our capacities for intuition, because it increases our sensory-motor interactions (especially gestural, tactile, visual and sound interactions) with large numbers of people, documents and environments, whether they are real, distant, simulated, fictional or mixed. These augmentations of memory, communication and intuition influence each other to produce an overall augmentation of our field of cognitive activity.
Semantic encoding, that is, the system of semantic metadata based on IEML, will greatly increase the field of augmented cognitive activity that I have described. It will produce a second level of cognitive complexity that will enter into dynamic relationship with the one described above to give rise to algorithmic intelligence. As we will see, semantic coding will generate a reflexivity of memory, a new perspectivism of intellectual intuition and an interoperability of communication.

Reflexive memory

The technical process of objectivation and augmentation of human memory began with the invention of writing and continued up to the development of the Web. But in speaking of reflexive memory, I go beyond Google and Wikipedia. In the future, the structure and evolution of our memory and the way we use it will become transparent and open to comparison and critical analysis. Indeed, communities will be able to observe—in the form of ecosystems of ideas—the evolution and current state of their cognitive activities and apply their capacities for analysis, synthesis and prediction to the social management of their knowledge and learning. At the same time, individuals will become capable of managing their personal knowledge and learning in relation to the various communities to which they belong. So much so that this reflexive memory will enable a new dialectic—a virtuous circle—of personal and collective knowledge management. The representation of memory in the form of ecosystems of ideas will allow individuals to make maximum use of the personal growth and cross-pollination brought about by their circulation among communities.

Perspectivist intellectual intuition

Semantic coding will give us a new sensory-motor intuition of the perspectivist nature of the information universe. Here we have to distinguish between the conceptual perspective and the contextual perspective.
The conceptual perspective organizes the relationships among terms, sentences and texts in IEML so that each of these semantic units can be processed as a point of view, or a virtual “centre” of the ecosystems of ideas, organizing the other units around it according to the types of relationships it has with them and their distance from it.
In IEML, the elementary units of meaning are terms, which are organized in the IEML dictionary (optimized for laptops + Chrome) in paradigms, that is, in systems of semantic relationships among terms. In the IEML dictionary, each term organizes the other terms of the same paradigm around it according to its semantic relationships with them. The different paradigms of the IEML dictionary are in principle independent of each other and none has precedence over the others a priori. Each of them can, in principle, be used to filter or categorize any set of data.
The sentences, texts and hypertexts in IEML represent paths between the terms of various paradigms, and these paths in turn organize the other paths around them according to their relationships and semantic proximity in the ecosystems of ideas. It will be possible to display this cascade of semantic perspectives and points of view using three-dimensional holograms in an immersive interactive mode.
Let us now examine the contextual perspective, which places in symmetry not the concepts within an ecosystem of ideas, but the ecosystems of ideas themselves, that is, the way in which various communities at different times categorize and evaluate data. It will thus be possible to display and explore the same set of data interactively according to the meaning and value it has for a large number of communities.
Reflexive memory, perspectivist intuition, interoperable and transparent communication together produce a cognitive augmentation characteristic of algorithmic intelligence, an augmentation more powerful than that of today.

Interoperable and transparent communication

The interoperability of communication will first concern the semantic compatibility of various theories, disciplines, universes of practices and cultures that will be able to be translated into IEML and will thus become not only comparable but also capable of exchanging concepts and operating rules without loss of their uniqueness. Semantic interoperability will also cover the automatic translation of IEML concepts into natural languages. Thanks to this pivot language, any semantic network in any natural language will be translated automatically into any other natural language. As a result, through the IEML code, people will be able to transmit and receive messages and categorize data in their own languages while communicating with people who use other languages. Here again, we need to think about cultural interoperability (communication in spite of differences in conceptual organization) and linguistic interoperability (communication in spite of differences in language) together; they will reinforce each other as a result of semantic coding.

Emergence

Emergence happens through an interdependant circulation of information between two levels of complexity. A code translates and betrays information in both directions: bottom-up and top-down.

Nature

According to our model, human collective intelligence emerges from natural evolution. The lower level of quantic complexity translates into a higher level of molecular complexity through the atomic stabilization and coding. There are no more than 120 atomic elements that explain the complexity of matter by their connections and reactions. The emergence of the next level of complexity – life – comes from the genetic code that is used by organisms as a trans-generational memory. Communication in neuronal networks translates organic life into conscious phenomena, including sense data, pleasure and pain, desire, etc. So emerges the animal life. Let’s note that organic life is intrinsically ecosystemic and that animals have developed many forms of social or collective intelligence. The human level emerges through the symbolic code : language, music, images, rituals and all the complexity of culture. It is only thank to symbols that we are able to conceptualize phenomena and think reflexively about what we do and think. Symbolic systems are all conventional but the human species is symbolic by nature, so to speak. Here, collective intelligence reaches a new level of complexity because it is based on collaborative symbol manipulation.

Culture

[WARNING: the next 5 paragraphs can be found in “collective intelligence for educators“, if you have already read them, go to the next slide: “algorithmic medium”] The above slide describes the successive steps in the emergence of symbolic manipulation. As for the previous slide, each new layer of cultural complexity emerges from the creation of a coding system.

During the longest part of human history, the knowledge was only embedded in narratives, rituals and material tools. The first revolution in symbolic manipulation is the invention of writing with symbols endowed with the ability of self-conservation. This leads to a remarquable augmentation of social memory and to the emergence of new forms of knowledge. Ideas were reified on an external surface, which is an important condition for critical thinking. A new kind of systematic knowledge was developed: hermeneutics, astronomy, medicine, architecture (including geometry), etc.

The second revolution optimizes the manipulation of symbols like the invention of the alphabet (phenician, hebrew, greek, roman, arab, cyrilic, korean, etc.), the chinese rational ideographies, the indian numeration system by position with a zero, paper and the early printing techniques of China and Korea. The literate culture based on the alphabet (or rational ideographies) developed critical thinking further and gave birth to philosophy. At this stage, scholars attempted to deduce knowledge from observation and deduction from first principles. There was a deliberate effort to reach universality, particularly in mathematics, physics and cosmology.

The third revolution is the mecanization and the industrialization of the reproduction and diffusion of symbols, like the printing press, disks, movies, radio, TV, etc. This revolution supported the emergence of the modern world, with its nation states, industries and its experimental mathematized natural sciences. It was only in the typographic culture, from the 16th century, that natural sciences took the shape that we currently enjoy: systematic observation or experimentation and theories based on mathematical modeling. From the decomposition of theology and philosophy emerged the contemporary humanities and social sciences. But at this stage human science was still fragmented by disciplines and incompatible theories. Moreover, its theories were rarely mathematized or testable.

We are now at the beginning of a fourth revolution where an ubiquitous and interconnected infosphere is filled with symbols – i.e. data – of all kinds (music, voice, images, texts, programs, etc.) that are being automatically transformed. With the democratization of big data analysis, the next generations will see the advent of a new scientific revolution… but this time it will be in the humanities and social sciences. The new human science will be based on the wealth of data produced by human communities and a growing computation power. This will lead to reflexive collective intelligence, where people will appropriate (big) data analysis and where subjects and objects of knowledge will be the human communities themselves.

Algo-medium

Let’s have a closer look at the algorithmic medium. Four layers have been added since the middle of the 20th century. Again, we observe the progressive invention of new coding systems, mainly aimed at the addressing of processors, data and meta-data.

The first layer is the invention of the automatic digital computer itself. We can describe computation as « processing on data ». It is self-evident that computation cannot be programmed if we don’t have a very precise addressing system for the data and for the specialized operators/processors that will transform the data. At the beginning these addressing systems were purely local and managed by operating systems.

The second layer is the emergence of a universal addressing system for computers, the Internet protocol, that allows for exchange of data and collaborative computing across the telecommunication network.

The third layer is the invention of a universal system for the addressing and displaying of data (URLs, http, html). Thank to this universal addressing of data, the World Wide Web is a hypertextual global database that we all create and share. It is obvious that the Web has had a deep social, cultural and economic impact in the last twenty years.

The construction of the algorithmic medium is ongoing. We are now ready to add a fourth layer of addressing and, this time, it will be a universal addressing system for semantic metadata. Why? First, we are still unable to resolve the problem of semantic interoperability across languages, classifications and ontologies. And secondly, except for some approximative statistical and logical methods, we are still unable to compute semantic relations, including distances and differences. This new symbolic system will be a key element to a future scientific revolution in the humanities and social sciences, leading to a new kind of reflexive collective intelligence for our species. Moreover, it will pave the way for the emergence of a new scientific cosmos – not a physical one but a cosmos of the mind that we will build and explore collaboratively. I want to strongly underline here that the semantic categorization of data will stay in the hands of people. We will be able to categorize the data as we want, from many different point of views. All that is required is that we use the same code. The description itself will be free.

Algo-intel

Let’s examine now the future emerging algorithmic intelligence. This new level of symbolic manipulation will be operated and shared in a mixed environment combining virtual worlds and augmented realities. The two lower levels of the above slide represent the current internet: an interaction between the « internet of things » and the « clouds » where all the data converge in an ubiquitous infosphere… The two higher levels, the « semantic sensorium » and the « reflexive collective intelligence » depict the human condition that will unfold in the future.

The things are material, localized realities that have GPS addresses. Here we speak about the smart territories, cities, buildings, machines, robots and all the mobile gadgets (phones, tablets, watches, etc.) that we can wear. Through binary code, the things are in constant interaction with the ubiquitous memory in the clouds. Streams of data and information processing reverberate between the things and the clouds.

When the data will be coded by a computable universal semantic addressing system, the data in the clouds will be projected automatically into a new sensorium. In this 3D, immersive and dynamic virtual environment we will be able to explore through our senses the abstract relationships between the people, the places and the meaning of digital information. I’m not speaking here of a representation, reproduction or imitation of the material space, like, for example, in Second Life. We have to imagine something completely different: a semantic sphere where the cognitive processes of human communities will be modeled. This semantic sphere will empower all its users. Search, knowledge exploration, data analysis and synthesis, collaborative learning and collaborative data curation will be multiplied and enhanced by the new interoperable semantic computing.

We will get reflexive collective intelligence thank to a scientific computable and transparent modeling of cognition from real data. This modeling will be based on the semantic code, that provides the « coordinate system » of the new cognitive cosmos. Of course, people will not be forced to understand the details of this semantic code. They will interact in the new sensorium through their prefered natural language (the linguistic codes of the above slide) and their favorite multimedia interfaces. The translation between different languages and optional interface metaphors will be automatic. The important point is that people will observe, analyze and map dynamically their own personal and collective cognitive processes. Thank to this new reflexivity, we will improve our collaborative learning processes and the collaborative monitoring and control of our physical environments. And this will boost human development!

Collective-Intelligence

The above slide represents the workings of a collective intelligence oriented towards human development. In this model, collective intelligence emerges from an interaction between two levels: virtual and actual. The actual is addressed in space and time while the virtual is latent, potential or intangible. The two levels function and communicate through several symbolic codes. In any coding system, there are coding elements (signs), coded references (things) and coders (being). This is why both actual and virtual levels can be conceptually analysed into three kinds of networks: signs, beings and things.

The actual human development can be analysed into a sphere of messages (signs), a sphere of people (beings) and a sphere of equipments – this last word understood in the largest possible sense – (things). Of course, the three spheres are interdependent.

The virtual human development is analysed into a sphere of knowledge (signs), a sphere of ethics (being) and a sphere of power (things). Again, the three spheres are interdependent.

Each of the six spheres is further analysed into three subdivisions, corresponding to the sub-rows on the slide. The mark S (sign) points to the abstract factors, the mark B (being) indicates the affective dimensions and the mark T (thing) shows the concrete aspects of each sphere.

All the realities described in the above table are interdependent following the actual/virtual and the sign/being/thing dialectics. Any increase of decrease in one « cell » will have consequences in other cells. This is just an example of the many ways collective intelligence will be represented, monitored and made reflexive in the semantic sensorium…

To dig into the philosophical concept of algorithmic intelligence go there

Ancient-Hands-Argentina

Proper quotation: « The Philosophical Concept of Algorithmic Intelligence », Spanda Journal special issue on “Collective Intelligence”, V (2), December 2014, p. 17-25. The original text can be found for free online at  Spanda

“Transcending the media, airborne machines will announce the voice of the many. Still indiscernible, cloaked in the mists of the future, bathing another humanity in its murmuring, we have a rendezvous with the over-language.” Collective Intelligence, 1994, p. xxviii.

Twenty years after Collective Intelligence

This paper was written in 2014, twenty years after L’intelligence collective [the original French edition of Collective Intelligence].[2] The main purpose of Collective Intelligence was to formulate a vision of a cultural and social evolution that would be capable of making the best use of the new possibilities opened up by digital communication. Long before the success of social networks on the Web,[3] I predicted the rise of “engineering the social bond.” Eight years before the founding of Wikipedia in 2001, I imagined an online “cosmopedia” structured in hypertext links. When the digital humanities and the social media had not even been named, I was calling for an epistemological and methodological transformation of the human sciences. But above all, at a time when less than one percent of the world’s population was connected,[4] I was predicting (along with a small minority of thinkers) that the Internet would become the centre of the global public space and the main medium of communication, in particular for the collaborative production and sharing of knowledge and the dissemination of news.[5] In spite of the considerable growth of interactive digital communication over the past twenty years, we are still far from the ideal described in Collective Intelligence. It seemed to me already in 1994 that the anthropological changes under way would take root and inaugurate a new phase in the human adventure only if we invented what I then called an “over-language.” How can communication readily reach across the multiplicity of dialects and cultures? How can we map the deluge of digital data, order it around our interests and extract knowledge from it? How can we master the waves, currents and depths of the software ocean? Collective Intelligence envisaged a symbolic system capable of harnessing the immense calculating power of the new medium and making it work for our benefit. But the over-language I foresaw in 1994 was still in the “indiscernible” period, shrouded in “the mists of the future.” Twenty years later, the curtain of mist has been partially pierced: the over-language now has a name, IEML (acronym for Information Economy MetaLanguage), a grammar and a dictionary.[6]

Reflexive collective intelligence

Collective intelligence drives human development, and human development supports the growth of collective intelligence. By improving collective intelligence we can place ourselves in this feedback loop and orient it in the direction of a self-organizing virtuous cycle. This is the strategic intuition that has guided my research. But how can we improve collective intelligence? In 1994, the concept of digital collective intelligence was still revolutionary. In 2014, this term is commonly used by consultants, politicians, entrepreneurs, technologists, academics and educators. Crowdsourcing has become a common practice, and knowledge management is now supported by the decentralized use of social media. The interconnection of humanity through the Internet, the development of the knowledge economy, the rush to higher education and the rise of cloud computing and big data are all indicators of an increase in our cognitive power. But we have yet to cross the threshold of reflexive collective intelligence. Just as dancers can only perfect their movements by reflecting them in a mirror, just as yogis develop awareness of their inner being only through the meditative contemplation of their own mind, collective intelligence will only be able to set out on the path of purposeful learning and thus move on to a new stage in its growth by achieving reflexivity. It will therefore need to acquire a mirror that allows it to observe its own cognitive processes. Be careful! Collective intelligence does not and will not have autonomous consciousness: when I talk about reflexive collective intelligence, I mean that human individuals will have a clearer and better-shared knowledge than they have today of the collective intelligence in which they participate, a knowledge based on transparent principles and perfectible scientific methods.

The key: A complete modelling of language

But how can a mirror of collective intelligence be constructed? It is clear that the context of reflection will be the algorithmic medium or, to put it another way, the Internet, the calculating power of cloud computing, ubiquitous communication and distributed interactive mobile interfaces. Since we can only reflect collective intelligence in the algorithmic medium, we must yield to the nature of that medium and have a calculable model of our intelligence, a model that will be fed by the flows of digital data from our activities. In short, we need a mathematical (with calculable models) and empirical (based on data) science of collective intelligence. But, once again, is such a science possible? Since humanity is a species that is highly social, its intelligence is intrinsically social, or collective. If we had a mathematical and empirical science of human intelligence in general, we could no doubt derive a science of collective intelligence from it. This leads us to a major problem that has been investigated in the social sciences, the human sciences, the cognitive sciences and artificial intelligence since the twentieth century: is a mathematized science of human intelligence possible? It is language or, to put it another way, symbolic manipulation that distinguishes human cognition. We use language to categorize sensory data, to organize our memory, to think, to communicate, to carry out social actions, etc. My research has led me to the conclusion that a science of human intelligence is indeed possible, but on the condition that we solve the problem of the mathematical modelling of language. I am speaking here of a complete scientific modelling of language, one that would not be limited to the purely logical and syntactic aspects or to statistical correlations of corpora of texts, but would be capable of expressing semantic relationships formed between units of meaning, and doing so in an algebraic, generative mode.[7] Convinced that an algebraic model of semantics was the key to a science of intelligence, I focused my efforts on discovering such a model; the result was the invention of IEML.[8] IEML—an artificial language with calculable semantics—is the intellectual technology that will make it possible to find answers to all the above-mentioned questions. We now have a complete scientific modelling of language, including its semantic aspects. Thus, a science of human intelligence is now possible. It follows, then, that a mathematical and empirical science of collective intelligence is possible. Consequently, a reflexive collective intelligence is in turn possible. This means that the acceleration of human development is within our reach.

The scientific file: The Semantic Sphere

I have written two volumes on my project of developing the scientific framework for a reflexive collective intelligence, and I am currently writing the third. This trilogy can be read as the story of a voyage of discovery. The first volume, The Semantic Sphere 1 (2011),[9] provides the justification for my undertaking. It contains the statement of my aims, a brief intellectual autobiography and, above all, a detailed dialogue with my contemporaries and my predecessors. With a substantial bibliography,[10] that volume presents the main themes of my intellectual process, compares my thoughts with those of the philosophical and scientific tradition, engages in conversation with the research community, and finally, describes the technical, epistemological and cultural context that motivated my research. Why write more than four hundred pages to justify a program of scientific research? For one very simple reason: no one in the contemporary scientific community thought that my research program had any chance of success. What is important in computer science and artificial intelligence is logic, formal syntax, statistics and biological models. Engineers generally view social sciences such as sociology or anthropology as nothing but auxiliary disciplines limited to cosmetic functions: for example, the analysis of usage or the experience of users. In the human sciences, the situation is even more difficult. All those who have tried to mathematize language, from Leibniz to Chomsky, to mention only the greatest, have failed, achieving only partial results. Worse yet, the greatest masters, those from whom I have learned so much, from the semiologist Umberto Eco[11] to the anthropologist Levi-Strauss,[12] have stated categorically that the mathematization of language and the human sciences is impracticable, impossible, utopian. The path I wanted to follow was forbidden not only by the habits of engineers and the major authorities in the human sciences but also by the nearly universal view that “meaning depends on context,”[13] unscrupulously confusing mathematization and quantification, denouncing on principle, in a “knee jerk” reaction, the “ethnocentric bias” of any universalist approach[14] and recalling the “failure” of Esperanto.[15] I have even heard some of the most agnostic speak of the curse of Babel. It is therefore not surprising that I want to make a strong case in defending the scientific nature of my undertaking: all explorers have returned empty-handed from this voyage toward mathematical language, if they returned at all.

The metalanguage: IEML

But one cannot go on forever announcing one’s departure on a voyage: one must set forth, navigate . . . and return. The second volume of my trilogy, La grammaire d’IEML,[16] contains the very technical account of my journey from algebra to language. In it, I explain how to construct sentences and texts in IEML, with many examples. But that 150-page book also contains 52 very dense pages of algorithms and mathematics that show in detail how the internal semantic networks of that artificial language can be calculated and translated automatically into natural languages. To connect a mathematical syntax to a semantics in natural languages, I had to, almost single-handed,[17] face storms on uncharted seas, to advance across the desert with no certainty that fertile land would be found beyond the horizon, to wander for twenty years in the convoluted labyrinth of meaning. But by gradually joining sign, being and thing in turn in the sense of the virtual and actual, I finally had my Ariadne’s thread, and I made a map of the labyrinth, a complicated map of the metalanguage, that “Northwest Passage”[18] where the waters of the exact sciences and the human sciences converged. I had set my course in a direction no one considered worthy of serious exploration since the crossing was thought impossible. But, against all expectations, my journey reached its goal. The IEML Grammar is the scientific proof of this. The mathematization of language is indeed possible, since here is a mathematical metalanguage. What is it exactly? IEML is an artificial language with calculable semantics that puts no limits on the possibilities for the expression of new meanings. Given a text in IEML, algorithms reconstitute the internal grammatical and semantic network of the text, translate that network into natural languages and calculate the semantic relationships between that text and the other texts in IEML. The metalanguage generates a huge group of symmetric transformations between semantic networks, which can be measured and navigated at will using algorithms. The IEML Grammar demonstrates the calculability of the semantic networks and presents the algorithmic workings of the metalanguage in detail. Used as a system of semantic metadata, IEML opens the way to new methods for analyzing large masses of data. It will be able to support new forms of translinguistic hypertextual communication in social media, and will make it possible for conversation networks to observe and perfect their own collective intelligence. For researchers in the human sciences, IEML will structure an open, universal encyclopedic library of multimedia data that reorganizes itself automatically around subjects and the interests of its users.

A new frontier: Algorithmic Intelligence

Having mapped the path I discovered in La grammaire d’IEML, I will now relate what I saw at the end of my journey, on the other side of the supposedly impassable territory: the new horizons of the mind that algorithmic intelligence illuminates. Because IEML is obviously not an end in itself. It is only the necessary means for the coming great digital civilization to enable the sun of human knowledge to shine more brightly. I am talking here about a future (but not so distant) state of intelligence, a state in which capacities for reflection, creation, communication, collaboration, learning, and analysis and synthesis of data will be infinitely more powerful and better distributed than they are today. With the concept of Algorithmic Intelligence, I have completed the risky work of prediction and cultural creation I undertook with Collective Intelligence twenty years ago. The contemporary algorithmic medium is already characterized by digitization of data, automated data processing in huge industrial computing centres, interactive mobile interfaces broadly distributed among the population and ubiquitous communication. We can make this the medium of a new type of knowledge—a new episteme[19]—by adding a system of semantic metadata based on IEML. The purpose of this paper is precisely to lay the philosophical and historical groundwork for this new type of knowledge.

Philosophical genealogy of algorithmic intelligence

The three ages of reflexive knowledge

Since my project here involves a reflexive collective intelligence, I would like to place the theme of reflexive knowledge in its historical and philosophical context. As a first approximation, reflexive knowledge may be defined as knowledge knowing itself. “All men by nature desire to know,” wrote Aristotle, and this knowledge implies knowledge of the self.[20] Human beings have no doubt been speculating about the forms and sources of their own knowledge since the dawn of consciousness. But the reflexivity of knowledge took a decisive step around the middle of the first millennium BCE,[21] during the period when the Buddha, Confucius, the Hebrew prophets, Socrates and Zoroaster (in alphabetical order) lived. These teachers involved the entire human race in their investigations: they reflected consciousness from a universal perspective. This first great type of systematic research on knowledge, whether philosophical or religious, almost always involved a divine ideal, or at least a certain “relation to Heaven.” Thus we may speak of a theosophical age of reflexive knowledge. I will examine the Aristotelian lineage of this theosophical consciousness, which culminated in the concept of the agent intellect. Starting in the sixteenth century in Europe—and spreading throughout the world with the rise of modernity—there was a second age of reflection on knowledge, which maintained the universal perspective of the previous period but abandoned the reference to Heaven and confined itself to human knowledge, with its recognized limits but also its rational ideal of perfectibility. This was the second age, the scientific age, of reflexive knowledge. Here, the investigation follows two intertwined paths: one path focusing on what makes knowledge possible, the other on what limits it. In both cases, knowledge must define its transcendental subject, that is, it must discover its own determinations. There are many signs in 2014 indicating that in the twenty-first century—around the point where half of humanity is connected to the Internet—we will experience a third stage of reflexive knowledge. This “version 3.0” will maintain the two previous versions’ ideals of universality and scientific perfectibility but will be based on the intensive use of technology to augment and reflect systematically our collective intelligence, and therefore our capacities for personal and social learning. This is the coming technological age of reflexive knowledge with its ideal of an algorithmic intelligence. The brief history of these three modalities—theosophical, scientific and technological—of reflexive knowledge can be read as a philosophical genealogy of algorithmic intelligence.

The theosophical age and its agent intellect

A few generations earlier, Socrates might have been a priest in the circle around the Pythia; he had taken the famous maxim “Know thyself” from the Temple of Apollo at Delphi. But in the fifth century BCE in Athens, Socrates extended the Delphic injunction in an unexpected way, introducing dialectical inquiry. He asked his contemporaries: What do you think? Are you consistent? Can you justify what you are saying about courage, justice or love? Could you repeat it seriously in front of a little group of intelligent or curious citizens? He thus opened the door to a new way of knowing one’s own knowledge, a rational expansion of consciousness of self. His main disciple, Plato, followed this path of rigorous questioning of the unthinking categorization of reality, and finally discovered the world of Ideas. Ideas for Plato are intellectual forms that, unlike the phenomena they categorize, do not belong to the world of Becoming. These intelligible forms are the original essences, archetypes beyond reality, which project into phenomenal time and space all those things that seem to us to be truly real because they are tangible, but that are actually only pale copies of the Ideas. We would say today that our experience is mainly determined by our way of categorizing it. Plato taught that humanity can only know itself as an intelligent species by going back to the world of Ideas and coming into contact with what explains and motivates its own knowledge. Aristotle, who was Plato’s student and Alexander the Great’s tutor, created a grand encyclopedic synthesis that would be used as a model for eighteen centuries in a multitude of cultures. In it, he integrates Plato’s discovery of Ideas with the sum of knowledge of his time. He places at the top of his hierarchical cosmos divine thought knowing itself. And in his Metaphysics,[22] he defines the divinity as “thought thinking itself.” This supreme self-reflexive thought was for him the “prime mover” that inspires the eternal movement of the cosmos. In De Anima,[23] his book on psychology and the theory of knowledge, he states that, under the effect of an agent intellect separate from the body, the passive intellect of the individual receives intelligible forms, a little like the way the senses receive sensory forms. In thinking these intelligible forms, the passive intellect becomes one with its objects and, in so doing, knows itself. Starting from the enigmatic propositions of Aristotle’s theology and psychology, a whole lineage of Peripatetic and Neo-Platonic philosophers—first “pagans,” then Muslims, Jews and Christians—developed the discipline of noetics, which speculates on the divine intelligence, its relation to human intelligence and the type of reflexivity characteristic of intelligence in general.[24] According to the masters of noetics, knowledge can be conceptually divided into three aspects that, in reality, are indissociable and complementary:

  • the intellect,or the knowing subject
  • the intelligence,or the operation of the subject
  • the intelligible,or what is known—or can be known—by the subject by virtue of its operation

From a theosophical perspective, everything that happens takes place in the unity of a self-reflexive divine thought, or (in the Indian tradition) in the consciousness of an omniscient Brahman or Buddha, open to infinity. In the Aristotelian tradition, Avicenna, Maimonides and Albert the Great considered that the identity of the intellect, the intelligence and the intelligible was achieved eternally in God, in the perfect reflexivity of thought thinking itself. In contrast, it was clear to our medieval theosophists that in the case of human beings, the three aspects of knowledge were neither complete nor identical. Indeed, since the passive intellect knows itself only through the intermediary of its objects, and these objects are constantly disappearing and being replaced by others, the reflexive knowledge of a finite human being can only be partial and transitory. Ultimately, human knowledge could know itself only if it simultaneously knew, completely and enduringly, all its objects. But that, obviously, is reserved only for the divinity. I should add that the “one beyond the one” of the neo-Platonist Plotinus and the transcendent deity of the Abrahamic traditions are beyond the reach of the human mind. That is why our theosophists imagined a series of mediations between transcendence and finitude. In the middle of that series, a metaphysical interface provides communication between the unimaginable and inaccessible deity and mortal humanity dispersed in time and space, whose living members can never know—or know themselves—other than partially. At this interface, we find the agent intellect, which is separate from matter in Aristotle’s psychology. The agent intellect is not limited—in the realm of time—to sending the intelligible categories that inform the human passive intellect; it also determines—in the realm of eternity—the maximum limit of what the human race can receive of the universal and perfectly reflexive knowledge of the divine. That is why, according to the medieval theosophists, the best a mortal intelligence can do to approach complete reflexive knowledge is to contemplate the operation in itself of the agent intellect that emanates from above and go back to the source through it. In accordance with this regulating ideal of reflexive knowledge, living humanity is structured hierarchically, because human beings are more or less turned toward the illumination of the agent intellect. At the top, prophets and theosophists receive a bright light from the agent intellect, while at the bottom, human beings turned toward coarse material appetites receive almost nothing. The influx of intellectual forms is gradually obscured as we go down the scale of degree of openness to the world above.

The scientific age and its transcendental subject

With the European Renaissance, the use of the printing press, the construction of new observation instruments, and the development of mathematics and experimental science heralded a new era. Reflection on knowledge took a critical turn with Descartes’s introduction of radical doubt and the scientific method, in accordance with the needs of educated Europe in the seventeenth century. God was still present in the Cartesian system, but He was only there, ultimately, to guarantee the validity of the efforts of human scientific thought: “God is not a deceiver.”[25] The fact remains that Cartesian philosophy rests on the self-reflexive edge, which has now moved from the divinity to the mortal human: “I think, therefore I am.”[26] In the second half of the seventeenth century, Spinoza and Leibniz received the critical scientific rationalism developed by Descartes, but they were dissatisfied with his dualism of thought (mind) and extension (matter). They therefore attempted, each in his own way, to constitute reflexive knowledge within the framework of coherent monism. For Spinoza, nature (identified with God) is a unique and infinite substance of which thought and extension are two necessary attributes among an infinity of attributes. This strict ontological monism is counterbalanced by a pluralism of expression, because the unique substance possesses an infinity of attributes, and each attribute, an infinity of modes. The summit of human freedom according to Spinoza is the intellectual love of God, that is, the most direct and intuitive possible knowledge of the necessity that moves the nature to which we belong. For Leibniz, the world is made up of monads, metaphysical entities that are closed but are capable of an inner perception in which the whole is reflected from their singular perspective. The consistency of this radical pluralism is ensured by the unique, infinite divine intelligence that has considered all possible worlds in order to create the best one, which corresponds to the most complex—or the richest—of the reciprocal reflections of the monads. As for human knowledge—which is necessarily finite—its perfection coincides with the clearest possible reflection of a totality that includes it but whose unity is thought only by the divine intelligence. After Leibniz and Spinoza, the eighteenth century saw the growth of scientific research, critical thought and the educational practices of the Enlightenment, in particular in France and the British Isles. The philosophy of the Enlightenment culminated with Kant, for whom the development of knowledge was now contained within the limits of human reason, without reference to the divinity, even to envelop or guarantee its reasoning. But the ideal of reflexivity and universality remained. The issue now was to acquire a “scientific” knowledge of human intelligence, which could not be done without the representation of knowledge to itself, without a model that would describe intelligence in terms of what is universal about it. This is the purpose of Kantian transcendental philosophy. Here, human intelligence, armed with its reason alone, now faces only the phenomenal world. Human intelligence and the phenomenal world presuppose each other. Intelligence is programmed to know sensory phenomena that are necessarily immersed in space and time. As for phenomena, their main dimensions (space, time, causality, etc.) correspond to ways of perceiving and understanding that are specific to human intelligence. These are forms of the transcendental subject and not intrinsic characteristics of reality. Since we are confined within our cognitive possibilities, it is impossible to know what things are “in themselves.” For Kant, the summit of reflexive human knowledge is in a critical awareness of the extension and the limits of our possibility of knowing. Descartes, Spinoza, Leibniz, the English and French Enlightenment, and Kant accomplished a great deal in two centuries, and paved the way for the modern philosophy of the nineteenth and twentieth centuries. A new form of reflexive knowledge grew, spread, and fragmented into the human sciences, which mushroomed with the end of the monopoly of theosophy. As this dispersion occurred, great philosophers attempted to grasp reflexive knowledge in its unity. The reflexive knowledge of the scientific era neither suppressed nor abolished reflexive knowledge of the theosophical type, but it opened up a new domain of legitimacy of knowledge, freed of the ideal of divine knowledge. This de jure separation did not prevent de facto unions, since there was no lack of religious scholars or scholarly believers. Modern scientists could be believers or non-believers. Their position in relation to the divinity was only a matter of motivation. Believers loved science because it revealed the glory of the divinity, and non-believers loved it because it explained the world without God. But neither of them used as arguments what now belonged only to their private convictions. In the human sciences, there were systematic explorations of the determinations of human existence. And since we are thinking beings, the determinations of our existence are also those of our thought. How do the technical, historical, economic, social and political conditions in which we live form, deform and set limits on our knowledge? What are the structures of our biology, our language, our symbolic systems, our communicative interactions, our psychology and our processes of subjectivation? Modern thought, with its scientific and critical ideal, constantly searches for the conditions and limits imposed on it, particularly those that are as yet unknown to it, that remain in the shadows of its consciousness. It seeks to discover what determines it “behind its back.” While the transcendental subject described by Kant in his Critique of Pure Reason fixed the image a great mind had of it in the late eighteenth century, modern philosophy explores a transcendental subject that is in the process of becoming, continually being re-examined and more precisely defined by the human sciences, a subject immersed in the vagaries of cultures and history, emerging from its unconscious determinations and the techno-symbolic mechanisms that drive it. I will now broadly outline the figure of the transcendental subject of the scientific era, a figure that re-examines and at the same time transforms the three complementary aspects of the agent intellect.

  • The Aristotelian intellect becomes living intelligence. This involves the effective cognitive activities of subjects, what is experienced spontaneously in time by living, mortal human beings.
  • The intelligence becomes scientific investigation. I use this term to designate all undertakings by which the living intelligence becomes scientifically intelligible, including the technical and symbolic tools, the methods and the disciplines used in those undertakings.
  • The intelligible becomes the intelligible intelligence, which is the image of the living intelligence that is produced through scientific and critical investigation.

An evolving transcendental subject emerges from this reflexive cycle in which the living intelligence contemplates its own image in the form of a scientifically intelligible intelligence. Scientific investigation here is the internal mirror of the transcendental subjectivity, the mediation through which the living intelligence observes itself. It is obviously impossible to confuse the living intelligence and its scientifically intelligible image, any more than one can confuse the map and the territory, or the experience and its description. Nor can one confuse the mirror (scientific investigation) with the being reflected in it (the living intelligence), nor with the image that appears in the mirror (the intelligible intelligence). These three aspects together form a dynamic unit that would collapse if one of them were eliminated. While the living intelligence would continue to exist without a mirror or scientific image, it would be very much diminished. It would have lost its capacity to reflect from a universal perspective. The creative paradox of the intellectual reflexivity of the scientific age may be formulated as follows. It is clear, first of all, that the living intelligence is truly transformed by scientific investigation, since the living intelligence that knows its image through a certain scientific investigation is not the same (does not have the same experience) as the one that does not know it, or that knows another image, the result of another scientific investigation. But it is just as clear, by definition, that the living intelligence reflects itself in the intelligible image presented to it through scientific knowledge. In other words, the living intelligence is equally dependent on the scientific and critical investigation that produces the intelligible image in which it is reflected. When we observe our physical appearance in a mirror, the image in the mirror in no way changes our physical appearance, only the mental representation we have of it. However, the living intelligence cannot discover its intelligible image without including the reflexive process itself in its experience, and without at the same time being changed. In short, a critical science that explores the limits and determinations of the knowing subject does not only reflect knowledge—it increases it. Thus the modern transcendental subject is—by its very nature—evolutionary, participating in a dynamic of growth. In line with this evolutionary view of the scientific age, which contrasts with the fixity of the previous age, the collectivity that possesses reflexive knowledge is no longer a theosophical hierarchy oriented toward the agent intellect but a republic of letters oriented toward the augmentation of human knowledge, a scientific community that is expanding demographically and is organized into academies, learned societies and universities. While the agent intellect looked out over a cosmos emanating from eternity, in analog resonance with the human microcosm, the transcendental subject explores a universe infinitely open to scientific investigation, technical mastery and political liberation.

The technological age and its algorithmic intelligence

Reflexive knowledge has, in fact, always been informed by some technology, since it cannot be exercised without symbolic tools and thus the media that support those tools. But the next age of reflexive knowledge can properly be called technological because the technical augmentation of cognition is explicitly at the centre of its project. Technology now enters the loop of reflexive consciousness as the agent of the acceleration of its own augmentation. This last point was no doubt glimpsed by a few pre–twentieth century philosophers, such as Condorcet in the eighteenth century, in his posthumous book of 1795, Sketch for a Historical Picture of the Progress of the Human Mind. But the truly technological dimension of reflexive knowledge really began to be thought about fully only in the twentieth century, with Pierre Teilhard de Chardin, Norbert Wiener and Marshall McLuhan, to whom we should also add the modest genius Douglas Engelbart. The regulating ideal of the reflexive knowledge of the theosophical age was the agent intellect, and that of the scientific-critical age was the transcendental subject. In continuity with the two preceding periods, the reflexive knowledge of the technological age will be organized around the ideal of algorithmic intelligence, which inherits from the agent intellect its universality or, in other words, its capacity to unify humanity’s reflexive knowledge. It also inherits its power to be reflected in finite intelligences. But, in contrast with the agent intellect, instead of descending from eternity, it emerges from the multitude of human actions immersed in space and time. Like the transcendental subject, algorithmic intelligence is rational, critical, scientific, purely human, evolutionary and always in a state of learning. But the vocation of the transcendental subject was to reflexively contain the human universe. However, the human universe no longer has a recognizable face. The “death of man” announced by Foucault[27] should be understood in the sense of the loss of figurability of the transcendental subject. The labyrinth of philosophies, methodologies, theories and data from the human sciences has become inextricably complicated. The transcendental subject has not only been dissolved in symbolic structures or anonymous complex systems, it is also fragmented in the broken mirror of the disciplines of the human sciences. It is obvious that the technical medium of a new figure of reflexive knowledge will be the Internet, and more generally, computer science and ubiquitous communication. But how can symbol-manipulating automata be used on a large scale not only to reunify our reflexive knowledge but also to increase the clarity, precision and breadth of the teeming diversity enveloped by our knowledge? The missing link is not only technical, but also scientific. We need a science that grasps the new possibilities offered by technology in order to give collective intelligence the means to reflect itself, thus inaugurating a new form of subjectivity. As the groundwork of this new science—which I call computational semantics—IEML makes use of the self-reflexive capacity of language without excluding any of its functions, whether they be narrative, logical, pragmatic or other. Computational semantics produces a scientific image of collective intelligence: a calculated intelligence that will be able to be explored both as a simulated world and as a distributed augmented reality in physical space. Scientific change will generate a phenomenological change,[28] since ubiquitous multimedia interaction with a holographic image of collective intelligence will reorganize the human sensorium. The last, but not the least, change: social change. The community that possessed the previous figure of reflexive knowledge was a scientific community that was still distinct from society as a whole. But in the new figure of knowledge, reflexive collective intelligence emerges from any human group. Like the previous figures—theosophical and scientific—of reflexive knowledge, algorithmic intelligence is organized in three interdependent aspects.

  • Reflexive collective intelligence represents the living intelligence, the intellect or soul of the great future digital civilization. It may be glimpsed by deciphering the signs of its approach in contemporary reality.
  • Computational semantics holds up a technical and scientific mirror to collective intelligence, which is reflected in it. Its purpose is to augment and reflect the living intelligence of the coming civilization.
  • Calculated intelligence, finally, is none other than the scientifically knowable image of the living intelligence of digital civilization. Computational semantics constructs, maintains and cultivates this image, which is that of an ecosystem of ideas coming out of the human activity in the algorithmic medium and can be explored in sensory-motor mode.

In short, in the emergent unity of algorithmic intelligence, computational semantics calculates the cognitive simulation that augments and reflects the collective intelligence of the coming civilization.

[1] Professor at the University of Ottawa

[2] And twenty-three years after L’idéographie dynamique (Paris: La Découverte, 1991).

[3] And before the WWW itself, which would become a public phenomenon only in 1994 with the development of the first browsers such as Mosaic. At the time when the book was being written, the Web still existed only in the mind of Tim Berners-Lee.

[4] Approximately 40% in 2014 and probably more than half in 2025.

[5] I obviously do not claim to be the only “visionary” on the subject in the early 1990s. The pioneering work of Douglas Engelbart and Ted Nelson and the predictions of Howard Rheingold, Joël de Rosnay and many others should be cited.

[6] See The basics of IEML (on line at: http://wp.me/P3bDiO-9V )

[7] Beyond logic and statistics.

[8] IEML is the acronym for Information Economy MetaLanguage. See La grammaire d’IEML (On line http://wp.me/P3bDiO-9V ) [9] The Semantic Sphere 1: Computation, Cognition and Information Economy (London: ISTE, 2011; New York: Wiley, 2011).

[10] More than four hundred reference books.

[11] Umberto Eco, The Search for the Perfect Language (Oxford: Blackwell, 1995).

[12] “But more madness than genius would be required for such an enterprise”: Claude Levi-Strauss, The Savage Mind (University of Chicago Press, 1966), p. 130.

[13] Which is obviously true, but which only defines the problem rather than forbidding the solution.

[14] But true universalism is all-inclusive, and our daily lives are structured according to a multitude of universal standards, from space-time coordinates to HTTP on the Web. I responded at length in The Semantic Sphere to the prejudices of extremist post-modernism against scientific universality.

[15] Which is still used by a large community. But the only thing that Esperanto and IEML have in common is the fact that they are artificial languages. They have neither the same form nor the same purpose, nor the same use, which invalidates criticisms of IEML based on the criticism of Esperanto.

[16] See IEML Grammar (On line http://wp.me/P3bDiO-9V ).

[17] But, fortunately, supported by the Canada Research Chairs program and by my wife, Darcia Labrosse.

[18] Michel Serres, Hermès V. Le passage du Nord-Ouest (Paris: Minuit, 1980).

[19] The concept of episteme, which is broader than the concept of paradigm, was developed in particular by Michel Foucault in The Order of Things (New York: Pantheon, 1970) and The Archaeology of Knowledge and the Discourse on Language (New York: Pantheon, 1972).

[20] At the beginning of Book A of his Metaphysics.

[21] This is the Axial Age identified by Karl Jaspers.

[22] Book Lambda, 9

[23] In particular in Book III.

[24] See, for example, Moses Maimonides, The Guide For the Perplexed, translated into English by Michael Friedländer (New York: Cosimo Classic, 2007) (original in Arabic from the twelfth century). – Averroes (Ibn Rushd), Long Commentary on the De Anima of Aristotle, translated with introduction and notes by Richard C. Taylor (New Haven: Yale University Press, 2009) (original in Arabic from the twelfth century). – Saint Thomas Aquinas: On the Unity of the Intellect Against the Averroists (original in Latin from the thirteenth century) – Herbert A. Davidson, Alfarabi, Avicenna, and Averroes, on Intellect. Their Cosmologies, Theories of the Active Intellect, and Theories of Human Intellect (New York, Oxford: Oxford University Press, 1992). – Henri Corbin, History of Islamic Philosophy, translated by Liadain and Philip Sherrard (London: Kegan Paul, 1993). – Henri Corbin, En Islam iranien: aspects spirituels et philosophiques, 2d ed. (Paris: Gallimard, 1978), 4 vol. – De Libera, Alain Métaphysique et noétique: Albert le Grand (Paris: Vrin, 2005).

[25] In Meditations on First Philosophy, “First Meditation.” [26] Discourse on the Method, “Part IV.”

[27] At the end of The Order of Things (New York: Pantheon Books, 1970). [28] See, for example, Stéphane Vial, L’être et l’écran (Paris: PUF, 2013).

Pierre Levy-photo 1

Originally published by the CCCTLab as an interview with Sandra Alvaro.

Pierre Lévy is a philosopher and a pioneer in the study of the impact of the Internet on human knowledge and culture. In Collective Intelligence. Mankind’s Emerging World in Cyberspace, published in French in 1994 (English translation in 1999), he describes a kind of collective intelligence that extends everywhere and is constantly evaluated and coordinated in real time, a collective human intelligence, augmented by new information technologies and the Internet. Since then, he has been working on a major undertaking: the creation of IEML (Information Economy Meta Language), a tool for the augmentation of collective intelligence by means of the algorithmic medium. IEML, which already has its own grammar, is a metalanguage that includes the semantic dimension, making it computable. This in turn allows a reflexive representation of collective intelligence processes.

In the book Semantic Sphere I. Computation, Cognition, and Information Economy, Pierre Lévy describes IEML as a new tool that works with the ocean of data of participatory digital memory, which is common to all humanity, and systematically turns it into knowledge. A system for encoding meaning that adds transparency, interoperability and computability to the operations that take place in digital memory.

By formalising meaning, this metalanguage adds a human dimension to the analysis and exploitation of the data deluge that is the backdrop of our lives in the digital society. And it also offers a new standard for the human sciences with the potential to accommodate maximum diversity and interoperability.

In “The Technologies of Intelligence” and “Collective Intelligence”, you argue that the Internet and related media are new intelligence technologies that augment the intellectual processes of human beings. And that they create a new space of collaboratively produced, dynamic, quantitative knowledge. What are the characteristics of this augmented collective intelligence?

The first thing to understand is that collective intelligence already exists. It is not something that has to be built. Collective intelligence exists at the level of animal societies: it exists in all animal societies, especially insect societies and mammal societies, and of course the human species is a marvellous example of collective intelligence. In addition to the means of communication used by animals, human beings also use language, technology, complex social institutions and so on, which, taken together, create culture. Bees have collective intelligence but without this cultural dimension. In addition, human beings have personal reflexive intelligence that augments the capacity of global collective intelligence. This is not true for animals but only for humans.

Now the point is to augment human collective intelligence. The main way to achieve this is by means of media and symbolic systems. Human collective intelligence is based on language and technology and we can act on these in order to augment it. The first leap forward in the augmentation of human collective intelligence was the invention of writing. Then we invented more complex, subtle and efficient media like paper, the alphabet and positional systems to represent numbers using ten numerals including zero. All of these things led to a considerable increase in collective intelligence. Then there was the invention of the printing press and electronic media. Now we are in a new stage of the augmentation of human collective intelligence: the digital or – as I call it – algorithmic stage. Our new technical structure has given us ubiquitous communication, interconnection of information, and – most importantly – automata that are able to transform symbols. With these three elements we have an extraordinary opportunity to augment human collective intelligence.

You have suggested that there are three stages in the progress of the algorithmic medium prior to the semantic sphere: the addressing of information in the memory of computers (operating systems), the addressing of computers on the Internet, and finally the Web – the addressing of all data within a global network, where all information can be considered to be part of an interconnected whole–. This externalisation of the collective human memory and intellectual processes has increased individual autonomy and the self-organisation of human communities. How has this led to a global, hypermediated public sphere and to the democratisation of knowledge?

This democratisation of knowledge is already happening. If you have ubiquitous communication, it means that you have access to any kind of information almost for free: the best example is Wikipedia. We can also speak about blogs, social media, and the growing open data movement. When you have access to all this information, when you can participate in social networks that support collaborative learning, and when you have algorithms at your fingertips that can help you to do a lot of things, there is a genuine augmentation of collective human intelligence, an augmentation that implies the democratisation of knowledge.

What role do cultural institutions play in this democratisation of knowledge?

Cultural Institutions are publishing data in an open way; they are participating in broad conversations on social media, taking advantage of the possibilities of crowdsourcing, and so on. They also have the opportunity to grow an open, bottom-up knowledge management strategy.

dialect_human_development

A Model of Collective Intelligence in the Service of Human Development (Pierre Lévy, en The Semantic Sphere, 2011) S = sign, B = being, T = thing.

We are now in the midst of what the media have branded the ‘big data’ phenomenon. Our species is producing and storing data in volumes that surpass our powers of perception and analysis. How is this phenomenon connected to the algorithmic medium?

First let’s say that what is happening now, the availability of big flows of data, is just an actualisation of the Internet’s potential. It was always there. It is just that we now have more data and more people are able to get this data and analyse it. There has been a huge increase in the amount of information generated in the period from the second half of the twentieth century to the beginning of the twenty-first century. At the beginning only a few people used the Internet and now almost the half of human population is connected.

At first the Internet was a way to send and receive messages. We were happy because we could send messages to the whole planet and receive messages from the entire planet. But the biggest potential of the algorithmic medium is not the transmission of information: it is the automatic transformation of data (through software).

We could say that the big data available on the Internet is currently analysed, transformed and exploited by big governments, big scientific laboratories and big corporations. That’s what we call big data today. In the future there will be a democratisation of the processing of big data. It will be a new revolution. If you think about the situation of computers in the early days, only big companies, big governments and big laboratories had access to computing power. But nowadays we have the revolution of social computing and decentralized communication by means of the Internet. I look forward to the same kind of revolution regarding the processing and analysis of big data.

Communications giants like Google and Facebook are promoting the use of artificial intelligence to exploit and analyse data. This means that logic and computing tend to prevail in the way we understand reality. IEML, however, incorporates the semantic dimension. How will this new model be able to describe they way we create and transform meaning, and make it computable?

Today we have something called the “semantic web”, but it is not semantic at all! It is based on logical links between data and on algebraic models of logic. There is no model of semantics there. So in fact there is currently no model that sets out to automate the creation of semantic links in a general and universal way. IEML will enable the simulation of ecosystems of ideas based on people’s activities, and it will reflect collective intelligence. This will completely change the meaning of “big data” because we will be able to transform this data into knowledge.

We have very powerful tools at our disposal, we have enormous, almost unlimited computing power, and we have a medium were the communication is ubiquitous. You can communicate everywhere, all the time, and all documents are interconnected. Now the question is: how will we use all these tools in a meaningful way to augment human collective intelligence?

This is why I have invented a language that automatically computes internal semantic relations. When you write a sentence in IEML it automatically creates the semantic network between the words in the sentence, and shows the semantic networks between the words in the dictionary. When you write a text in IEML, it creates the semantic relations between the different sentences that make up the text. Moreover, when you select a text, IEML automatically creates the semantic relations between this text and the other texts in a library. So you have a kind of automatic semantic hypertextualisation. The IEML code programs semantic networks and it can easily be manipulated by algorithms (it is a “regular language”). Plus, IEML self-translates automatically into natural languages, so that users will not be obliged to learn this code.

The most important thing is that if you categorize data in IEML it will automatically create a network of semantic relations between the data. You can have automatically-generated semantic relations inside any kind of data set. This is the point that connects IEML and Big Data.

So IEML provides a system of computable metadata that makes it possible to automate semantic relationships. Do you think it could become a new common language for human sciences and contribute to their renewal and future development?

Everyone will be able to categorise data however they want. Any discipline, any culture, any theory will be able to categorise data in its own way, to allow diversity, using a single metalanguage, to ensure interoperability. This will automatically generate ecosystems of ideas that will be navigable with all their semantic relations. You will be able to compare different ecosystems of ideas according to their data and the different ways of categorising them. You will be able to chose different perspectives and approaches. For example, the same people interpreting different sets of data, or different people interpreting the same set of data. IEML ensures the interoperability of all ecosystem of ideas. On one hand you have the greatest possibility of diversity, and on the other you have computability and semantic interoperability. I think that it will be a big improvement for the human sciences because today the human sciences can use statistics, but it is a purely quantitative method. They can also use automatic reasoning, but it is a purely logical method. But with IEML we can compute using semantic relations, and it is only through semantics (in conjunction with logic and statistics) that we can understand what is happening in the human realm. We will be able to analyse and manipulate meaning, and there lies the essence of the human sciences.

Let’s talk about the current stage of development of IEML: I know it’s early days, but can you outline some of the applications or tools that may be developed with this metalanguage?

Is still too early; perhaps the first application may be a kind of collective intelligence game in which people will work together to build the best ecosystem of ideas for their own goals.

I published The Semantic Sphere in 2011. And I finished the grammar that has all the mathematical and algorithmic dimensions six months ago. I am writing a second book entitled Algorithmic Intelligence, where I explain all these things about reflexivity and intelligence. The IEML dictionary will be published (online) in the coming months. It will be the first kernel, because the dictionary has to be augmented progressively, and not just by me. I hope other people will contribute.

This IEML interlinguistic dictionary ensures that semantic networks can be translated from one natural language to another. Could you explain how it works, and how it incorporates the complexity and pragmatics of natural languages?

The basis of IEML is a simple commutative algebra (a regular language) that makes it computable. A special coding of the algebra (called Script) allows for recursivity, self-referential processes and the programming of rhizomatic graphs. The algorithmic grammar transforms the code into fractally complex networks that represent the semantic structure of texts. The dictionary, made up of terms organized according to symmetric systems of relations (paradigms), gives content to the rhizomatic graphs and creates a kind of common coordinate system of ideas. Working together, the Script, the algorithmic grammar and the dictionary create a symmetric correspondence between individual algebraic operations and different semantic networks (expressed in natural languages). The semantic sphere brings together all possible texts in the language, translated into natural languages, including the semantic relations between all the texts. On the playing field of the semantic sphere, dialogue, intersubjectivity and pragmatic complexity arise, and open games allow free regulation of the categorisation and the evaluation of data. Ultimately, all kinds of ecosystems of ideas – representing collective cognitive processes – will be cultivated in an interoperable environment.

start-ieml

Schema from the START – IEML / English Dictionary by Prof. Pierre Lévy FRSC CRC University of Ottawa 25th August 2010 (Copyright Pierre Lévy 2010 (license Apache 2.0)

Since IEML automatically creates very complex graphs of semantic relations, one of the development tasks that is still pending is to transform these complex graphs into visualisations that make them usable and navigable.

How do you envisage these big graphs? Can you give us an idea of what the visualisation could look like?

The idea is to project these very complex graphs onto a 3D interactive structure. These could be spheres, for example, so you will be able to go inside the sphere corresponding to one particular idea and you will have all the other ideas of its ecosystem around you, arranged according to the different semantic relations. You will be also able to manipulate the spheres from the outside and look at them as if they were on a geographical map. And you will be able to zoom in and zoom out of fractal levels of complexity. Ecosystems of ideas will be displayed as interactive holograms in virtual reality on the Web (through tablets) and as augmented reality experienced in the 3D physical world (through Google glasses, for example).

I’m also curious about your thoughts on the social alarm generated by the Internet’s enormous capacity to retrieve data, and the potential exploitation of this data. There are social concerns about possible abuses and privacy infringement. Some big companies are starting to consider drafting codes of ethics to regulate and prevent the abuse of data. Do you think a fixed set of rules can effectively regulate the changing environment of the algorithmic medium? How can IEML contribute to improving the transparency and regulation of this medium?

IEML does not only allow transparency, it allows symmetrical transparency. Everybody participating in the semantic sphere will be transparent to others, but all the others will also be transparent to him or her. The problem with hyper-surveillance is that transparency is currently not symmetrical. What I mean is that ordinary people are transparent to big governments and big companies, but these big companies and big governments are not transparent to ordinary people. There is no symmetry. Power differences between big governments and little governments or between big companies and individuals will probably continue to exist. But we can create a new public space where this asymmetry is suspended, and where powerful players are treated exactly like ordinary players.

And to finish up, last month the CCCB Lab held began a series of workshops related to the Internet Universe project, which explore the issue of education in the digital environment. As you have published numerous works on this subject, could you summarise a few key points in regard to educating ‘digital natives’ about responsibility and participation in the algorithmic medium?

People have to accept their personal and collective responsibility. Because every time we create a link, every time we “like” something, every time we create a hashtag, every time we buy a book on Amazon, and so on, we transform the relational structure of the common memory. So we have a great deal of responsibility for what happens online. Whatever is happening is the result of what all the people are doing together; the Internet is an expression of human collective intelligence.

Therefore, we also have to develop critical thinking. Everything that you find on the Internet is the expression of particular points of view, that are neither neutral nor objective, but an expression of active subjectivities. Where does the money come from? Where do the ideas come from? What is the author’s pragmatic context? And so on. The more we know the answers to these questions, the greater the transparency of the source… and the more it can be trusted. This notion of making the source of information transparent is very close to the scientific mindset. Because scientific knowledge has to be able to answer questions such as: Where did the data come from? Where does the theory come from? Where do the grants come from? Transparency is the new objectivity.

Blog of Collective Intelligence (since 2003)

pierre_levy

Pierre Lévy is a philosopher and a pioneer in the study of the impact of the Internet on human knowledge and culture. In Collective Intelligence. Mankind’s Emerging World in Cyberspace, published in French in 1994 (English translation in 1999), he describes a kind of collective intelligence that extends everywhere and is constantly evaluated and coordinated in real time, a collective human intelligence, augmented by new information technologies and the Internet. Since then, he has been working on a major undertaking: the creation of IEML (Information Economy Meta Language), a tool for the augmentation of collective intelligence by means of the algorithmic medium. IEML, which already has its own grammar, is a metalanguage that includes the semantic dimension, making it computable. This in turn allows a reflexive representation of collective intelligence processes.

In the book Semantic Sphere I. Computation, Cognition, and Information Economy, Pierre Lévy describes IEML as…

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An IEML paradigm projected onto a sphere.

Communication presented at The Future of Text symposium IV at the Google’s headquarters in London (2014).

Symbolic manipulation accounts for the uniqueness of human cognition and consciousness. This symbolic manipulation is now augmented by algorithms. The problem is that we still have not invented a symbolic system that could fully exploit the algorithmic medium in the service of human development and human knowledge.

E-Cultural-revolutions

The slide above describes the successive steps in the augmentation of symbolic manipulation.

The first revolution is the invention of writing with symbols endowed with the ability of self-conservation. This leads to a remarquable augmentation of social memory and to the emergence of new forms of knowledge.

The second revolution optimizes the manipulation of symbols like the invention of the alphabet (phenician, hebrew, greek, roman, arab, cyrilic, korean, etc.), the chinese rational ideographies, the indian numeration system by position with a zero, paper and the early printing techniques of China and Korea.

The third revolution is the mecanization and the industrialization of the reproduction and diffusion of symbols, like the printing press, disks, movies, radio, TV, etc. This revolution supported the emergence of the modern world, with its nation states, industries and its experimental mathematized natural sciences.

We are now at the beginning of a fourth revolution where an ubiquitous and interconnected infosphere is filled with symbols – i.e. data – of all kinds (music, voice, images, texts, programs, etc.) that are being automatically transformed. With the democratization of big data analysis, the next generations will see the advent of a new scientific revolution… but this time it will be in the humanities and social sciences.

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Let’s have a closer look to the algorithmic medium. Four layers have been added since the middle of the 20th century.

– The first layer is the invention of the automatic digital computer itself. We can describe computation as « processing on data ». It is self-evident that computation cannot be programmed if we don’t have a very precise addressing system for the data and for the specialized operators/processors that will transform the data. At the beginning these addressing systems were purely local and managed by operating systems.

– The second layer is the emergence of a universal addressing system for computers, the Internet protocol, that allowed for exchange of data and collaborative computing across the telecommunication network.

– The third layer is the invention of a data universal addressing and displaying system (http, html), welcoming a hypertextual global database: the World Wide Web. We all know that the Web has had a deep social, cultural and economic impact in the last fifteen years.

– The construction of this algorithmic medium is ongoing. We are now ready to add a fourth layer of addressing and, this time, we need a universal addressing system for metadata, and in particular for semantic metadata. Why? First, we are still unable to resolve the problem of semantic interoperability across languages, classifications and ontologies. And secondly, except for some approximative statistical and logical methods, we are still unable to compute semantic relations, including distances and differences. This new symbolic system will be a key element to a future scientific revolution in the humanities and social sciences leading to a new kind of reflexive collective intelligence for our species. There lies the future of text.

E-IEML-math2

My version of a universal semantic addressing system is IEML, an artificial language that I have invented and developped over the last 20 years.

IEML is based on a simple algebra with six primitive variables (E, U, A, S, B, T) and two operations (+, ×). The multiplicative operation builds the semantic links. This operation has three roles: a depature node, an arrival node and a tag for the link. The additive operation gathers several links to build a semantic network and recursivity builds semantic networks with multiple levels of complexity: it is « fractal ». With this algebra, we can automatically compute an internal network corresponding to any variable and also the relationships between any set of variables.

IEML is still at the stage of fundamental research but we now have an extensive dictionary – a set of paradigms – of three thousand terms and grammatical algorithmic rules that conform to the algebra. The result is a language where texts self-translate into natural language, manifest as semantic networks and compute collaboratively their relationships and differences. Any library of IEML texts then self-organizes into ecosystems of texts and data categorized in IEML will self-organize according to their semantic relationships and differences.

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Now let’s take an example of an IEML paradigm, the paradigm of “Collective Intelligence in the service of human development” for instance, where we will grasp the meaning of the primitives and in which way they are being used.

-First, let’s look at the dialectic between virtual (U) and actual (A) human development represented by the rows.

-Then, the ternary dialectic between sign (S), being (B) and thing (T) are represented by the columns.

-The result is six broad interdependent aspects of collective intelligence corresponding to the intersections of the rows (virtual/actual) and columns (sign/being/thing).

– Each of these six broad aspects of CI are then decomposed into three sub-aspects corresponding to the sign/being/thing dialectic.

The semantic relations (symmetries and inclusions) between the terms of a paradigm are all explicit and therefore computable. All IEML paradigms are designed with the same principles as this one, and you can build phrases by assembling the terms through multiplications and additions.

Fortunatly, fundamental research is now finished. I will spend the next months preparing a demo of the automatic computing of semantic relations between data coded in IEML. With tools to come…

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