Archives for posts with tag: deep meaning

IEML (the Information Economy Meta Language) has four main directions of research and development in 2019: in mathematics, data science, linguistics and software development. This blog entry reviews them successively.

1- A mathematical research program

I will give here a philosophical description of the structure of IEML, the purpose of the mathematical research to come being to give a formal description and to draw from this formalisation as much useful information as possible on the calculation of relationships, distances, proximities, similarities, analogies, classes and others… as well as on the complexity of these calculations. I had already produced a formalization document in 2015 with the help of Andrew Roczniak, PhD, but this document is now (2019) overtaken by the evolution of the IEML language. The Brazilian physicist Wilson Simeoni Junior has volunteered to lead this research sub-program.

IEML Topos

The “topos” is a structure that was identified by the great mathematician Alexander Grothendieck, who “is considered as the re-founder of algebraic geometry and, as such, as one of the greatest mathematicians of the 20th century” (see Wikipedia).

Without going into technical details, a topos is a bi-directional relationship between, on the one hand, an algebraic structure, usually a “category” (intuitively a group of transformations of transformation groups) and, on the other hand, a spatial structure, which is geometric or topological. 

In IEML, thanks to a normalization of the notation, each expression of the language corresponds to an algebraic variable and only one. Symmetrically, each algebraic variable corresponds to one linguistic expression and only one. 

Topologically, each variable in IEML algebra (i.e. each expression of the language) corresponds to a “point”. But these points are arranged in different nested recursive complexity scales: primitive variables, morphemes of different layers, characters, words, sentences, super-phrases and texts. However, from the level of the morpheme, the internal structure of each point – which comes from the function(s) that generated the point – automatically determines all the semantic relationships that this point has with the other points, and these relationships are modelled as connections. There are obviously a large number of connection types, some very general (is contained in, has an intersection with, has an analogy with…) others more precise (is an instrument of, contradicts X, is logically compatible with, etc.).

The topos that match all the expressions of the IEML language with all the semantic relationships between its expressions is called “The Semantic Sphere”.

Algebraic structure of IEML

In the case of IEML, the algebraic structure is reduced to 

  • 1. Six primitive variables 
  • 2. A non-commutative multiplication with three variables (substance, attribute and mode). The IEML multiplication is isomorphic to the triplet ” departure vertex, arrival vertex, edge ” which is used to describe the graphs.
  • 3. A commutative addition that creates a set of objects.

This algebraic structure is used to construct the following functions and levels of variables…

1. Functions using primitive variables, called “morpheme paradigms”, have as inputs morphemes at layer n and as outputs morphemes at layer n+1. Morpheme paradigms include additions, multiplications, constants and variables and are visually presented in the form of tables in which rows and columns correspond to certain constants.

2. “Character paradigms” are complex additive functions that take morphemes as inputs and characters as outputs. Character paradigms include a group of constant morphemes and several groups of variables. A character is composed of 1 to 5 morphemes arranged in IEML alphabetical order. (Characters may not include more than five morphemes for cognitive management reasons).

3. IEML characters are assembled into words (a substance character, an attribute character, a mode character) by means of a multiplicative function called a “word paradigm”. A word paradigm intersects a series of characters in substance and a series of characters in attribute. The modes are chosen from predefined auxiliary character paradigms, depending on whether the word is a noun, a verb or an auxiliary. Words express subjects, keywords or hashtags. A word can be composed of only one character.

4. Sentence building functions assemble words by means of multiplication and addition, with the necessary constraints to obtain grammatical trees. Mode words describe the grammatical/semantic relationships between substance words (roots) and attribute words (leaves). Sentences express facts, proposals or events; they can take on different pragmatic and logical values.

5. Super-sentences are generated by means of multiplication and addition of sentences, with constraints to obtain grammatical trees. Mode sentences express relationships between substance sentences and attribute sentences. Super-sentences express hypotheses, theories or narratives.

6. A USL (Uniform Semantic Locator) or IEML text is an addition (a set) of words, sentences and super-sentences. 

Topological structure of IEML: a semantic rhizome

Static

The philosophical notion of rhizome (a term borrowed from botany) was developed on a philosophical level by Deleuze and Guattari in the preface to Mille Plateaux (Minuit 1980). In this Deleuzo-Guattarian lineage, by rhizome I mean here a complex graph whose points or “vertices” are organized into several levels of complexity (see the algebraic structure) and whose connections intersect several regular structures such as series, tree, matrix and clique. In particular, it should be noted that some structures of the IEML rhizome combine hierarchical or genealogical relationships (in trees) with transversal or horizontal relationships between “leaves” at the same level, which therefore do not respect the “hierarchical ladder”. 

Dynamic

We can distinguish the abstract, or virtual, rhizomatic grid drawn by the grammar of the language (the sphere to be dug) and the actualisation of points and relationships by the users of the language (the dug sphere of chambers and galleries).  Characters, words, sentences, etc. are all chambers in the centre of a star of paths, and the generating functions establish galleries of “rhizomatic” relationships between them, as many paths for exploring the chambers and their contents. It is therefore the users, by creating their lexicons and using them to index their data, communicate and present themselves, who shape and grow the rhizome…

Depending on whether circuits are more or less used, on the quantity of data or on the strength of interactions, the rhizome undergoes – in addition to its topological transformations – various types of quantitative or metric transformations. 

* The point to remember is that IEML is a language with calculable semantics because it is also an algebra (in the broad sense) and a complex topological space. 

* In the long term, IEML will be able to serve as a semantic coordinate system for the information world at large.

2 A research program in data science

The person in charge of the data science research sub-program is the software engineer (Eng. ENSIMAG, France) Louis van Beurden, who holds also a master’s degree in data science and machine translation from the University of Montréal, Canada. Louis is planning to complete a PhD in computer science in order to test the hypothesis that, from a data science perspective, a semantic metadata system in IEML is more efficient than a semantic metadata system in natural language and phonetic writing. This doctoral research will make it possible to implement phases A and B of the program below and to carry out our first experiment.

Background information

The basic cycle in data science can be schematized according to the following loop:

  • 1. selection of raw data,
  • 2. pre-processing, i.e. cleaning data and metadata imposition (cataloguing and categorization) to facilitate the exploitation of the results by human users,
  • 3. statistical processing,
  • 4. visual and interactive presentation of results,
  • 5. exploitation of the results by human users (interpretation, storytelling) and feedback on steps 1, 2, 3

Biases or poor quality of results may have several causes, but often come from poor pre-treatment. According to the old computer adage “garbage in, garbage out“, it is the professional responsibility of the data-scientists to ensure the quality of the input data and therefore not to neglect the pre-processing phase where this data is organized using metadata.

Two types of metadata can be distinguished: 1) semantic metadata, which describes the content of documents or datasets, and 2) ordinary metadata, which describes authors, creation dates, file types, etc. Let us call “semantic pre-processing” the imposition of semantic metadata on data.

Hypothesis

Since IEML is a univocal language and the semantic relationships between morphemes, words, sentences, etc. are mathematically computable, we assume that a semantic metadata system in IEML is more efficient than a semantic metadata system in natural language and phonetic writing. Of course, the efficiency in question is related to a particular task: search, data analysis, knowledge extraction from data, machine learning, etc.

In other words, compared to a “tokenization” of semantic metadata in phonetic writing noting a natural language, a “tokenization” of semantic metadata in IEML would ensure better processing, better presentation of results to the user and better exploitation of results. In addition, semantic metadata in IEML would allow datasets that use different languages, classification systems or ontologies to be de-compartmentalized, merged and compared.

Design of the first experience

The ideal way to do an experiment is to consider a multi-variable system and transform only one of the system variables, all other things being equal. In our case, it is only the semantic metadata system that must vary. This will make it easy to compare the system’s performance with one (phonetic tokens) or the other (semantic tokens) of the semantic metadata systems.

  • – The dataset of our first experience encompasses all the articles of the Sens Public scientific journal.
  • – Our ordinary metadata are the author, publication date, etc.
  • – Our semantic metadata describe the content of articles.
  •     – In phonetic tokens, using RAMEAU categories, keywords and summaries,
  •     – In IEML tokens by translating phonetic tokens.
  • – Our processes are “big data” algorithms traditionally used in natural language processing 
  •     – An algorithm for calculating the co-occurrences of keywords.
  •     – A TF-IDF (Term Frequency / Inverse Document Frequency) algorithm that works from a word / document matrix.
  •     – A clustering algorithm based on “word embeddings” of keywords in articles (documents are represented by vectors, in a space with as many dimensions as words).
  • – A user interface will offer a certain way to access the database. This interface will be obviously adapted to the user’s task (which remains to be chosen, but could be of the “data analytics” type).
  • Result 1 corresponds to the execution of the “machine task”, i.e. the establishment of a connection network on the articles (relationships, proximities, groupings, etc.). We’ll have to compare….
  •     – result 1.1 based on the use of phonetic tokens with 
  •     – result 1.2 based on the use of IEML tokens.
  • Result 2 corresponds to the execution of the selected user-task (data analytics, navigation, search, etc.). We’ll have to compare….
  •     – result 2.1, based on the use of phonetic tokens, with 
  •     – result 2.2, based on the use of IEML tokens.

Step A: First indexing of a database in IEML

Reminder: the data are the articles of the scientific journal, the semantic metadata are the categories, keywords and summaries of the articles. From the categories, keywords and article summaries, a glossary of the knowledge area covered by the journal is created, or a sub-domain if it turns out that the task is too difficult. It should be noted that in 2019 we do not yet have the software tools to create IEML sentences and super-phrases that allow us to express facts, proposals, theories, narratives, hypotheses, etc. Phrases and super-phrases, perhaps accessible in a year or two, will therefore have to wait for a later phase of the research.

The creation of the glossary will be the work of a project community, linked to the editors of Sens-Public magazine and the Canada Research Chair in Digital Writing (led by Prof. Marcello Vitali-Rosati) at the Université de Montréal (Digital Humanities). Pierre Lévy will accompany this community and help it to identify the constants and variables of its lexicon. One of the auxiliary goals of the research is to verify whether motivated communities can appropriate IEML to categorize their data. Once we are satisfied with the IEML indexing of the article database, we will proceed to the next step.

Step B: First experimental test

  • 1. The test is determined to measure the difference between results based on phonetic tokens and results based on IEML tokens. 
  • 2. All data processing operations are carried out on the data.
  • 3. The results (machine tasks and user tasks) are compared with both types of tokens.

The experiment can eventually be repeated iteratively with minor modifications until satisfactory results are achieved.

If the hypothesis is confirmed, we proceed to the next step

Step C: Towards an automation of semantic pre-processing in IEML.

If the superior efficiency of IEML tokens for semantic metadata is demonstrated, then there will be a strong interest in maximizing the automation of IEML semantic pre-processing

The algorithms used in our experiment are themselves powerful tools for data pre-processing, they can be used, according to methods to be developed, to partially automate semantic indexing in IEML. The “word embeddings” will make it possible to study how IEML words are correlated with the natural language lexical statistics of the articles and to detect anomalies. For example, we will check if similar USLs (a USL is an IEML text) point to very different texts or if very different texts have similar USLs. 

Finally, methods will be developed to use deep learning algorithms to automatically index datasets in IEML.

Step D: Research and development perspective in Semantic Machine Learning

If step C provides the expected results, i.e. methods using AI to automate the indexing of data in IEML, then big data indexed in IEML will be available.  As progress will be made, semantic metadata may become increasingly similar to textual data (summary of sections, paragraphs, sentences, etc.) until translation into IEML is achieved, which remains a distant objective.

The data indexed in IEML could then be used to train artificial intelligence algorithms. The hypothesis that machines learn more easily when data is categorized in IEML could easily be validated by experiments of the same type as described above, by comparing the results obtained from training data indexed in IEML and the results obtained from the same data indexed in natural languages.

This last step paves the way for a better integration of statistical AI and symbolic AI (based on facts and rules, which can be expressed in IEML).

3 A research program in linguistics, humanities and social sciences

Introduction

The semiotic and linguistic development program has two interdependent components:

1. The development of the IEML metalanguage

2. The development of translation systems and bridges between IEML and other sign systems, in particular… 

  •     – natural languages,
  •     – logical formalisms,
  •     – pragmatic “language games” and games in general,
  •     – iconic languages,
  •     – artistic languages, etc.

This research and development agenda, particularly in its linguistic dimension, is important for the digital humanities. Indeed, IEML can serve as a system of semantic coordinates of the cultural universe, thus allowing the humanities to cross a threshold of scientific maturity that would bring their epistemological status closer to that of the natural sciences. Using IEML to index data and to formulate assumptions would result in….

  • (1) a de-silo of databases used by researchers in the social sciences and humanities, which would allow for the sharing and comparison of categorization systems and interpretive assumptions;
  • (2) an improved analysis of data.
  • (3) The ultimate perspective, set out in the article “The Role of the Digital Humanities in the New Political Space” (http://sens-public.org/article1369.html in French), is to aim for a reflective collective intelligence of the social sciences and humanities research community. 

But IEML’s research program in the perspective of the digital humanities – as well as its research program in data science – requires a living and dynamic semiotic and linguistic development program, some aspects of which I will outline here.

IEML and the Meaning-Text Theory

IEML’s linguistic research program is very much based on the Meaning-Text theory developed by Igor Melchuk and his school. “The main principle of this theory is to develop formal and descriptive representations of natural languages that can serve as a reliable and convenient basis for the construction of Meaning-Text models, descriptions that can be adapted to all languages, and therefore universal. ”(Excerpt translated from the Wikipedia article on Igor Melchuk). Dictionaries developed by linguists in this field connect words according to universal “lexical functions” identified through the analysis of many languages. These lexical functions have been formally transposed into the very structure of IEML (See the IEML Glossary Creation Guide) so that the IEML dictionary can be organized by the same tools (e.g. Spiderlex) as those of the Meaning-Text Theory research network. Conversely, IEML could be used as a pivot language – or concept description language – *between* the natural language dictionaries developed by the network of researchers skilled in Meaning-Text theory.

Construction of specialized lexicons in the humanities and social sciences

A significant part of the IEML lexicon will be produced by communities having decided to use IEML to mark out their particular areas of knowledge, competence or interaction. Our research in specialized lexicon construction aims to develop the best methods to help expert communities produce IEML lexicons. One of the approaches consists in identifying the “conceptual skeleton” of a domain, namely its main constants in terms of character paradigms and word paradigms. 

The first experimentation of this type of collaborative construction of specialized lexicons by experts will be conducted by Pierre Lévy in collaboration with the editorial team of the Sens Public scientific journal and the Canada Research Chair in Digital Textualities at the University of Montréal (led by Prof. Marcello Vitali-Rosati). Based on a determination of their economic and social importance, other specialized glossaries can be constructed, for example on the theme of professional skills, e-learning resources, public health prevention, etc.

Ultimately, the “digital humanities” branch of IEML will need to collaboratively develop a conceptual lexicon of the humanities to be used for the indexation of books and articles, but also chapters, sections and comments in documents. The same glossary should also facilitate data navigation and analysis. There is a whole program of development in digital library science here. I would particularly like to focus on the human sciences because the natural sciences have already developed a formal vocabulary that is already consensual.

Construction of logical, pragmatic and narrative character-tools

When we’ll have a sentence and super-phrase editor, it is planned to establish a correspondence between IEML – on the one hand – and propositional calculus and first order logics – on the other hand –. This will be done by specifying special character-tools to implement logical functions. Particular attention will be paid to formalizing the definition of rules and the declaration that “facts” are true in IEML. It should be noted in passing that, in IEML, grammatical expressions represent classes, sets or categories, but that logical individuals (proper names, numbers, etc.) or instances of classes are represented by “literals” expressed in ordinary characters (phonetic alphabets, Chinese characters, Arabic numbers, URLs, etc.).

In anticipation of practical use in communication, games, commerce, law (smart contracts), chatbots, robots, the Internet of Things, etc., we will develop a range of character-tools with illocutionary force such as “I offer”, “I buy”, “I quote”, “I give an instruction”, etc.

Finally, we will making it easier for authors of super-sentences by developing a range of character-tools implementing “narrative functions”.

4 A software development program

A software environment for the development and public use of the IEML language

Logically, the first multi-user IEML application will be dedicated to the development of the language itself. This application is composed of the following three web modules.

  • 1. A morpheme editor that also allows you to navigate in the morphemes database, or “dictionary”.
  • 2. A character and word editor that also allows navigation in the “lexicon”.
  • 3. A navigation and reading tool in the IEML library as a whole, or “IEML database” that brings together the dictionary and lexicon, with translations, synonyms and comments in French and English for the moment.

The IEML database is a “Git” database and is currently hosted by GitHub. Indeed, a Git database makes it possible to record successive versions of the language, as well as to monitor and model its growth. It also allows large-scale collaboration among teams capable of developing specific branches of the lexicon independently and then integrating them into the main branch after discussion, as is done in the collaborative development of large software projects. As soon as a sub-lexicon is integrated into the main branch of the Git database, it becomes a “common” usable by everyone (according to the latest General Public License version.

Morpheme and word editors are actually “Git clients” that feed the IEML database. A first version of this collaborative read-write environment should be available in the fall of 2019 and then tested by real users: the editors of the Scientific Journal “Sens Public” as well as other participants in the University of Montréal’s IEML seminar.

The following versions of the IEML read/write environment should allow the editing of sentences and texts as well as literals that are logical individuals not translated into IEML, such as proper names, numbers, URLs, etc.

A social medium for collaborative knowledge management

A large number of applications using IEML can be considered, both commercial and non-commercial. Among all these applications, one of them seems to be particularly aligned with the public interest: a social medium dedicated to collaborative knowledge and skills management. This new “place of knowledge” could allow the online convergence of the missions of… 

  • – museums and libraries, 
  • – schools and universities, 
  • – companies and administrations (with regard to their knowledge creation and management dimension), 
  • – smart cities, employment agencies, civil society networks, NGO, associations, etc.

According to its general philosophy, such a social medium should…

  • – be supported by an intrinsically distributed platform, 
  • – have the simplicity – or the economy of means – of Twitter,
  • – ensure the sovereignty of users over their data,
  • – promote collaborative processes.

The main functions performed by this social medium would be:

  • – data curation (reference and categorization of web pages, edition of resource collections), 
  • – teaching offers and learning demands,
  • – offers and demands for skills, or employment market.

IEML would serve as a common language for

  • – data categorization, 
  • – description of the knowledge and skills, 
  • – the expression of acts within the social medium (supply, demand, consent, publish, etc.)
  • – addressing users through their knowledge and skills.

Three levels of meaning would thus be formalized in this medium.

  • (1) The linguistic level in IEML  – including lexical and narrative functions – formalizes what is spoken about (lexicon) and what is said (sentences and super-phrases).
  • – (2) The logical – or referential – level adds to the linguistic level… 
  •     – logical functions (first order logic and propositional logic) expressed in IEML using logical character-tools,
  •     – the ability of pointing to references (literals, document URLs, datasets, etc.),
  •     – the means to express facts and rules in IEML and thus to feed inference engines.
  • – (3) The pragmatic level adds illocutionary functions and users to the linguistic and logical levels.
  •     – Illocutionary functions (thanks to pragmatic character-tools) allow the expression of conventional acts and rules (such as “game” rules). 
  •     – The pragmatic level obviously requires the consideration of players or users, as well as user groups.
  •     – It should be noted that there is no formal difference between logical inference and pragmatic inference but only a difference in use, one aiming at the truth of propositions according to referred states of things, the other calculating the rights, obligations, gains, etc. of users according to their actions and the rules of the games they play.

The semantic profiles of users and datasets will be arranged according to the three levels that have just been explained. The “place of knowledge” could be enhanced by the use of tokens or crypto-currencies to reward participation in collective intelligence. If successful, this type of medium could be generalized to other areas such as health, democratic governance, trade, etc.

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

deepmeaning

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.

Bootstrapping

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.

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