Sign, symbol, language


Pas une pipe

Meaning involves at least three elements playing distinct roles. A sign (1) means something (2) for someone (3). A sign may be an entity or an event. What makes it a “sign”, is not its intrinsic properties, but the role it plays in meaning. A thing designated by a sign is often called the object or the referent and, what makes the referent of a sign is not its intrinsic properties but the role it plays in the triadic relation. The being is often called the subject or the interpreter. It may be a human being, a group, an animal, a machine or whatever entity or process endowed with self-reference (with the distinction self/environment) and interpretation. The interpreter always takes the context into account when it interprets a sign. For example, I (the interpreter) can smell smoke (sign) and infer it comes from something burning (a referent part of the context).

Communication and signs exist for any living organisms. Cells can recognize concentrations of poison or food from afar, plants use their flowers to trick insects into their reproductive processes, animal species practice complex semiotic games, including camouflage and mimicries. Animals – organisms with brains or nervous systems – acknowledge, interpret and emit signs constantly. Their cognition is complex: the sensorimotor cycle involves categorization, feeling, and environment mapping. Animals learn from experience, they solve problems, communicate and social species manifest collective intelligence. All these cognitive properties imply the emission and interpretation of signs. When a wolf growls, no need to add a long discourse, a clear message is sent to its adversary.


A symbol is a sign that divides into two parts: the signifier and the signified. The signified (virtual) is a general category or an abstract class, and the signifier (actual) is a tangible phenomenon that represents the signified. The signifier may be a sound, a black mark on white paper, a trace or a gesture. For example, let?s take the word “tree” as a symbol. It is made of 1) a signifier sound voicing the word tree, and 2) a signified concept: part of the family of plants with root, trunk, branches, and leaves. The relation between the signifier and the signified is conventional and depends on which symbolic system the symbol is part of (in this case: the English language). What we mean by conventional is that in most cases, there is no analogy or causal connection between the sound and the concept. For example, between the sound ?crocodile? and the crocodile species. We use different signifiers to indicate the same signified in different languages. Furthermore, the concepts symbolized by languages depend on the environment and culture of their speakers. In our example, the signified of the sound “tree” is decided by the English language, it is not left to the choice of the interpreter. However, the interlocutor picks the referent of the word according to the context of the speech act: is it a syntactic tree, a palm tree, a Christmas tree? Please, note the distinction between the concept and the referent.


By language, I mean a complete language, a general symbolic system that allows people to think reflexively, ask questions, tell stories, dialogue and engage in complex social interaction. English, French, Spanish, Arabic, Russian, Chinese Mandarin or Esperanto are languages. Every human being is biologically equipped to speak and recognize languages. The linguistic ability is natural, genetic, embedded in our brains and universal. In contrast languages (like English, French, etc.) are social, conventional, cultural, multiple, evolving and hybridizing. They mix and change according to the transformations of demographic, technological, economic, social and political contexts.

Our natural linguistic ability multiplies the cognitive faculties that we share with other social animals. It empowers reflexive thought, lasting and precise memory, fast learning, long-term planning, large-scale complex coordination and cultural evolution. Animals cannot understand and use linguistic symbols to their full extent, only humans can. Even the best-trained gorilla will not pretend that the story of another gorilla is false or exaggerated. It will neither ask you an appointment for the first Tuesday of the next month nor inform you where its grandfather was born.

In animal cognition, the categories that organize perception and action are enacted by neural networks. In human cognition, these categories become explicit thank to symbols and move to the forefront of our awareness. Ideas become objects of reflection. With language comes arithmetics, art, religion, politics, economy, and technology. Compared to other social species, human collective intelligence is more powerful and creative because it is supported and augmented by its linguistic ability. Therefore, if we work in data science, artificial intelligence or cognitive computing, it would be useful to understand – and model – not only the functioning of neurons and neurotransmitters, common to all animals but also the structure and organization of language, unique to our species.

Natural languages contain the possibility of logical reasoning and arithmetic computing but they cannot be reduced to these features. In this sense, programming languages like Python, Javascript or C++ are too specialized to be considered as complete languages. Their basic units are empty syntactic containers. No grandmother can tell a story in Python to her grandchildren and there are no words in OWL to say “butter” or “crocodile”.


A natural language is made of recursively nested units: a phoneme is an elementary sound, a word is a chain of phonemes, a sentence is a chain of words and a text is a chain of sentences. A language has a finite dictionary of words and syntactic rules for the construction of texts. From its dictionary and set of syntactic rules, a language offers its users the ability to generate – and understand! – an infinity of texts.


Humans cannot pronounce or recognize several phonemes simultaneously. They can only pronounce one sound at a time. So languages have to obey the constraint of sequentiality. A speech looks like a temporal chain of phonemes, with an acoustic punctuation reflecting its grammatical organization.

Phonemes are generally divided into consonants and vowels. Some languages have “ click ” consonants (in East and Southern Africa) and others (like Chinese) have tones on their vowels. Despite the great diversity of sounds used to pronounce human languages, the number of conventional sounds for a language is limited: the order of magnitude is between thirty and one hundred.


Phonemes are meaningless sounds without any signifier associated with it. The first symbolic unit, with a signifier related to a signifier, is the word. By “ word ” I mean an atomic sense unit. For example, technically, the expression “ smallest ” contains two words: “ small ” (meaning tiny) and “ est ” (meaning the most).

How many words does a language contain? The biggest English dictionary counts 200 000 words, Latin has 50 000 words, Chinese has 30 000 characters, the biblical Hebrew amounts to 6000 words. The French classical author Jean Racine was able to evoke the whole range of human passions with only 3700 words in all of his 13 plays. Most linguists think that whatever the language, as an order of magnitude, a skillful and cultivated speaker masters around 10 000 words.

All languages contain nouns depicting structures or entities and verbs describing actions, events, and processes. Depending on the language, there are other types of words, like adjectives, adverbs, prepositions or sense units marking the grammatical functions, the gender, the number, the person, the time, etc.

Note that a word cannot be true or false. As part of a language, its signifier points to a signified, an abstract category, and not to a state of things. Only a sentence that is spoken in context and pretends to describe a reality – a sentence that has a referent – can be true or false.


At the level of the sentence, we leave the abstract dictionary of a language to enter the concrete world of speech acts in contexts. First, let’s distinguish three sub-levels of complexity at the sentence level: the topic, the phrase, and the super-phrase. A topic is a super-word indicating a subject, a matter, an object or a process that cannot be described by one single word, i.e., “ history of linguistics ”, “ smartphone ” or “ tourism in Canada ”. Different languages have diverse rules for building topics like joining root-words to case-words or straight agglutination of words. By relating several topics, a phrase brings to mind an event, an action or a fact, i.e., “ I bought her a new smartphone for her twentieth birthday ”. A phrase can be verbal, like the previous example, or nominal like “ the blue seat of my father’s car ”. Finally, a super-phrase evokes a network of relations between facts or events, like a theory or a narrative. The relationships between phrases can be temporal (after), spatial (behind), causal (because), logical (therefore), they can underline contrasts (but, despite…) and so on.


The higher linguistic unit, or text, results from a punctuated sequence of sentences. A text has a signified resulting from the syntactic rules applied to the signifieds of its words. It also has a referent in the mind of its speaker, a referent that is inferred by its listeners from the signified of the text and from the temporal, spatial and social contexts of its utterance. Even when the text is in fact produced by a computer program, the listener cannot help but imagine an intention to mean something by a speaker and to construct the mental model of a referent.


When we listen to a speech, we transform a chain of sounds into a semantic network and we infer from this network a new mental model of our situation. Conversely, we are able to transform a mental model into the corresponding semantic network and then this network into a train of phonemes. Semantics is the back and forth translation between chains of phonemes and semantic networks. The semantic networks themselves are multi-layered and can be broken down into three levels: paradigmatic, syntagmatic and textual.

Paradigmatic relations

In any language dictionary, words are generally arranged in paradigms. A paradigm is a set of mutually exclusive words that cover a particular functional or thematic zone. For example, languages may comprise paradigms to indicate time (past, present, future) and mode (active, passive) of verbs. Most languages include paradigms for economic actions (buy, sell, lend, repay), or colors (red, blue, yellow…). For instance, a speaker may replace a word from a paradigm by another word from the same paradigm and still make sense. In the sentence “ I bought a car ” you can replace bought by sold because buy and sell are part of the same paradigm. But you cannot replace bought by yellow. Two words from the same paradigm are both opposed (they don’t have the same meaning) and related (they are exchangeable).

Words can also be related because they are in taxonomic relation, like horse and animal. The English dictionary indicates that a horse is a particular case of an animal. Words can also be composed of smaller words, for example, “ metalanguage ” comes from meta (beyond, second order) and language.

I will not write down here a complete list of all the relations that can be found between the words of a dictionary. The main point is that the words of a language are not isolated but inter-related by a dense network of semantic connections. In dictionaries, words are always defined and explained by the way of other words. Let’s call “ paradigmatic ” – in a very general sense – the relations between the words of a language. When we hear a sentence using the word “ sold ”, we know, in an implicit way, that “ sold ” is a verb, that it is opposed to “ bought ”, that it is not “ lent ”, and that it is the past tense of “ sell ”.

Syntagmatic relations

At a particular moment in time and in a definite situation of speakers, the relations between words in a language’s dictionary are constant. But in the speeches, the relations between words change according to their syntagmatic – or grammatical – roles. In the two following sentences: “ The gazelle smells the presence of the lion ” and “ The lion smells the presence of the gazelle ” the words “ gazelle ” and “ lion ” do not share the same grammatical role, so the words are not connected according to the same syntagmatic networks… therefore the sentences have distinct meanings. Syntagmatic networks can generally be reduced to a grammatical tree of verbal and nominal sentences (search «syntactic tree» on Google image).

Textual relations

At the grammatical level, a text is just a recognizable chain of sounds. But at the semantic level, texts are interconnected by relations like linguistic anaphoras and isotopies.

A text anaphoric links relate words or sentences to pronouns, conjunctions, etc. An example of anaphora is, when we read a pronoun, we know which noun – mentioned in a previous or following sentence – it is referring to.

On the other hand, isotopies are recurrences of themes that weave the unity of a text: the identity of heroes (characters), genres (love stories or historical novels), places, etc. These redundancies are essentially about words, paradigms, sentences and sentence structures. Iso-topia means “the same topic ” in greek. The notion of isotopy also encompasses all kinds of phonetic, prosodic, syntactic and narrative repetitions that help the listener to understand the text. From a sheer sequentiality of sentences, isotopies guide us into the construction of an intra-textual semantic network.


What does it mean to understand the meaning of a train of phonemes at the semantic level? It means that, from the sequence of sounds, we build a multi-layered semantic network: paradigmatic, syntagmatic and textual. When weaving the paradigmatic layer, we answer questions like: “ what is this word, to what paradigms does it belong? Which one of its senses should I consider? ” Then we connect words by responding to this kind of questions: “ what are the syntagmatic relations between the words in that sentence? ” Finally we interlace the texts by recognizing the anaphoras and isotopies that inter-connect their sentences. Our understanding of a text is this three-layered network of sense units.

Ambiguities can happen at all three levels and multiply their effects. In case of homophony, the same sound can point to two different words like “ ate ” and “ eight ”. Sometimes, one word may convey several distinct meanings like “ mole (1) ”, that means an animal digging galleries and “ mole (2) ” that means a deep undercover spy. In case of synonymy, the same meaning can be represented by distinct words like “ tiny ” and “ small ”. Amphibologies refer to syntagmatic ambiguities like in: “ Mary saw the woman on the mountain with a telescope. ” Who is on the mountain, Mary or the woman? Moreover, is it the mountain or the woman that has the telescope? Textual relations are even more ambiguous than paradigmatic and syntagmatic ones because the rules for anaphora and isotopy are loosely defined. Text understanding goes beyond grammar and vocabulary. It implies the building and comparison of complex and dynamic mental models. Human beings do not always resolve correctly all the ambiguities of speech and when they do, it is often by taking into account the pragmatic (or extra-textual) context, that is generally implicit… and out of the reach of computers.

Computers cannot understand or translate texts with the only help of a dictionary and a grammar because dictionaries and grammars of natural languages like English or Arabic have local versions, are fuzzy and evolve constantly. Moreover, textual rules change with social contexts, language games, and literary genres. Finally, computers cannot engage in the pragmatic context of speeches – like human beings do – to disambiguate texts. Natural language processing (a sub-discipline of artificial intelligence) compensate for the irregularity of natural languages by using a lot of statistical calculations and “ deep learning ” algorithms. Depending on its training set, the algorithm interprets a text by choosing the most probable semantic network. The results of these algorithms have to be validated and improved by human reviewers.


The word “ pragmatics ” comes from the ancient Greek pragma: “deed, act”. In their pragmatic sense, speeches are “acts” or performances. They do something. A speech may be descriptive and, in this case, it can be true or false. But a speech may also play a lot of other social functions like order, pray, judge, promise, etc. A speech act functions as a move in a game played by its speaker. So, distinct from the semantic meaning that we have analyzed in the previous section, the pragmatic meaning of a text is related to the kind of social game that is played by the interlocutors. For example, is the text pronounced on a stage in a play or in a real tribunal? The pragmatic meaning is also related to the real effects of its utterance, effects that are unknown at the moment of the pronunciation. For example: did I convince you? Have you kept your word? In the case of meaning as “ real effect ”, the sense of a speech can only be known after its utterance and future events can always modify it. The pragmatic ambiguity of a speech act comes from the ignorance about the time and place of the utterance, from the ignorance of the precise referents of the speech, from the uncertainty about of the social game played by the speaker, from the ambivalence or concealment of the speaker’s intentions and of course from the impossibility to know in advance the effects of an utterance.

Pragmatics is all about the triadic relation between symbols (speeches or texts), interpreters (people or interlocutors) and referents (objects, reality, extra-textual context). At the pragmatic level, any speech is pointing to – and acting on – a referential context that is common to the interlocutors. The pragmatic context is used for the disambiguation of the texts’ semantics and for the actualization of its deictic symbols (like: here, you, me, that one there, or next Tuesday). Indeed, the pragmatic context is often viewed by the specialists of natural language processing from the exclusive angle of disambiguation. But in the dynamics of communication, the pragmatic context is not only a tool for disambiguation but also – and more importantly – the common object that is at stake for the participants. The pragmatic context works like a shared and synchronized memory where interlocutors “write” and “read” their speeches – or other symbolic acts – in order to transform a real social situation.


I was happily surprised to be chosen as an “IBM influencer” and invited to the innovation and disruption Forum organized in Toronto the 16th of November to celebrate the 100th anniversary of IBM in Canada. With a handful of other people, I had the privilege to meet with Bryson Koehler the CTO of the IBM Cloud and Watson (Watson is the name given to IBM’s artificial intelligence). That meeting was of great interest to me: I learned a lot about the current state of cloud computing and artificial intelligence.


Image: Demonstration of a robot at the IBM innovation and disruption forum in Toronto

Contrary to other big tech companies, IBM already existed when I was born in 1956. The company was in the business of computing even before the transistors. IBM adapted itself to electronics and dominated the industry in the era of big central mainframes. It survived the PC revolution when Microsoft and Apple were kings. They navigated the turbulent waters of the social Web despite the might of Google, Facebook, and Amazon. IBM is today one of the biggest players in the market for cloud computing, artificial intelligence and business consulting.

The transitions and transformations in IBM’s history were not only technological but also cultural. In the seventies, when I was a young philosopher and new technology enthusiast, IBM was the epitome of the grey suit, blue tie, black attache-case corporate America. Now, every IBM employee – from the CEO Dino Trevisani to the salesman – wears jeans. IBM used to be the “anti-Apple” company but now everybody has a Mac laptop. Instead of proprietary technology, IBM promotes open-source software. IBM posters advertise an all-inclusive and diverse “you” across the specter of gender, race, and age. Its official management and engineering philosophy is design thinking and, along with the innovative spirit, the greatest of IBM’s virtues is the ability to listen!

Toronto’s Forum was all about innovation and disruption. Innovation is mainly about entrepreneurship: self-confidence, audacity, tenacity, resilience and market orientation. Today’s innovation is “agile”: implement a little bit, test, listen to the clients, learn from your errors, re-implement, etc. As for the disruption, it is inevitable, not only because of the speed of digital transformation but also because of the cultural shifts and the sheer succession of generations. So their argument is fairly simple: instead of being disrupted, be the disruptor! The overall atmosphere of the Forum was positive and inspirational and it was a pleasure to participate.

There were two kinds of general presentations: by IBM clients and by IBM strategists and leaders. In addition, a lot of stands, product demonstrations and informative mini-talks on various subjects enabled the attendees to learn about current issues like e-health and hospital applications, robotics, data management, social marketing, blockchain and so on. One of the highlights of the day was the interview of Arlene Dickinson (a well known Canadian TV personality, entrepreneur, and investor) by Dino Trevisani, the CEO of IBM Canada himself. Their conversation about innovation in Canada today was both instructive and entertaining.

From my point of view as a philosopher specialized in computing, Bryson Koehler (CTO for IBM cloud and Watson) made a wonderful presentation, imbued with simplicity and clarity, yet full of interesting content. Before being an IBMer Bryson worked for the Weather Channel, so he was familiar handling exabytes of data! According to Bryson Koehler, the future is not only the cloud, that is to say, infrastructure and software as a service, but also in the “cloud-native architecture“, where a lot of loosely connected mini-services can be easily assembled like Lego blocks and on top of which you can build agile and resilient applications. Bryson is convinced that all businesses are going to become “cloud natives” because they need the flexibility and security that it provides. To illustrate this, I learned that Watson is not a standalone monolithic “artificial intelligence” anymore but is now divided into several mini-services, each one with its API, and part of the IBM cloud offer alongside other services like blockchain, video storage, weather forecast, etc.

BrysonImage: Bryson Koehler at the IBM innovation and disruption Forum in Toronto

Bryson Koehler recognizes that the techniques of artificial intelligence,  the famous deep learning algorithms, in particular, are all the same amongst the big competitors (Amazon, Google, Microsoft and IBM) in the cloud business. These algorithms are now taught in universities and implemented in open source programs. So what makes the difference in IA today is not the technique but the quality and quantity of the datasets in use to train the algorithms. Since every big player has access to the public data on the web and to the syndicated data (on markets, news, finance, etc.) sold by specialized companies, what makes a real difference is the *private data* that lies behind the firewall of businesses. So what is the competitive advantage of IBM? Bryson Koehler sees it in the trust that the company inspires to its clients, and their willingness to confide their data to its cloud. IBM is “secure by design” and will never use a client’s dataset to train algorithms used by this client’s competitors. Everything boils down to confidence.

At lunchtime, with a dozen of other influencers, I had a conversation with researchers at Watson. I was impressed by what I learned about cognitive computing, one of IBM’s leitmotiv. Their idea is that the value is not created by replicating the human mind in a computer but in amplifying human cognition in real-world situations. In other words, Big Blue (IBM’s nickname) does not entertain the myth of singularity. It does not want to replace people with machines but help its clients to make better decisions in the workplace. There is a growing flow of data from which we can learn about ourselves and the world. Therefore we have no other choice than to automate the process of selecting the relevant information, synthesize its content and predict, as much as possible, our environment. IBM’s philosophy is grounded in intellectual humility. In this process of cognitive augmentation, nothing is perfect or definitive: people make errors, machines too, and there is always room for improvement of our models. Let’s not forget that only humans have goals, ask questions and can be satisfied. Machines are just here to help.

Once the forum was over, I was walking in front of the Ontario lake and thought about the similarity between philosophy and computer engineering: aren’t both building cognitive tools?

Toronto-boardwalkImage: walking meditation in front of the Lake Ontario after the IBM innovation and disruption Forum in Toronto

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: openness, transparency and commonality. But such ethical and practical guidelines are probably not powerful enough to help us crossing a new 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 licensing, 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, fosters trust and supports 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. A new open public space should encourage all participants to create, comment, categorize, assess and analyze its content.

Then, transparency is the very ground for trust and the precondition of an authentic dialogue. Data and people (including the administrators of a platform), should be traceable and audit-able. Transparency should be reciprocal, without distinction between the rulers and the ruled. Such transparency will ultimately be the basis for 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 this 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

This 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 and it will use “deep learning”: an artificial intelligence technology that employs specialized chips and algorithms that roughly mimic the learning process of neurons. Finally, to be completely up to date, the next public platform will enable blockchain-based payments, transactions, contracts and secure records

If a 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 comes up with some technical advantage compared to the existing commercial platforms. Moreover, this technical advantage should have appealing political and philosophical dimensions.

No one really fancies the dream of autonomous machines, specially considering the current limitations of artificial intelligence. Instead, we want 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 will 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! Right now deep learning is based on neural networks simulation. It is enough to model roughly animal cognition (every animal species has neurons) but it is not refined enough to model human cognition. The difference between animal cognition and human cognition is the reflexive thinking that comes from language, which adds a layer of semantic addressing on top of neural connectivity. Speech production and understanding is an innate property of individual human brains. But as humanity is a social species, language is a property of human societies. 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.

But there is a problem with natural languages (english, french, arabic, etc.), 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. But instead of using statistics, deep meaning adopts a regular and computable metalanguage. I have designed IEML (Information Economy MetaLanguage) from the beginning to optimize semantic computing. IEML words are built from six primitive symbols and two operations: addition and multiplication. The semantic relations between IEML words follow the lines of their generative operations. The total number of words do not exceed 10 000. From its dictionary, the generative grammar of IEML allows the construction of sentences at three layers of complexity: topics are made of words, phrases (facts, events) are made of topics and super-phrases (theories, narratives) are made of phrases. The higher meaning unit, or text, is a unique set of sentences. Deep meaning technology uses IEML as the semantic addressing system of a social database.

Given large datasets, deep meaning allows the automatic computing of semantic relations between data, semantic analysis and semantic visualizations. This new technology fosters semantic interoperability: it decompartmentalizes tags, folksonomies, taxonomies, ontologies and languages. When on line communities categorize, assess and exchange semantic data, they generate explorable ecosystems of ideas that represent their collective intelligence. Take note that the vision of collective intelligence proposed here is distinct from 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 nurture dialogue and reflexivity.

The main functions of the new public sphere


In the new public sphere, every netizen will act as an author, editor, artist, curator, critique, messenger, contractor and 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, in a primitive, simple way. 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 will offer a consistent service of collaborative data curation empowered by a common semantic addressing system.

Augmented by deep meaning technology, our public sphere will include a semantic metadata editor applicable to any document format. It will work as a registration system for the works of the mind. Communication will be 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 will self-translate in all natural languages and interconnect by conceptual proximity. The blockchain layer will allow any transaction to be recorded. The platform will remunerate authors and curators in collective intelligence coins, according to the public engagement generated by their work. The new public sphere will be grounded in the internet of things, smart cities, ambient intelligence and augmented reality. People will control their environment and communicate with sensors, software agents and bots of all kinds in the same immersive semantic space. Virtual worlds will simulate the collective intelligence of teams, networks and cities.


This IEML-based 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.

Emission de radio (Suisse romande), 25 minutes en français.

You-Tube Video (in english) 1h



Paul et Pierre - de dos

Paul, mon cousin, mon frère, mon ami,

Nous sommes nés à un an d’intervalle, presque à la même date, au milieu des années 1950, dans la communauté juive de Béja, en Tunisie.  Mon père Henri et sa sœur Nicole – ta mère – s’aimaient tendrement. Nos pères étaient associés dans la même boutique et nous jouions comme des frères dans l’arrière-boutique.

Très jeunes, l’histoire nous a balloté sur l’autre rive de la Méditerranée et nous avons atterri à Toulouse. C’est là que nos destins se sont séparés. Alors que tes parents tenaient bon et construisaient un foyer stable, j’ai été entraîné loin de l’Occitanie par les tourbillons d’un naufrage familial. Mais quand je revenais dans la ville rose visiter mon père pour les vacances de Pâques, ma tante Nicole bien aimée m’accueillait dans sa maison et elle était pour moi une véritable mère. Te souviens-tu quand nous allions ensemble à la bibliothèque, où quand tu me jouais au piano un morceau de musique que tu venais d’apprendre ?  Nous nous amusions d’un mot, d’un son, d’un geste, de tout et de rien. J’ai encore dans mes oreilles l’écho de nos rires…

Paul et Pierre au restau

Lorsque que tu faisais tes études de médecine, tu suivais en même temps des cours de philosophie à l’Université, en cachette de tes parents. Mais j’étais dans la confidence. A l’époque, nous avions d’homériques discussions sur les grands philosophes. Quand nous avons commencé à travailler et à fonder une famille, nous nous sommes un peu perdu de vue. Mais quelle fête, quelle joie, quand nous avions l’occasion de nous revoir ! Paul, tu étais ma référence, un autre moi-même, une version différente de mon destin. Nos deux vies étaient parallèles, elles rimaient comme Pierre et Paul.

Tu étais pour moi une manière de héros : tu aidais les mères à mettre leurs bébés au monde ! Médecin de garde, debout la nuit, tu opérais dans l’urgence pour sauver des vies. Consciencieux, responsable, tu étais toujours au fait des derniers développements de ta spécialité. Moi, quatre fois déraciné, j’enviais le médecin toulousain honorablement connu dans sa ville, aimé de ses patients et de leur famille.

J’aimais errer des heures dans ta bibliothèque de grand humaniste. Lucide, tu t’inquiétais partout de la tentation de la bonne conscience satisfaite. Tu étais ouvert, curieux de l’autre, mais sans jamais renier ton identité. Tu ne t’arrêtais pas à l’opinion moutonnière. Tu étais drôle, sympathique, bon vivant et généreux, mais aussi droit, honnête et authentique jusqu’à la rugosité. Je t’aimais, Paul. Qui ne t’aimait pas ? Ton humanité transparaissait immédiatement dans ton sourire et dans tes gestes.

Paul et Pierre Shabbat

Chacun a son Paul Boubli : le fils, le frère, l’époux, le père, le médecin, l’ami, le collègue… Mon Paul à moi, c’est le jumeau karmique, l’alter ego, l’âme sœur. Paul ! Notre dialogue a duré soixante ans. Mon cœur se brise mille fois à la pensée de ne plus te revoir… Rien n’efface la douleur de te perdre. Mais tu as engendré et élevé avec ta chère épouse Véronique quatre merveilleux enfants qui restent avec nous : Zacharie, Esther, Joseph et Samson. Mais tu lègues un héritage : le bien que tu as fait autour de toi, les étincelles que tu as semé dans nos vies. Par la blessure de mon cœur brisé, je recueille ces étincelles dans ma mémoire. Comprendre, aider, soigner, donner, éclairer le monde autour de soi, voilà l’exemple de courage que tu montres à chacun de nous. Toi – Prince d’une secrète noblesse andalouse – voici que de l’autre côté des larmes, de l’autre côté du temps, tu nous transmets le flambeau.

Bricologie & Sérendipité

Nous avons à résoudre des problèmes complexes au sens d’Edgar Morin : énergie, alimentation, dérèglement climatique, etc, que nous retrouvons “imbriqués” dans le domaine des transports. Individuellement, de nombreuses personnes perçoivent les enjeux et ont identifié des solutions. Mais collectivement les organisations, dans lesquelles ils évoluent, restent bloquées dans des processus et des schémas de décision, sans réelle capacité à évoluer et se transformer à la hauteur. Une des pistes pour expliquer ce paradoxe se trouve dans les mécanismes de l’intelligence collective.

L’intelligence collective est une propriété du vivant qui se manifeste quand plusieurs personnes interagissent avec un objectif commun : trouver une solution, développer un produit, réaliser une oeuvre ou une activité sportive. Un groupe de musique, une équipe de foot ou un service d’une entreprise mettent en oeuvre des actions coordonnées différentes en fonction de leur intelligence collective avec plus ou moins de réussite.

En effet, cette dernière…

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“Au pays de Numérix” d’Alexandre Moatti date de 2015, mais il est plus que jamais d’actualité, au moment où Mounir Mahjoubi vient d’être nommé secrétaire d’état au numérique du gouvernement Edouard Philippe. Beaucoup de gens attendent du nouveau président de la République française, jeune et réputé moderniste, un “cours nouveau” en matière de numérique en France. On ne saurait trop recommander la lecture de ce livre à son entourage.

Sur la forme c’est un ouvrage court, facile à lire, qui cultive un ton mesuré et rationnel. Il évoque le plus souvent des sujets que l’auteur connaît de première main, ce qui ne gâte rien. Franchement partisan des usages cognitifs du réseau et de “l’Internet de la connaissance” l’auteur a lui même oeuvré dans le domaine des bibliothèques numériques, a créé plusieurs sites web de type savant et participe de manière active à Wikipedia en français. Même s’il ne cite pas explicitement ces philosophes, on le sent opposé aux diatribes anti-GAFA – Google Apple Facebook Amazon – hystériques de Bernard Stiegler ou Eric Sadin, tout comme aux jugements négatifs à l’emporte pièce d’Alain Finkelkraut sur Internet. Mais il prend soin également de signaler certains aspects négatifs ou fâcheux de l’internet contemporain et de se distinguer du transhumanisme apocalyptique d’un Raymond Kurzweil ou du lyrisme a-critique d’un Pierre Lévy…

Une bonne partie de l’ouvrage est consacré aux réponses françaises et européennes au projet de Google Books autour de 2005. A l’origine, Google voulait utiliser ses centres de calcul et son algorithme de recherche pour construire une bibliothèque d’Alexandrie des temps modernes : tous les livres à disposition de tout le monde sur Internet! La France et l’Europe se devaient de relever le défi américain. Mais l’auteur montre que leurs réponses obéissent à des “effets de manche”, à des logiques d’annonce ou de communication politiques, à des stratégies de pouvoir et de captation de fonds publics par diverses institutions pour aboutir en fin de compte à d’infimes résultats concrets. Je note de mon côté que même si Google Books existe et rend des services (gratuits) au public et aux chercheurs, le projet initial est venu se fracasser sur la législation des droits d’auteurs, comme l’explique bien ce récent article de Wired. Tout cela permet de comprendre le succès d’entreprises illégales mais populaires comme la bibliothèque Genesis.

Au pays de Numérix, il y a beaucoup d’idéologie anti-américaine et anti-capitaliste… mais l’auteur montre que l’état – balkanisé par des baronnies ministérielles et institutionnelles en concurrence – travaille en fait au service d’intérêts sectoriels ou privés au lieu de mettre les capacités techniques de la France et l’argent du contribuable au service du public. Le bilan est accablant: projet après projet, les leçons des échecs ne sont jamais tirées et les mêmes erreurs sont répétées. Comme si, face à la domination de la Silicon Valley, il suffisait de s’indigner et de jeter des millions d’euros par la fenêtre pour que l’Europe ou la France (re)trouvent leur place dans le monde.

Au delà des divers projets de bibliothèques numériques européennes, Alexandre Moatti montre comment sont bloquées la collaboration des savants, la diffusion des connaissances et le rayonnement de la haute culture sur Internet. Trois coupables travaillent de conserve: la législation contemporaine des droits d’auteurs, d’ineptes politiques publiques et la rapacité des grandes maisons européennes de l’édition scientifique (Elsiever, Springer). Les arguments – de bon sens – mis en avant par Moatti ne sont pas nouveaux. Ils reprennent largement les idées du mouvement international de l’open data en général et de l’open science en particulier. Mais le réquisitoire est fort bien articulé. Il rejoint d’ailleurs les réflexions contemporaines autour de la nécessaire réinvention de l’édition scientifique (voir par exemple le récent article de Marcello Vitali-Rosati).

En refermant l’ouvrage, je n’ai pas pu m’empêcher de penser que, même s’il se trouvait à la tête de l’état français des gens conscients de l’importance capitale de l’internet au service de la connaissance et désireux de réformer les mauvaises habitudes de l’administration à cet égard, leur action ne serait pas forcément couronnée de succès. Car il faudrait faire évoluer les mentalités en profondeur, convaincre les enseignants, les journalistes, les hauts fonctionnaires. Il faudrait que la société dans son ensemble réalise que la grande transformation du numérique n’est pas seulement technique ou industrielle, mais concerne aussi et surtout le savoir et la culture. Il faudrait s’aviser que la civilisation du futur est à inventer et que cela ne se fait pas à coup de peur et de ressentiment, mais de courage, d’imagination et d’expérimentation.


L’accès du grand public à la puissance de diffusion du Web ainsi que les flots de données numériques qui coulent désormais de toutes les activités humaines nous confrontent au problème suivant : comment transformer les torrents de données en fleuves de connaissances ? Certains observateurs enthousiastes du traitement statistique des « big data », comme Chris Anderson, (l’ancien rédacteur en chef de Wired), se sont empressés de déclarer que les théories scientifiques – en général! – étaient désormais obsolètes [Voir : de Chris Anderson « The End of Theory: The Data Deluge Makes the Scientific Method Obsolete », Wired, 23 juin 2008.] Nous n’aurions plus besoin que de mégadonnées et d’algorithmes statistiques opérant dans les centres de calcul : les théories – et donc les hypothèses qu’elles proposent et la réflexion dont elles sont issues – appartiendraient à une étape révolue de la méthode scientifique. Il paraît que les nombres parlent d’eux-mêmes. Mais c’est évidemment oublier qu’il faut, préalablement à tout calcul, déterminer les données pertinentes, savoir exactement ce que l’on compte, et nommer – c’est-à-dire catégoriser – les patterns émergents. De plus, aucune corrélation statistique ne livre directement des relations causales. Celles-ci relèvent nécessairement d’hypothèses qui expliquent les corrélations mises en évidence par les calculs statistiques. Sous couvert de pensée révolutionnaire, Chris Anderson et ses émules ressuscitent la vieille épistémologie positiviste et empiriste en vogue au XIXe siècle selon laquelle seuls les raisonnements inductifs (c’est-à-dire uniquement basés sur les données) sont scientifiques. Cette position revient à refouler ou à passer sous silence les théories – et donc les hypothèses risquées fondées sur une pensée personnelle – qui sont nécessairement à l’oeuvre dans n’importe quel processus d’analyse de données et qui se manifestent par des décisions de sélection, d’identification et de catégorisation. On ne peut initier un traitement statistique et interpréter ses résultats sans aucune théorie. Le seul choix que nous ayons est de laisser les théories à l’état tacite ou de les expliciter. Expliciter une théorie permet de la relativiser, de la comparer avec d’autres théories, de la partager, de la généraliser, de la critiquer et de l’améliorer [Parmi la très abondante littérature sur le sujet, voir notamment les ouvrages de deux grands épistémologues du XXe siècle, Karl Popper et Michael Polanyi]. Cela constitue même une des principales composantes de ce qu’il est convenu d’appeler « la pensée critique », que l’éducation secondaire et universitaire est censée développer chez les étudiants.

Outre l’observation empirique, la connaissance scientifique a toujours eu à voir avec le souci de la catégorisation et de la description correcte des données phénoménales, description qui obéit nécessairement à des théories plus ou moins formalisées. En décrivant des relations fonctionnelles entre des variables, la théorie offre une prise conceptuelle sur le monde phénoménal qui permet (au moins partiellement) de le prévoir et de le maîtriser. Les données d’aujourd’hui correspondent à ce que l’épistémologie des siècles passés appelait les phénomènes. Pour continuer de filer cette métaphore, les algorithmes d’analyse de flots de données correspondent aux instruments d’observation de la science classique. Ces algorithmes nous montrent des patterns, c’est-à-dire en fin de compte des images. Mais ce n’est pas parce que nous sommes capables d’exploiter la puissance du médium algorithmique pour « observer » les données qu’il faut s’arrêter en si bon chemin. Nous devons maintenant nous appuyer sur la puissance de calcul de l’Internet pour « théoriser » (catégoriser, modéliser, expliquer, partager, discuter) nos observations, sans oublier de remettre cette théorisation entre les mains d’une intelligence collective foisonnante.

Tout en soulignant la distinction entre corrélation et causalité dans leur livre de 2013 sur les big data, Viktor Mayer-Schonberger  et Kenneth Cukier annoncent que nous nous intéresserons de plus en plus aux corrélations et de moins en moins à la causalité, ce qui les range dans le camp des empiristes. Leur livre fournit néanmoins un excellent argument contre le positivisme statistique. Ils racontent dans leur ouvrage la très belle histoire de Matthew Maury, un officier de marine américain qui, vers le milieu du XIXe siècle, agrégea les données des livres de navigation figurant dans les archives officielles pour établir des cartes fiables des vents et des courants [In Big Data: A Revolution… (déjà cité) p. 73-77]. Certes, ces cartes ont été construites à partir d’une accumulation de données empiriques. Mais je fais respectueusement remarquer à Cukier et Mayer-Schonberger qu’une telle accumulation n’aurait jamais pu être utile, ou même simplement faisable, sans le système de coordonnées géographique des méridiens et des parallèles… qui est tout sauf empirique et basé sur des données. De la même manière, ce n’est qu’en adoptant un système de coordonnées sémantique que nous pourrons organiser et partager les flots de données de manière utile.

Aujourd’hui, la plupart des algorithmes qui gèrent l’acheminement des recommandations et la fouille des données sont opaques, puisqu’ils sont protégés par le secret commercial des grandes compagnies du Web. Quant aux algorithmes d’analyse ils sont, pour la plupart, non seulement opaques mais aussi hors d’atteinte de la majorité des internautes pour des raisons à la fois techniques et économiques. Or il est impossible de produire de la connaissance fiable au moyen de méthodes secrètes. Bien plus, si l’on veut résoudre le problème de l’extraction d’information utile à partir du flot diluvien des big data, on ne pourra pas éternellement se limiter à des algorithmes statistiques travaillant sur le type d’organisation de la mémoire numérique dont nous disposons en 2017. Il faudra tôt ou tard, et le plus tôt sera le mieux, implémenter une organisation de la mémoire conçue dès l’origine pour les traitements sémantiques. On ne pourra apprivoiser culturellement la croissance exponentielle des données – et donc transformer ces données en connaissance réfléchie – que par une mutation qualitative du calcul.

Retenons que la « science des données » (data science en anglais) devient une composante essentielle de la compréhension des phénomènes économiques et sociaux. Plus aucune organisation ne peut s’en passer. Au risque de marcher à l’aveugle, les stratégies économiques, politiques et sociales doivent s’appuyer sur l’art d’analyser les mégadonnées. Mais cet art ne comprend pas seulement les statistiques et la programmation. Il inclut aussi ce que les américains appellent la « connaissance du domaine » et qui n’est autre qu’une modélisation ou une théorie causale de la réalité analysée, théorie forcément d’origine humaine, enracinée dans une expérience pratique et orientée par des fins. Ce sont toujours les humains et leurs récits producteurs de sens qui mobilisent les algorithmes.

Références documentaires

Voir ma collection d’articles sur les “Big Data” dans Les tags peuvent être utilisés pour naviguer dans la collection.