Archives for posts with tag: IA

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.


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


Critique réciproque de l’intelligence artificielle et des sciences humaines

Je me souviens d’avoir participé, vers la fin des années 1980, à un Colloque de Cerisy sur les sciences cognitives auquel participaient quelques grands noms américains de la discipline, y compris les tenants des courants neuro-connexionnistes et logicistes. Parmi les invités, le philosophe Hubert Dreyfus (notamment l’auteur de What Computers Can’t Do, MIT Press, 1972) critiquait vertement les chercheurs en intelligence artificielle parce qu’ils ne tenaient pas compte de l’intentionnalité découverte par la phénoménologie. Les raisonnements humains réels, rappelait-il, sont situés, orientés vers une fin et tirent leur pertinence d’un contexte d’interaction. Les sciences de la cognition dominées par le courant logico-statistique étaient incapables de rendre compte des horizons de conscience qui éclairent l’intelligence. Dreyfus avait sans doute raison, mais sa critique ne portait pas assez loin, car ce n’était pas seulement la phénoménologie qui était ignorée. L’intelligence artificielle (IA) n’intégrait pas non plus dans la cognition qu’elle prétendait modéliser la complexité des systèmes symboliques et de la communication humaine, ni les médias qui la soutiennent, ni les tensions pragmatiques ou les relations sociales qui l’animent. A cet égard, nous vivons aujourd’hui dans une situation paradoxale puisque l’IA connaît un succès pratique impressionnant au moment même où son échec théorique devient patent.

Succès pratique, en effet, puisqu’éclate partout l’utilité des algorithmes statistiques, de l’apprentissage automatique, des simulations d’intelligence collective animale, des réseaux neuronaux et d’autres systèmes de reconnaissance de formes. Le traitement automatique du langage naturel n’a jamais été aussi populaire, comme en témoigne par exemple l’usage de Google translate. Le Web des données promu par le WWW consortium (dirigé par Sir Tim Berners-Lee). utilise le même type de règles logiques que les systèmes experts des années 1980. Enfin, les algorithmes de computation sociale mis en oeuvre par les moteurs de recherche et les médias sociaux montrent chaque jour leur efficacité.

Mais il faut bien constater l’échec théorique de l’IA puisque, malgré la multitude des outils algorithmiques disponibles, l’intelligence artificielle ne peut toujours pas exhiber de modèle convaincant de la cognition. La discipline a prudemment renoncé à simuler l’intelligence dans son intégralité. Il est clair pour tout chercheur en sciences humaines ayant quelque peu pratiqué la transdisciplinarité que, du fait de sa complexité foisonnante, l’objet des sciences humaines (l’esprit, la pensée, l’intelligence, la culture, la société) ne peut être pris en compte dans son intégralité par aucune des théories computationnelles de la cognition actuellement disponible. C’est pourquoi l’intelligence artificielle se contente dans les faits de fournir une boîte à outils hétéroclite (règles logiques, syntaxes formelles, méthodes statistiques, simulations neuronales ou socio-biologiques…) qui n’offrent pas de solution générale au problème d’une modélisation mathématique de la cognition humaine.

Cependant, les chercheurs en intelligence artificielle ont beau jeu de répondre à leurs critiques issus des sciences humaines : « Vous prétendez que nos algorithmes échouent à rendre compte de la complexité de la cognition humaine, mais vous ne nous en proposez vous-mêmes aucun pour remédier au problème. Vous vous contentez de pointer du doigt vers une multitude de disciplines, plus « complexes » les unes que les autres (philosophie, psychologie, linguistique, sociologie, histoire, géographie, littérature, communication…), qui n’ont pas de métalangage commun et n’ont pas formalisé leurs objets ! Comment voulez-vous que nous nous retrouvions dans ce bric-à-brac ? » Et cette interpellation est tout aussi sensée que la critique à laquelle elle répond.


Synthèse de l’intelligence artificielle et des sciences humaines

Ce que j’ai appris de Hubert Dreyfus lors de ce colloque de 1987 où je l’ai rencontré, ce n’était pas tant que la phénoménologie serait la clé de tous les problèmes d’une modélisation scientifique de l’esprit (Husserl, le père de la phénoménologie, pensait d’ailleurs que la phénoménologie – une sorte de méta-science de la conscience – était impossible à mathématiser et qu’elle représentait même le non-mathématisable par exellence, l’autre de la science mathématique de la nature), mais plutôt que l’intelligence artificielle avait tort de chercher cette clé dans la seule zone éclairée par le réverbère de l’arithmétique, de la logique et des neurones formels… et que les philosophes, herméneutes et spécialistes de la complexité du sens devaient participer activement à la recherche plutôt que de se contenter de critiquer. Pour trouver la clé, il fallait élargir le regard, fouiller et creuser dans l’ensemble du champ des sciences humaines, aussi opaque au calcul qu’il semble à première vue. Nous devions disposer d’un outil à traiter le sens, la signification, la sémantique en général, sur un mode computationnel. Une fois éclairé par le calcul le champ immense des relations sémantiques, une science de la cognition digne de ce nom pourrait voir le jour. En effet, pour peu qu’un outil symbolique nous assure du calcul des relations entre signifiés, alors il devient possible de calculer les relations sémantiques entre les concepts, entre les idées et entre les intelligences. Mû par ces considérations, j’ai développé la théorie sémantique de la cognition et le métalangage IEML : de leur union résulte la sémantique computationnelle.

Les spécialistes du sens, de la culture et de la pensée se sentent démunis face à la boîte à outils hétérogène de l’intelligence artificielle : ils n’y reconnaissent nulle part de quoi traiter la complexité contextuelle de la signification. C’est pourquoi la sémantique computationnelle leur propose de manipuler les outils algorithmiques de manière cohérente à partir de la sémantique des langues naturelles. Les ingénieurs s’égarent face à la multitude bigarrée, au flou artistique et à l’absence d’interopérabilité conceptuelle des sciences humaines. Remédiant à ce problème, la sémantique computationnelle leur donne prise sur les outils et les concepts foisonnants des insaisissables sciences humaines. En somme, le grand projet de la sémantique computationnelle consiste à construire un pont entre l’ingénierie logicielle et les sciences humaines de telle sorte que ces dernières puissent utiliser à leur service la puissance computationnelle de l’informatique et que celle-ci parvienne à intégrer la finesse herméneutique et la complexité contextuelle des sciences humaines. Mais une intelligence artificielle grande ouverte aux sciences humaines et capable de calculer la complexité du sens ne serait justement plus l’intelligence artificielle que nous connaissons aujourd’hui. Quant à des sciences humaines qui se doteraient d’un métalangage calculable, qui mobiliseraient l’intelligence collective et qui maîtriseraient enfin le médium algorithmique, elles ne ressembleraient plus aux sciences humaines que nous connaissons depuis le XVIIIe siècle : nous aurions franchi le seuil d’une nouvelle épistémè.