Archives for posts with tag: Collective intelligence

I put forward in this paper a vision for a new generation of cloud-based public communication service designed to foster reflexive collective intelligence. I begin with a description of the current situation, including the huge power and social shortcomings of platforms like Google, Apple, Facebook, Amazon, Microsoft, Alibaba, Baidu, etc. Contrasting with the practice of these tech giants, I reassert the values that are direly needed at the foundation of any future global public sphere: opennness, transparency and commonality. But such ethical and practical guidelines are probably not powerful enough to help us crossing a threshold in collective intelligence. Only a disruptive innovation in cognitive computing will do the trick. That’s why I introduce “deep meaning” a new research program in artificial intelligence, based on the Information Economy  MetaLanguage (IEML). I conclude this paper by evoking possible bootstrapping scenarii for the new public platform.

The rise of platforms

At the end of the 20th century, one percent of the human population was connected to the Internet. In 2017, more than half the population is connected. Most of the users interact in social media, search information, buy products and services online. But despite the ongoing success of digital communication, there is a growing dissatisfaction about the big tech companies – the “Silicon Valley” – who dominate the new communication environment.

The big techs are the most valued companies in the world and the massive amount of data that they possess is considered the most precious good of our time. Silicon Valley owns the big computers: the network of physical centers where our personal and business data are stored and processed. Their income comes from their economic exploitation of our data for marketing purposes and from their sales of hardware, software or services. But they also derive considerable power from the knowledge of markets and public opinions that stems from their information control.

The big cloud companies master new computing techniques mimicking neurons when they learn a new behavior. These programs are marketed as deep learning or artificial intelligence even if they have no cognitive autonomy and need some intense training by humans before becoming useful. Despite their well known limitations, machine learning algorithms have effectively augmented the abilities of digital systems. Deep learning is now used in every economic sector. Chips specialized in deep learning are found in big data centers, smartphones, robots and autonomous vehicles. As Vladimir Putin rightly told young Russians in his speech for the first day of school in fall 2017: “Whoever becomes the leader in this sphere [of artificial intelligence] will become the ruler of the world”.

The tech giants control huge business ecosystems beyond their official legal borders and they can ruin or buy competitors. Unfortunately, the big tech rivalry prevents a real interoperability between cloud services, even if such interoperability would be in the interest of the general public and of many smaller businesses. As if their technical and economic powers were not enough, the big tech are now playing into the courts of governments. Facebook warrants our identity and warns our family and friends that we are safe when a terrorist attack or a natural disaster occurs. Mark Zuckerberg states that one of Facebook’s mission is to insure that the electoral process is fair and open in democratic countries. Google Earth and Google Street View are now used by several municipal instances and governments as their primary source of information for cadastral plans and other geographical or geospatial services. Twitter became an official global political, diplomatic and news service. Microsoft sells its digital infrastructure to public schools. The kingdom of Denmark opened an official embassy in Silicon Valley. Cryptocurrencies independent from nation states (like Bitcoin) are becoming increasingly popular. Blockchain-based smart contracts (powered by Ethereum) bypass state authentication and traditional paper bureaucracies. Some traditional functions of government are taken over by private technological ventures.

This should not come as a surprise. The practice of writing in ancient palace-temples gave birth to government as a separate entity. Alphabet and paper allowed the emergence of merchant city-states and the expansion of literate empires. The printing press, industrial economy, motorized transportation and electronic media sustained nation-states. The digital revolution will foster new forms of government. Today, we discuss political problems in a global public space taking advantage of the web and social media and the majority of humans live in interconnected cities and metropoles. Each urban node wants to be an accelerator of collective intelligence, a smart city. We need to think about public services in a new way. Schools, universities, public health institutions, mail services, archives, public libraries and museums should take full advantage of the internet and de-silo their datasets. But we should go further. Are current platforms doing their best to enhance collective intelligence and human development? How about giving back to the general population the data produced in social media and other cloud services, instead of just monetizing it for marketing purposes ? How about giving to the people access to cognitive powers unleashed by an ubiquitous algorithmic medium?

Information wants to be open, transparent and common

We need a new kind of public sphere: a platform in the cloud where data and metadata would be our common good, dedicated to the recording and collaborative exploitation of memory in the service of our collective intelligence. The core values orienting the construction of this new public sphere should be: openness, transparency and commonality

Firstly openness has already been experimented in the scientific community, the free software movement, the creative commons license, Wikipedia and many more endeavors. It has been adopted by several big industries and governments. “Open by default” will soon be the new normal. Openness is on the rise because it maximizes the improvement of goods and services, foster trust and support collaborative engagement. It can be applied to data formats, operating systems, abstract models, algorithms and even hardware. Openness applies also to taxonomies, ontologies, search architectures, etc. This notion may be generalized to an open creation, description and interpretation of data. A new open public space should encourage all participants to create, comment, categorize, assess and analyze its content.

, transparency is the very basis of trust and the precondition of authentic dialogue. Data and people (including the administrators of a platform), should be traceable and audit-able. Transparency should be reciprocal, without distinction between rulers and ruled. Such transparency will ultimately be the basis of reflexive collective intelligence, allowing teams and communities of any size to observe and compare their cognitive activity

Commonality means that people will not have to pay to get access to the new public sphere: all will be free and public property. Commonality means also transversality: de-silo and cross-pollination. Smart communities will interconnect and recombine all kind of useful information: open archives of libraries and museums, free academic publications, shared learning resources, knowledge management repositories, open-source intelligence datasets, news, public legal databases…

From deep learning to deep meaning

The new public platform will be based on the web and its open standards like http, URL, html, etc. Like all current platforms, it will take advantage of distributed computing in the cloud. It will use “deep learning”: an artificial intelligence technology that employs specialized chips and algorithms that roughly mimic the learning process of neurons. Deep learning is used by Google, Facebook, Amazon, Microsoft and by other companies specialized in data analytics. Finally, to be completely up to date, the public platform should enable blockchain-based payments, transactions, contracts and secure records

If our public platform offers the same technologies as the big tech (cloud, deep learning, blockchain), with the sole difference of openness, transparency and commonality, it may prove insufficient to foster a swift adoption, as is demonstrated by the relative failures of Diaspora (open Facebook) and Mastodon (open Twitter). Such a project may only succeed if it has some technical advantage compared to the existing commercial platforms. Moreover, this technical advantage should have appealing political and philosophical dimensions.

The majority of us do not fancy the dream of autonomous machines, specially considering the current limitations of artificial intelligence. We want instead an artificial intelligence designed for the augmentation of human personal and collective intellect. That’s why, in addition to the current state of the art, the new platform should integrate the brand new deep meaning technology. Deep meaning will expand the actual reach of artificial intelligence, improve the user experience of big data analytics and allow the reflexivity of personal and collective intelligence.

Language as a platform

In a nutshell, deep learning models neurons and deep meaning models language. In order to augment the human intellect, we need both! Deep learning is based on neural networks simulation. It is enough to model roughly animal cognition (every animal species has neurons) but not enough to model human cognition. The difference between animal cognition and human reflexive thought comes from language, which adds a layer of semantic addressing on top of neuronal connectivity. Speech production and understanding is an innate property of individual human brains. But as humanity is a social species, language works only at the social scale. Languages are conventional, shared by members of the same culture and learned by social contact. In human cognition, the categories that organize perception, action, memory and learning are expressed linguistically so they may be reflected upon and shared in conversations. A language works like the semantic addressing system of a social virtual database.

The problem with natural languages (english, french, arabic, etc.) is that they are irregular and do not lend themselves easily to machine understanding or machine translation. The current trend in natural language processing (an important field of artificial intelligence) is to use statistical algorithms and deep learning methods to understand and produce linguistic data. Instead of using statistics, deep meaning adopts a regular and computable metalanguage to organize linguistic and non-linguistic data. IEML (Information Economy MetaLanguage) has been designed to optimize semantic computing. IEML words are built from six primitive symbols and two operations: addition and multiplication. The semantic relations between words follow the lines of their generative operations. Words (the total number of which do not exceed 10 000) represent the conceptual building blocks of the language. From these elementary concepts, the generative grammar of IEML allows the construction of propositions at three layers of complexity: words into topics, topics into phrases (facts, events) and phrases into super-phrases (theories, narratives). The higher meaning unit, or text, is a unique set of propositions. Deep meaning technology uses IEML as the semantic addressing system of a social database.

From an analytics angle, deep meaning allows the automatic computing of semantic relations between data and semantic visualizations of large datasets. From the point of view of interoperability, it decompartmentalizes tags, folksonomies, taxonomies, ontologies and languages. On the reflexive side, when on line communities categorize, assess and exchange semantic data, they generate explorable ecosystems of ideas that represent their collective intelligence. Note that the vision of collective intelligence proposed here is opposed to the “wisdom of the crowd” model, that assumes independent agents and excludes dialogue and reflexivity. Just the opposite : deep meaning was designed from the beginning to foster dialogue and reflexivity.

The main functions of the new public sphere


In the new public sphere, every netizen has the rights of an author, an editor, an artist, a curator, a critique, a messenger, a contractor and a gamer. The next platform weaves five functions together: curation, creation, communication, transaction and immersion.

By curation I mean the collaborative creation, edition, analysis, synthesis, visualization, explanation and publication of datasets. People posting, liking and commenting content on social media are already doing data curation, even if in a crude way and unknowingly. Active professionals in the fields of heritage preservation (library, museums), digital humanities, education, knowledge management, data-driven journalism or open-source intelligence practice data curation in a more systematic and mindful manner. The new platform offers a consistent service of collaborative data curation empowered by a common semantic addressing system.

Augmented by deep meaning, our public sphere includes a semantic metadata editor applicable to any document format. It works as a registration system for the works of the mind. Communication is ensured by a global Twitter-like public posting system. But instead of the current hashtags that are mere sequences of characters, the new semantic tags self-translate in all natural languages and interconnect by conceptual proximity. The blockchain layer allows any transaction to be recorded. The platform remunerates authors and curators in collective intelligence coins, according to the public engagement generated by their work. The new public sphere is grounded in the internet of things, smart cities, ambient intelligence and augmented reality. People control their environment and communicate with sensors, software agents and bots of all kinds in the same immersive semantic space. Virtual worlds simulate the collective intelligence of teams, networks and cities.


The design and prototyping of this platform has been developed between 2002 and 2017 at the University of Ottawa. A prototype is currently in a pre-alpha version, featuring the curation functionality. An alpha version will be demonstrated in the summer of 2018. How to bridge the gap from the fundamental research to the full scale industrial platform? Such endeavor will be much less expensive than the conquest of space and could bring a tremendous augmentation of human collective intelligence. Even if the network effect applies obviously to the new public space, small communities of pioneers will benefit immediately from its early release. On the humanistic side, I have already mentioned museums and libraries, researchers in humanities and social science, collaborative learning networks, data-oriented journalists, knowledge management and business intelligence professionals, etc. On the engineering side, deep meaning opens a new sub-field of artificial intelligence that will enhance current techniques of big data analytics, machine learning, natural language processing, internet of things, augmented reality and other immersive interfaces. Because it is open source by design, the development of the new technology can be crowdsourced and shared easily among many different actors.

Let’s draw a distinction between the new public sphere, including its semantic coordinate system, and the commercial platforms that will give access to it. This distinction being made, we can imagine a consortium of big tech companies, universities and governments supporting the development of the global public service of the future. We may also imagine one of the big techs taking the lead to associate its name to the new platform and developing some hardware specialized in deep meaning. Another scenario is the foundation of a company that will ensure the construction and maintenance of the new platform as a free public service while sustaining itself by offering semantic services: research, consulting, design and training. In any case, a new international school must be established around a virtual dockyard where trainees and trainers build and improve progressively the semantic coordinate system and other basic models of the new platform. Students from various organizations and backgrounds will gain experience in the field of deep meaning and will disseminate the acquired knowledge back into their communities.

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…


  • 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).

Intlekt v.0

Intlekt v.0 is a collaborative data curation tool that allows
– the categorization of data in IEML,
– the semantic visualization of collections of data categorized in IEML
– the publication of these collections

The prototype (to be issued in May 2018) will be mono-user but the full blown app will be social.

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).


Dice sculpture by Tony Cragg

Ce post est la version française d’un entretien en portugais (Brésil) avec le prof.  Juremir Machado da Silva


1 – JMDS: Le développement d’internet a pris plus de temps qu’on n’imagine, mais pour presque tout le monde internet c’est l’explosion du web pendant les années 1990. On peut dire d’une certaine façon que ça fait 30 ans qu’on est entré dans un nouvel imaginaire. Est-ce qu’il y a encore beaucoup de choses à venir ou le cycle a atteint son plafond?

PL: Internet s’est developpé de façon beaucoup plus rapide que n’importe quel autre système de communication. Il y avait moins de 1% de la population mondiale branchée au début des années 1990 et près de 45% une génération plus tard. On avance très vite vers 50% et plus…
Nous sommes seulement au début de la révolution du medium algorithmique. Au cours des générations suivantes nous allons assister à plusieurs grandes mutations. L’informatique ubiquitaire fondue dans le paysage et constamment accessible va se généraliser. L’accès à l’analyse de grandes masses de données (qui est aujourd’hui dans les mains des gouvernements et grandes entreprises) va se démocratiser. Nous aurons de plus en plus d’images de notre fonctionnement collectif en temps réel, etc. L’éducation va se recentrer sur la formation critique à la curation collective des données. La sphère publique va devenir internationale et va s’organiser par « nuages sémantiques » dans les réseaux sociaux. Les états vont passer de la forme « état-nation » à la forme « état en essaim » avec un territoire souverain et une strate déterritorialisée dans l’info-sphère ubiquitaire, les crypto-monnaies vont se répandre, etc.

2 –JMDS: On parte beaucoup d’internet des objets et de tout internet. Ce sont des vraies mutations ou juste des accélérations?

Internet peut être analysé en deux aspects conceptuellement distincts mais pratiquement interdépendants et inséparables. D’une part l’infosphère, les données, les algorithmes, qui sont immatériels et ubiquitaires : ce sont les « nuages ». D’autre part les capteurs, les gadgets, les smart-phones, les dispositifs portables de toutes sortes, les ordinateurs, les data centers, les robots, tout ce qui est inévitablement physique et localisé : les « objets ». Les nuages ne peuvent pas fonctionner sans les objets et vice versa: les objets ne peuvent pas fonctionner sans les nuages. L’Internet, c’est l’interaction constante du localisé et du délocalisé, des objets et des nuages. Tout cela est en quelque sorte logiquement déductible de l’automatisation de la manipulation symbolique au moyen de systèmes électroniques, mais nous allons de plus en plus en sentir les effets dans notre vie de tous les jours.

3 –JMDS:  Avec internet les prédictions sont déchaînées. On continue à parle de l’avenir des journaux en papier et du livre. Il y a ceux qui disent que le papier va cohabiter avec des nouveaux supports et ceux qui disent que c’est juste une question de temps pour la fin de l’imprimé. Les arguments des uns et des autres sont sérieux? Par exemple, par rapport au papier, l’affectif et l’effet de nostalgie n’y compte pas trop? C’est une affaire de génération?

PL: Je crois que la fin de la presse papier est une affaire de temps. Pour la recherche, l’éducation, l’information, tout va passer au numérique. En revanche, j’imagine qu’il va toujours y avoir des lecteurs sur papier pour des romans ou des livres rares, un peu comme il y a toujours un petit marché pour le vinyl en musique. Personnellement, j’aime lire des livres sur papier et les nouvelles sur Internet (surtout par Twitter), mais ce ne sont pas mes préférences personnelles qui sont en jeu… l’électrification, voire l’algorithmisation, de la lecture et de l’écriture sont inévitables.

4 –JMDS:  Après 30 ans de nouveautés comme les réseaux sociaux, quelle a été la grande transformation, le point principal de cette mutation?

PL: Depuis l’apparition du Web au milieu des années 1990, il n’y a pas eu de grande mutation technique, seulement une multitude de petits progrès. Sur un plan socio-politique, le grand basculement me semble le passage d’une sphère publique dominée par la presse, la radio et la télévision à une sphère publique dominée par les wikis, les blogs, les réseaux sociaux et les systèmes de curation de contenu où tout le monde peut s’exprimer. Cela signifie que le monopole intellectuel des journalistes, éditeurs, hommes politiques et professeurs est en train de s’éroder. Le nouvel équilibre n’a pas encore été trouvé mais l’ancien équilibre n’a plus cours.

5 –JMDS: Tu parles depuis beaucoup de temps d’intelligence collective et des collectifs intelligents. On voit cependant internet et ses réseaux sociaux utilisés pour le bien et pour le mal, par exemple, pour disséminer les idées radicales des extrémistes musulmans. Peut-on parler d’une « intelligence collective du mal » d’internet ou d’un outil de la bêtise universelle?

PL: Je parle d’intelligence collective pour signaler et encourager une augmentation des capacités cognitives en général, sans jugement de valeur : augmentation de la mémoire collective, des possibilités de coordination et de création de réseaux, des opportunités d’apprentissage collaboratif, de l’ouverture de l’accès à l’information, etc. Je pense que cet aspect est indéniable et que tous les acteurs intellectuels et sociaux responsables devraient se servir de ces nouvelles possibilités dans l’éducation, dans la gestion des connaissances dans les entreprises et les administrations, pour la délibération politique démocratique, etc. Il faut voir l’invention de Internet dans le prolongement de l’invention de l’écriture ou de l’imprimerie. Il s’agit d’une augmentation des capacités humaines de manipulation symbolique. Maintenant, le coeur de cette capacité c’est le langage, qui ne dépend d’aucune technique particulière et qui existe dès l’origine de l’espèce humaine. C’est grâce au langage qu’existent l’art, la culture, la religion, les valeurs, la complexité de nos institutions économiques, sociales, politiques… Mais qui dit langage dit aussi mensonge et manipulation. Qui dit valeurs dit bien ET mal, beau ET laid. Il est absurde d’imaginer qu’un instrument qui augmente les pouvoirs du langage en général ne laisserait subsister que le vrai, le bien et le beau. Vrai pour qui, bien pour qui ? Le vrai n’émerge que du dialogue ouvert des points de vue. Je dirais même plus, si l’on essayait de faire de l’Internet une machine à produire du vrai, du bien et du beau, on ne parviendrait qu’à un projet totalitaire, d’ailleurs voué à l’échec.

6 –JMDS:  Dans les réseaux sociaux la violence verbale est énorme. On s’attaque, on s’insulte, on divise le monde entre droite et gauche, les bons et les mauvais, les miens et les tiens. Il y a déjà des journalistes qui ferment leurs blogs aux commentaires des lecteurs saturés de post racistes, des menaces et d’insultes. On est encore dans une étape d’apprentissage de l’utilisation des ces outils?

PL: Si quelqu’un m’insulte ou m’envoie des choses choquantes sur Twitter, je le bloque et c’est tout! On n’aura jamais une humanité parfaite. En revanche, l’utilisateur d’Internet n’est pas un mineur intellectuel, il possède un grand pouvoir mais aussi une grande responsabilité. Le problème, surtout pour les enseignants, consiste à éduquer les utilisateurs. Il faut apprendre à décider de ses priorités, à gérer son attention, à faire un choix judicieux et une analyse critique des sources auxquelles on se branche, prêter attention à la culture de ses correspondants, apprendre à identifier les récits et leurs contradictions, etc. C’est cela, la nouvelle « literacy digitale »: devenir responsable!

7 –JMDS:  Une des questions les plus discutées à propos d’internet concerne les droits d’auteur et la gratuité. Les internautes ont tendance à exiger le tout gratuit. Mais l’information a un coût. Qui va payer? La publicité? Les journaux ferment leurs sites? Le temps de payer pour consommer sur internet est définitivement arrivé?

PL: Il n’est pas impossible de faire payer les utilisateurs pour de très bons services. Par ailleurs, oui, la publicité et surtout la vente des informations produites par les utilisateurs à des firmes de marketing constitue aujourd’hui la principale manière de « monétiser » les services en ligne. Le droit d’auteur est clairement en crise pour la musique et de plus en plus pour les films. Je voudrais souligner particulièrement le domaine de la recherche et de l’enseignement où les éditeurs apparaissent dorénavant comme le frein principal au partage de la connaissance. La rémunération de la création à l’âge du médium algorithmique est un problème complexe auquel je n’ai pas de réponse simple valable dans tous les cas…

8 –JMDS:  Tu as parlé aussi de démocratie virtuelle. On peut dire aujourd’hui qu’on avance vers une nouvelle ère de démocratisation?

PL: Oui, dans la mesure où il est possible d’accéder à des sources d’information beaucoup plus diverses que dans le passé, dans la mesure aussi où tout le monde peut s’exprimer à destination d’un vaste public et enfin parce qu’il est beaucoup plus facile aux citoyens de se coordonner et de s’organiser à des fins de discussion, de délibération ou d’action. Cette « démocratie virtuelle » peut avoir un fondement local, comme dans certains projets de « villes intelligentes », mais il y a aussi une déterritorialisation ou une internationalisation de la sphère publique. Il est par exemple possible de suivre la vie politique de nombreux pays en direct ou de vivre au diapason de l’ensemble de la planète selon les points de vue ou les sujets qui nous intéressent. On ne peut pas non plus passer sous silence l’émergence de campagnes politiques utilisant toutes les techniques de l’analyse de données et du profilage marketing, ainsi que le monitoring – voire la manipulation – de l’opinion publique mondiale sur les réseaux sociaux par les agences de renseignements (de tous les pays).

9 –JMDS:  Internet a déjà changé notre façon de penser, de lire et d’organiser notre construction du savoir?

PL: C’est indéniable. L’accessibilité immédiate des dictionnaires, des encyclopédies (dont Wikipedia), des livres en accès ouvert ou payant, de multiples vidéos éducatives a mis l’équivalent d’immenses bibliothèques et médiathèques à la portée de tous, partout. De plus, nous pouvons nous abonner à de nombreux sites web spécialisés et nous connecter à des réseaux de personnes interessées par les mêmes sujets afin de construire nos connaissances de manière collaborative. Le développement de nouveaux types de réseaux de collaboration dans la recherche ou d’apprentissage dans l’enseignement (les fameux MOOCs) en témoignent clairement.

10 –JMDS:  Il y a une chanson au Brésil qui dit “malgré tout ce qu’on a fait et vécu nous sommes toujours les mêmes et vivons comme nos parents”. Sommes-nous toujours les mêmes ou bien l’Internet nous a changé et séparés de la vie de nos parents?

PL: Nous sommes toujours des êtres humains incarnés et mortels, heureux et malheureux. La condition humaine fondamentale ne change pas. Ce qui change c’est notre culture matérielle et intellectuelle. Notre puissance de communication s’est multipliée et distribuée dans l’ensemble de la société. La perception du monde qui nous entoure s’est aggrandie et précisée. Notre mémoire a augmenté. Nos capacités d’analyse de situations complexes à partir de flots de données vont bientôt transformer notre rapport à notre environnement biologique et social. Grâce à la quantité de données disponibles et à la croissance de notre puissance de calcul, nous allons probablement connaître au XXIe siècle une révolution des sciences humaines comparable à la révolution des sciences de la nature du XVIIe siècle. Nous sommes toujours les mêmes ET nous changeons.


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.


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.


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.


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!).


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.



IBERTIC (Instituto Iberoamericano de TIC in Education) invited me in Buenos Aires for a series of conferences and consultations during the week of 13th to 17th of april 2015. I gave four speeches in front of different audiences: one of my main speeches was about « collective intelligence for educators » and another about « The emergence of reflexive collective intelligence ».

I had several meetings with people engaged in teaching, training and policy coordination related to « TIC and education ».
Argentina has a big state-led program (Connectar Igualidad) to give a computer to every student in secondary education. I visited the warehouse where these computers are packed and sent to the students and I had a look at the mainly « open » applications included in the package. I visited also Canal Encuentro, the state-run educational television, where a team related to the program « Connectar Igualidad » is building one portal for educators and another one for the students. These portals are supposed to provide learning resources and tools for communication and collaboration.


During this trip I had, at several occasions, the opportunity to speak about my own experience in using TICs as an educator. In the various courses that I teach at the University of Ottawa, I ask my students to participate to a closed Facebook group, to register on Twitter (and to follow me: @plevy), to use a collaborative repository in the cloud (a social bookmarking plateform or a curation plateform like and to open a blog if they don’t have already one.

– The Facebook group is used to share the syllabus, our agenda, the mandatory lectures (all of them on line « at one click »), our electronic addresses (Twitter, blog, collaborative memory plateform), the questions asked by the students, etc. The students can participate to the collective writing and editing of « mini-wikis » inside the FB group. They are invited to suggest good reads related to the course by adding commented links.

– Twitter is used through a careful use of hashtags. I use it for quick real-time feed-back during the course: to check what the students have understood. Then, every 2 or 3 weeks, I invite students to look back at their collective traces on Twitter to recollect what they have learned and to ask questions if something is not clear. I experimented also a « twitter exam » where the students have to evaluate my tweets: no reaction if my tweet is false, a favorite if it contain some truth, a retweet if they agree and a retweet plus a favorite if they strongly agree. After having reviewed the tweets and their responses, I ask to the students what are – according to them – their worst possible errors of appreciation. The final evaluation of the exam (that is, of their reactions to my tweets) is made by applying to the students the rules that they have determined themselves! Finally I teach them the practical use of Twitter lists.

– The collaborative repository in the cloud (Diigo, is used to teach the sudents the use of categories or « tags » to organize a common long-term memory, as opposed to the ephemeral information on popular social media.

– The blogs are used as a way to display the assignments. The students are encouraged to add images and links. For the last assignment they have to describe – from their own point of view – the main points that they have learned during the course.

At the end of a semester, the students have not only acquired knowledge about the subject matter, they also improved their collaborative learning skills in a trans-platform environment!


A social network is a human reality
As « how » once told me: a social network is neither a platform nor a software: it is a human reality. In the same vein, building a closed platform is in no way a solution to collaboration, training, learning or communication problems. The solution is to grow a « community of practice » or a « collaborative learning network » that will use all the available and relevant platforms, including face to face meetings and well known commercial platforms that are available at no cost.

There is no such thing as an educational technology
There are no educational technologies. There are only educational or learning uses of technology. The most important things are not the technologies by themselves (software, platforms, resources) but the educational practices and the effective collaborative learning processes.

The new literacy
The new intellectual literacy encompasses all collaborative data curation skills, including attention management, formal modeling, memory management, critical thinking, stimergic communication, etc. It is neither a discipline nor a specialized knowledge but a consistent set of transversal competencies that should be strengthened in all kinds of learning practices. Of course, this literacy cannot be taught by people who do not master it!

Staying motivated despite constraints
The educational community faces a lot of constraints, particularly in not so rich countries:
– lack of infrastructure (hardware, software, connectivity),
– lack of institutional facilitation (innovation and openness are praised in theory but not encouraged in practise),
– lack of knowledge and skills on the educator’s side.
The educator should consider herself / himself as an artist transforming these constraints into beauty through a creative process. Do what you can in your circonstances. There are no perfect method, software or platform that will solve all the problems magically in every environment and context. Teaching is itself an open-ended (collaborative) learning process.

I recommand this video in spanish about Personal Learning Environments


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.


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.


[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.


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.


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!


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

We will first make a detour by the history of knowledge and communication in order to understand what are the current priorities in education.



The above slide describes the successive steps in the augmentation of symbolic manipulation. At each step in the history of symbolic manipulation, a new kind of knowledge unfolds. During the longest part of human history, the knowledge was only embedded in narratives, rituals and material tools.

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. 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.



We have seen that for each revolution in symbolic manipulation, there was some new developements of knowledge. The same can be said of learning methods and institutions. The school was invented by the scribes. At the beginning, it was a professional training for a caste of writing specialists: scribes and priests. Pedagogy was strict and repetitive. Our current primary school is reminiscent of this first learning institution.

Emerging in the literate culture, the liberal education was aimed at broader elites than the first scribal schools. Young people were trained in reading and interpreting the « classics ». They learned how to build rational argumentation and persuasive discourses.

In modern times, education became compulsory for every citizen of the nation state. Learning became industrialized and uniform through state programs and institutions.

At the time of the algorithmic medium, knowledge is evolving very fast, almost all learning resources are available for free and we interact in social media. This is the end of the old model of learning communities organizing themselves around a library or any physical knowledge repository. Current learning should be conceived as delocalized, life-long and collaborative. The whole society will get a learning dimension. But that does not mean that traditional learning institutions for young people are no longer relevant. Just the opposite, because young people should be prepared for collaborative learning in social media using a practically infinite knowledge repository without any transcending guiding authority. They will need not only technical skills (that will evolve and become obsolete very quickly) but above all moral and intellectual skills that will empower them in their life-long discovery travels.



In the algorithmic medium, communication becomes a collaboration between peers to create, categorize, criticize, organize, read, promote and analyse data by the way of algorithmic tools. It is a stigmergic communication because, even if people dialogue and talk to each other, the main channel of communication is the common memory itself, a memory that everybody transforms and exploits. The above slide lists some examples of this new communication practices. Data curation skills are at the core of the new algorithmic literacy.


I present in the above slide the fundamental intellectual and moral skills that every student will have to master in order to survive in the algorithmic culture. The slide is organized by three rows and three columns that work in an interdependant manner. As the reader can see, personal intelligence is not independant form collective intelligence and vice versa. Moreover, both of them need critical intelligence!


Attention management is not only about focusing or avoiding distraction. It is also about choosing what we want or need to learn and being able to select the relevant sources. We decide what is relevant or not according to our own priorities and our criteria for trust. By the way, people and institutions are the real sources to be trusted or not, not the platforms!

Interpretation. Even with the statistical tools of big data analysis, we will always need theories and causal hypothesis, and not only correlations. We want to understand something and act upon this understanding. Having intuitions and theories derived from our knowledge of a domain, we can use data analytics to test our hypothesis. Asking the right questions to the data is not trivial!

Memory management. The data that we gather must be managed at the material level: we must choose the right memory tool in the clouds. But the data must also be managed at the conceptual level: we have to create and maintain a useful categorisation system (tags, ontologies…) in order to retrieve and analyse easily the desired information.


External critique. There is no transcendant authority in the new communication space. If we don’t want to be fooled, we need to diversify our sources. This means that we will gather sources that have diverse theories and point of views. Then, we should act on this diversity by cross-examining the data and observe where they contradict and where they confirm each other.

Internal critique. In order to understand who is a source, we must identify its classification system, its categories and its narrative. In a way, the source is its narrative.

Pragmatic critique. In essence, the pragmatic critique is the most devastating because it is at this point that we compare the narrative of the source and what it is effectively doing. We can do this by checking the actions of one source as reported by other sources. We can also notice the contradictions in the source’s narratives or a discrepancy between its official narrative and the pragmatic effects of its discourses. A source cannot be trusted when it is not transparent about its references, agenda, finance, etc.


The collective intelligence that I am speaking about is not a miracle solution but a goal to reach. It emerges in the new algorithmic environment in interaction with personal and critical intelligence .

Stigmergic communication. Stigmergy means that people communicate by modifying a common memory. We should distinguish between the local and the global memory. In the local memory (particular communities or networks), we should pay attention to singular contexts and histories. We should also avoid ignorance of other’s contributions, non-relevant questions, trolling, etc.

Liberty. Liberty is a dialectic of power and responsability. Our power here is our ability to create, assess, organize, read and analyse data. Every act in the algorithmic medium re-organizes the common memory: reading, tagging, buying, posting, linking, liking, subscribing, etc. We create collaboratively our own common environment. So we need to take responsability of our actions.

Collaborative learning. This is the main goal of collective intelligence and data curation skills in general. People add explicit knowledge to the common memory. They express what they have learnt in particular contexts (tacit knowledge) into clear and decontextualized propositions, or narratives, or visuals, etc. They translate into common software or other easily accessible resources (explicit) the skills and knowledge that they have internalized in their personal reflexes through their experience (tacit). Symetrically, people try to apply whatever usefull resources they have found in the common memory (explicit) and to acquire or integrate it into their reflexes (tacit).


The final slide above is a visual explicitation of the collaborative learning process. Peers working in a common field of practice use their personal intelligence (PI) to transform tacit knowledge into explicit knowledge. They also work in order to translate some common explicit knowledge into their own practical knowledge. In the algorithmic medium, the explicit knowledge takes the form of a common memory: data categorized and evaluated by the community. The whole process of transforming tacit knowledge into explicit knowledge and vice versa takes place largely in social media, thank to a civilized creative conversation. Intellectual and social (or moral) skills work together!