Emergence

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

Nature

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

Culture

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

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

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

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

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

Algo-medium

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

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

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

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

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

Algo-intel

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

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

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

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

Collective-Intelligence

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

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

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

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

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

To know more about the software design of the semantic sphere go here.

To dig into the philosophical concept of algorithmic intelligence go there

Read first : Architecture of the Semantic Sphere

A. SYNTAX

architecture3-syntax.v1

Figure A

A. 1. SCRIPT LOGIC

For a general introduction, see the following sections of the Grammar:

  • 2.1 and 2.2

A. 1.2 Script parser

For the rules on multiplication, addition, delimiters, characters, abbreviations, order, limits on recursivity (7 layers from 0 to 6), etc. see Grammar section:

  • 2.2.6
  • 2.2.7
  • 2.2.8

A. 1.3 Script data structure

See the following sections of the Grammar:

  • 2.1 to 2.2.2
  • 4.1 to 4.2
  • 5.2 to 5.4

Each unique Script is the representation of a unique expression of the IEML regular language (IEML algebra): a set of sequences of primitives at the same length.

A. 2. WORKING SOFTWARE Version 1 (Steve Newcomb, 2008): http://starparser.ieml.org/

Version 2 (Andrew Roczniak, 2015): http://test-ieml.rhcloud.com/ScriptParser/tester.jsp

B. DICTIONARY architecture4-dictionary.v1

Figure B

B. 1 DICTIONARY LOGIC

Requierments: for a general introduction, see the following sections of the Grammar:

– 3.1.1 to 3.1.6

– 4.3

B.1.2 Dictionary generation functions

•    Input : script data

•    Decompose the initial or « key » script until singular sequences are reached

•    Build the syntactic network of relations:

⁃    Multiplicative order between scripts of the paradigm and scripts from paradigms of layer n-1

⁃    Multiplicative symmetry

⁃    Additive order

⁃    Additive symmetry

⁃    Diagonal relation

1. IF  the terms of the paradigm have a constant part

2. IF the variable part of a term is a « twin » (it has identical substance and attribute at the same layer)

3. THEN every term meeting condition 2. of a paradigm meeting condition 1 is in diagonal relation with the other terms of the paradigm that meet condition 2.

See for example O:M:.O:M:.-we.h.-’

•    Build the semantic network of relations

⁃    Multiplicative order (etymology) can be de-selected for all or some syntactic roles of a paradigm

⁃    Multiplicative symmetry can be deselected for a paradigm. Deselection of the multiplicative symmetry cancels the diagonal relation at the semantic level.

•    Connect names to scripts in order to create terms (see: dictionary requierments)

⁃    We define associative tables (key, value) for each natural language. The keys are scripts (unique) and the values are names (names are unique in a table defined by its natural language). When a script is related to a name in one of these tables, it is a term.

•    Build definitions from user edition (see « Dictionary requierments » section 2)

⁃    First root of the paradigm (key term corresponding to the initial script): the NL name is a property and a definition

⁃    Intermediary leaf (variable term): the NL name is a property given by the user and the NL definition is automatically generated.

⁃    Final leaf (constant term): the NL name is an example given by the user and the NL definition is automatically generated.

B.1.3 Dictionary data structure (rules)

B.1.3.1 Identities

•    Each term is composed of a valid script and a name edited by the user

•    No term of the dictionary have the same name in the same natural language

•    Each script belong to one single paradigm

•    Each term belong to one single paradigm

•    Each term has a generated definition (the definition of a key is its name)

B.1.3.2 Relations

•    All the scripts of a paradigm are intervowen by the syntaxic network of relations

•    Terms of different paradigms can only be connected through etymologic relations (× order relations).

•    Semantic network of a term: all the connections of a term are semantic = no term is connected to a non-named script.

•    All terms have at least 2 types of relations: + order and + symmetry

•    All terms have at maximum 5 types of relations: + order, + symmetry, × order, × symmetry, diagonal.

B. 2 DICTIONARY INTERACTION

B. 2.1 Dictionary visualization

•    Tables requierments (generation rules)

•    Index (list of terms) visualized by :

⁃    NL,

⁃    layers,

⁃    classes,

⁃    tables_only / all_terms

•    Relations between terms: list of relations for each term / NL

•    One sequence of tables automatically generated for a paradigm

•    Additional tables generated form variable terms chosen by the user (the new tables add in the index)

•    Tables can be visualized in script mode (visualize all scripts on the tables) or in term mode (visualize only terms on the tables)

B. 2.2 Dictionary edition

The edition of the dictionary occurs in a « table » environment.

C. PROPOSITIONS AND TEXTS architecture5-proposition.v1

Figure C1

C1. PROPOSITION LOGIC

C1. 1 Proposition Parser

C1.1.1 Structure of a proposition

See grammar 3.2.1 to 3.2.4 and 3.2.8

COMPONENTS

A = IEML term in natural language

B = IEML word

C = IEML phrase

E = IEML emptiness (void role)

+ addition

() container of addition

× multiplication

[] container of multiplication

/ end of proposition

SEVEN PROPOSITIONAL LEVELS

WORD

1. term-word: A

2. morpheme-word: (A+A+…)

3. inflected-word: [(A+A+…)x(E)x(A+A+…)]

PHRASE

4. clause: [BxBxB]

5. complex phrase: ([BxBxB]+[BxBxB]+[BxBxB]+…)

SUPER-PHRASE

6. super-clause: [CxCxC]

7. complex super-phrase: ([CxCxC]+[CxCxC]+[CxCxC]+…)

C1.1.2 Rules

– Check that there are three and only three variables for each [×]

– Check that several identical terms are not added in the same [+]

– Check script order in all (+)

– Check that all variables of the same operation have the same layer

– If variables of the same operation have not the same layer, promote variables to match the higher layer (See paragraphs 2.2.6.3, 3.2.1.4 and 3.2.8.2 of the grammar)

C1.2.1 Grammatical roles

– All the nodes that are variables of a multiplication have a grammatical role label

– substance

– attribute

– mode

C1.2.2 Propositional level

– All the nodes have a propositional level label

1. term-word

2. morpheme-word

3. inflected-word

4. clause

5. complex phrase

6. super-clause

7. complex super-phrase

C1.2.3 Grammatical classes

– All the nodes from propositional levels 1, 2, 3, 4 have a grammatical class label

– noun

– verb

– auxilliary

C2.1 TEXT LOGIC architecture5-text.v1

Figure C2

A text is composed by a USL and a tag

C2.1.1. USL

For the structure of USLs see Grammar  3.2.5

A USL begins by a star *

A USL ends with 2 stars **

Inside a USL, each proposition is followed by a slash /

The propositions are ordered by (labelled) propositional levels 1. term-word 2. morpheme-word 3. inflected-word 4. clause 5. complex phrase 6. super-clause 7. complex super-phrase

Inside each level the propositions are ordered by the script order. Ordering (propositional level, script order) is automatic.

C2.1.2 Tag

The tag begins with a hash sign #

The tag ends with one title by natural language (and only one single title by unique natural language).

Example: #CurationForLearning

Identical titles in the same natural language are not allowed.

2.2 TEXT AND PROPOSITION EDITOR VIEWER (@CHRISTAN AND @BENCE)

The editor is a tool to allow users to create USL’s using the terms contained in the dictionary.  The parser and ordering rules will only allow the creation of valid and properly ordered USL’s.

•    New USL’s are created by clicking on the “New USL” button.

•    Creating a new USL’s reveals the USL editor at the bottom of the screen (fixed position).

•    New USL’s can be aborted by clicking cancel in the editor.

•    Terms are added by simply clicking the add button on the desired term in the dictionary index or search results list.

•    Terms can also be added from tables in the term view (paradigmatic circuits).

•    Terms can be reordered by dragging and dropping in the editor window.

•    Terms can be deleted by dragging onto the trash icon in the bottom right of the editor.

•    Operations ( +,x ) and related constraints ( (),[ ], / ) can be added to a USL by clicking on the respective buttons at the top of the editor.

•    Operation and related constraints can be placed in-between terms by dragging and dropping.

•    Once saved, the USL will be evaluated for proper script order and adjustments will be made automatically

•    Saved USL’s are stored in the current user’s “libray”.

•    Stored USL’s can be duplicated by clicking on the duplication button located next to the term in the vocabulary list.

•    USL’s can be deleted by clicking on the delete button located next to the term in the vocabulary list.

D LIBRARY

architecture6-library

Figure D

D1. BOOLEAN SEARCH IN THE LIBRARY (OR, AND, EXCEPT)

– by term

– by proposition

– by tag

– by author

– by date of creation

– by natural language

D2.  LIBRARY DATA STRUCTURE

A library is a set of texts. A text has three main components:

– an author

– a tag (by NL)

– a USL (an ordered list of propositions)

Having in input:

– these three components of a text

– a set of texts = a library (selected by boolean search or otherwise)

– the dictionary,

Five related data sets may be computed in output:

1.    The set of texts of the library written by the same author

2.    The set of texts of the library having the same USL

3.    For each proposition, the set of texts of the library containing this proposition

4.    A lexicon (set of terms) of the text

5.    For each term, the set of texts of the library containing this term

D3. HYPERTEXTUAL NAVIGATION IN THE LIBRARY

– author button—> clicable list of tags of the texts written by the author

– tag button—> clicable list of tags of the texts having the same USL

– proposition button—> clicable list of tags of the texts containg the same proposition

– term button —> clicable list of tags of the texts containg the same term —> corresponding paradigmatic table

D4. DATA, PEOPLE, TIME IN IEML LIBRARIES

Social and temporal aspects will grow and complexify with time. I am only interested here by the most basic aspects that will suffice for the beginning.

Paradigms

– authors Right to transform (NL) and to delete (Script) the paradigm that he/she has created

– versions

– translators have rights to add examples to existing translations and to transform and to delete his/her original translation

– translation versions

Texts

– authors have the right to transform the USL and the tag of the text that he/she has created

– versions

– translators have the right to translate the tag of a text

– translation versions

0. INTRODUCTION

The main objective of the Semantic Sphere (a software in the cloud) is the emergence of a reflexive collective intelligence from the data curation of its users. This project is relevant for collaborative learning and knowledge management (and even for religious studies!). For the moment, the Semantic Sphere is a scientific project developped in an academic environment.  The IEML semantic code v.1 (see Fig. 1) is under construction and wil be released at the end of 2015.

1. SOFTWARE ARCHITECTURE

architecture1-framework1.v2

FIGURE 1

The Semantic Sphere software is released under licence GPL version 3 and following. The IEML semantic code platform (in short: IEML) plays the role of a common coding system (or protocol, or operating system) for all the subsequent platforms. Each new version of IEML is automatically imported and used by the subsequent platforms. The first platform to be developped after the release of the first version of IEML will be the Semantic Space.

2. COMMON ARCHITECTURE OF PLATFORMS AND THEIR LEVELS architecture1-framework2.v1

FIGURE 2

Any platform and any level (or module) of a platform is organized into…

– a stable logical core (data generation function leading to data-structures)

– an interaction plug-in that renders the data structures for the users and allows the users to command the data generation functions.

There may be many different interaction plugins for one stable logical core. Information flows from edition to data generation, then to data structure, rendition and back to edition. The machine time-stamps and records (and can read) all the functions commanded by the users.

3. SOFTWARE ARCHITECTURE OF THE SEMANTIC SPHERE

architecture1-framework3.v2

FIGURE 1.3

The Semantic Sphere is the software composed by IEML and the Semantic Space.

3.1 IEML

IEML has five logical levels. The cores of these levels are perfectly aligned and compatible as if their was one single logical core. Each logical level corresponds to a literary function:

  • syntax,
  • dictionary,
  • proposition and text grammar,
  • library.

The IEML dictionary has translations (built by the users) in natural languages (NL). The dictionary and the grammar allows for automatic translations of propositions and texts written in IEML. A plug-in unifies the interaction between IEML and the users.

3.2 Space

The Semantic Space has three logical levels. The cores of theses levels corresponds to three interdependant functions:

– data indexation

– semantic analysis of the data (algebraic operations)

– semantic mapping of the data into spatio-temporal forms (geometric operations)

A plug-in unifies the interaction between the users and the 3 core functions of the Space. To get more information about the Semantic Space, you can read this document in french (mainly mathematical).

4. SOCIAL ARCHITECTURE OF THE SEMANTIC SPHERE

architecture1-framework4.v2

FIGURE 1.4

4.1 Universal rights (visitors)

  • Reading All users have the right to read all the code (soft or semantic) written by other users
  • Writing (light grey of fig. 1.4) All users have the rights to write:

– IEML propositions and IEML texts in the library

– Natural language translations of IEML texts

– data indexation in the Semantic Space

– data indexation rules in the Semantic Space

4.2 Distincts Privileges (members)

Priviliges are only writing privileges. Writing privileges can add-up.

4.2.1 IEML privileges

Programming

– IEML core

– IEML plugin Semantic coding

– IEML paradigms (dictionary): by versions

– Dictionary translation: by languages and versions)

4.2.2 Semantic Space privileges

– Core programming (by versions)

– Plugin programming (by plugins and versions)

4.3 Social relations architecture2-scaffoldingB.v1

Figure 1.5

Social relations are organized by the user/coder relation and should be managed according to the IEML conversation/collaboration process (Figure 1.5)

– All users are coders

– Semantic coders are the users of soft coders

– Plugin coders are the users of core coders

– Space coders (soft and semantic) are the users of IEML coders (soft and semantic)

– Paradigms translators are the users of paradigm authors

– Texts translators are the users of text authors

– Texts authors and translators are the users of paradigms authors and translators

– For the semantic coders in the Space: semantic indexation authors are the users of semantic indexation rulers (groups, communities, games)

To read the next IEML developement paper, clic HERE: Architecture of the IEML Software

DansL'usine

MY TRIP

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

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

STORIES FROM MY PERSONAL EXPERIENCE

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

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

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

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

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

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

MY TAKE-AWAY ADVICE FOR IBERTIC AND THE EDUCATIONAL COMMUNITY IN ARGENTINA

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

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

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

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

I recommand this video in spanish about Personal Learning Environments

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

THE EVOLUTION OF KNOWLEDGE

0-Four-revol

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

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

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

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

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

THE EVOLUTION OF EDUCATION

0-School-revol

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

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

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

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

DATA CURATION SKILLS AT THE CORE OF THE NEW LITERACY

0-Hypersphere

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

0-Data-curation

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

PERSONAL INTELLIGENCE

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

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

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

CRITICAL INTELLIGENCE

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

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

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

COLLECTIVE INTELLIGENCE

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

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

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

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

 0-Collaborative-learning

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

Ancient-Hands-Argentina

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

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

Twenty years after Collective Intelligence

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

Reflexive collective intelligence

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

The key: A complete modelling of language

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

The scientific file: The Semantic Sphere

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

The metalanguage: IEML

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

A new frontier: Algorithmic Intelligence

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

Philosophical genealogy of algorithmic intelligence

The three ages of reflexive knowledge

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

The theosophical age and its agent intellect

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

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

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

The scientific age and its transcendental subject

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

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

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

The technological age and its algorithmic intelligence

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

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

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

[1] Professor at the University of Ottawa

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

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

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

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

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

[7] Beyond logic and statistics.

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

[10] More than four hundred reference books.

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

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

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

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

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

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

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

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

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

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

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

[22] Book Lambda, 9

[23] In particular in Book III.

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

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

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

Pierre Lévy:

Pierre Levy-photo 1

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

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

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

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

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

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

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

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

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

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

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

dialect_human_development

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

start-ieml

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

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

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

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

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

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

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

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

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

Originally posted on Blog of Collective Intelligence 2003-2015:

pierre_levy

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

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

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