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Il y a eu ce moment si particulier où j’ai pris ma plume et où je t’ai écrit une lettre. Nous avions 18 ans. Tu avais cédé déjà à cette douleur indicible qui t’a aujourd’hui emporté. Cette lettre, tu m’en as longtemps parlé, car elle a scellé notre amitié. Je n’avais partagé avec toi que le rire. Quand j’ai appris que ta légèreté avait son pendant de noirceur, j’ai découvert à quel point notre complicité m’était précieuse.
Pour moi, une première brêche dans l’insouciance de mes toutes jeunes années. Un premier moment de vérité. Alors, entre nos âmes, un fil ténu a commencé de se tisser.
Nous avions 18 ans quand notre amitié à débuté. Il s’est écoulé 18 ans depuis.
La douleur t’es restée. Un nouveau cycle s’est déroulé. Notre amitié n’a jamais décru.
Au cours de ces 18 années nous avons connu d’innombrables moments de vérité et désormais un réseau complexe s’est tissé entre nos âmes.
Dans l’éternité où tu vogues aujourd’hui, nos rires adolescents résonnent de mille échos; ils sont devenus le témoin d’une confiance absolue.
Tu resteras pour toujours mon amie de 18 ans.

Je viens de terminer la lecture du dernier livre de Chris Anderson, Free. Il s’agit d’un livre sur la grande tendance des produits gratuits sur internet – et ailleurs. Mr Anderson semble vouloir inventer un nouveau concept, le “Free” ou “Free” tout court. Ce concept est intraduisible en français car il évoque non seulement la gratuité mais la liberté aussi. Cependant, le livre s’adresse clairement aux entrepreneurs, et il sera surtout question d’argent. La “gratuité” apparaît comme le résultat d’une prise de recul: un produit ou un service faisant toujours partie d’un écosystème complexe, on choisit de le rendre gratuit parce que ça attire du monde, et on se rattrape sur les produits annexes. Ou alors on rend un service gratuit pour les curieux, qui n’en feront qu’un usage léger, et on le fait payer aux utilisateurs plus exigeants. Ce genre de choses.

Mais “Free”, le concept, ça va quand même un peu plus loin. D’abord, d’après Anderson, il faut se pencher sur les raisons qui le rendent tellement attrayant sur le plan marketing. Et puis de là, comment se positionner par rapport à lui ce qui, nous dit l’auteur, est une question qui se posera à chacun de nous: on peut bien faire payer des choses aux gens, mais tout en sachant qu’il y aura presque toujours une alternative gratuite. Bon. Ensuite, il y a la Free Economy: Wikipedia, Linux, tout ça.

“Free Economy” est une idée qui fait peur aux Etats-Unis; si ce n’est pas du communisme, et si, comme on vient de le voir, “Free” n’est qu’un arme de plus dans l’arsenal de l’entrepreneur, que vient-il donc remplacer? À cette question Anderson donne 2 réponses: 1) du temps; 2) de la réputation. Du temps lorsqu’on capte votre temps de cerveau ou celui de ceux à qui vous allez parler des services que vous utilisez gratuitement; de la réputation, lorsque vous offrez gratuitement votre temps pour augmenter votre “capital social”. La boucle est bouclée: “Free” = “Money” (quoique indirectement)! et le capitalisme est sauf.

Personnellement, je trouve ça un peu décevant. Il y a aussi dans le livre une ou deux envolées sur le thème de l’abondance. “Free” serait le signe que nous nous préparons à une ère d’abondance. Plus ils se dématérialisent, plus produits et services devient abondants, et donc bon marché et, à la limite, gratuits. Ce qui est vrai aujourd’hui des services le sera bientôt pour les biens matériels.

C’est déjà plus enthousiasmant. Mais pourquoi une économie basée sur l’argent serait-elle remplacée par une économie basée sur le temps et la réputation dans une société de l’abondance? J’ai presque l’impression que ce serait pire. Au moins l’argent, malgré la mesquinerie ou la cupidité qu’il nous inspire, a une odeur de soufre qui le rend excitant, en quelque sorte. Mais le “temps (de cerveau disponible)” et la “réputation”, peut-on vraiment imaginer concepts plus vulgaires?

L’esquisse que fait Anderson d’une société où les coûts de production seraient devenus marginaux et où nous puissions remplacer le “free market” par le “Free” tout court est alléchante, mais tel qu’il en délimite les contours, il s’agit rien moins que d’un leurre. La liberté, la vraie, c’est une percée dans le corps des conventions, de cela même qui se comptabilise, l’affirmation de l’audace qui caractérise notre espèce dans ces meilleurs moments. Il est dommage qu’un mot comme Free fasse l’objet d’un aussi mauvais traitement.

Statistics are a very efficient tool in business. They provide control panels that are extremely useful to decision-makers and make it possible to optimize processes in order to maximize profit, or any other organization’s main – or higher, purpose. Statistics in business rely on metrics – quantified data. This data is obtained through a body of techniques known as “operationalization”, or how to convert the organization’s daily life’s deemed interesting deeds into manipulable numbers. It then transforms that data to produce figures that will eventually affect the organizations processes in a (hopefully) positive way.
This process is really all about numbers; however, statistics, in its noblest acceptance as a mathematical discipline, is this formidable body of techniques which create relative certainty out of near absolute uncertainty. It has its roots in probability, which itself arose from our fascination for gambling. But in common parlance, what we mean with “statistics” is very often something much simpler, which tells more about our fascination with numbers then our sheer passion for mathematical abstraction. “Show me the statistics” could as well be put as “show me the figures”: we want to see the charts, what’s on top, what’s in and what’s not.
Wired magazine’s latest cover story, “living by numbers”, is about the many means by which to track various data about ourselves and utilize that information to improve our workouts, nutrition and health. The statistics are basically inexistent and the basic premise here seems to be that personal improvement is all about getting more performant according to some body of standards put together by leading insurance companies. A very depressing perspective indeed.
Business Intelligence is much more subtle nowadays and some of its methods rely on hardcore statistical maths. In some cases, some almost “magical” technology is at hand, which really augments our cognitive abilities and let us uncover patterns that we weren’t aware of.

Now, as individual human beings, there is probably no good reason why we wouldn’t be subjected to hidden patterns, just as organizations. But there’s an additional difficulty: we are complex entities, so what areas of our lives should we focus on? Wired’s article suggest that accumulating performance indicators and relying on algorithms to cut bad habits such as smoking or overeating are a good starting point. Businesses, up to a certain level, can be compared to machines, because we like to think of them as organizations serving a specific purpose. But we human beings cannot be likened to eating/exercising/self indulging mecanisms. There are other dimensions of an individual than its sheer capacity to maintain itself in an arguably “optimal condition”, which are presumably more important for us to know about; they are probably the driving forces that motivate us to entertain any desire on these particular subjects in the first place as well (if at all). We take any business’ purpose for granted, but a human being is a whole different class of affairs. Our motivations in life are ever-changing; we set ourselves different objectives at different times of the day, goals of all sorts ranging from immediate, practically unnoticeable tasks, to aspirations of an almost spiritual nature. Our projects then get mixed up with our bad habits, and long matured destinies vanish instantly at the advent of an unmissable opportunity. Sometimes, a certain sense of absurdity emerges from our shattered existences. Yet, in many cases, we are probably just missing perspective.

To capture this information, we need a symbolic representation system that is capable to reproduce the complexity of human existence. While our various electronic devices deliver a constant flow of digital information – mail, browser history, social networks, …, this still has to be transformed into a meaningful material, one that is sufficiently rich to provide us with the many facets that need be exposed by any sufficiently powerful model of human affairs. We will then be able to look at our life and the many ways it connects with various events, the lives of our entourage, our environment, even the weather… all at one glance. We will have a life in symbols.

My Safari Bookshelf currently has two books on Collective Intelligence. They are not theoretical books, but practical programming guides. One is titled “Collective Intelligence in Action”, the other “Programming for Collective Intelligence”. Sofar I had nurtured an almost philosophical interest for CI, but these books promise much more: I can get in front of my computer and start taking advantage of it. It is becoming a resource, maybe even a new kind of utility.

Personal Intelligence is another beast. It is this most precious – and unevenly distributed, capacity of knowing oneself and others in order to make sensible decisions in life. More so even than CI, PI has deep echoes in the philosophical tradition (everyone remembers Socrates’ motto). O’Reilly does have a few books that relate to PI, but they are not programming titles.

Thus, there seems to be no way for me to leverage Personal Intelligence insights and concepts using a computer. The idea might sound idiotic, but think about it: people (you?) generate tons of highly personal information on computers these days. Imagine what Freud could do if he could lay a hand on the accumulated data you are generating on all your electronic devices. Having you lay down on a sofa would probably feel redundant.

Organizations have exercised introspection for a long time, though. In the corporate world, this is known as Business Intelligence. Techniques in these disciplines have been inspired, or directly applied, from the fields of A.I. and bioinformatics. It is a vibrant field where techniques stemming from the 70s and 80s and that have been deemed useless ever since, have finally found their target domain; what’s more, they very effectively benefit the bottom line. It is basically this technology that is being rebranded and explained in simple words in my Safari books on Collective Intelligence.

Individuals could probably benefit from these techniques, too. Through mining their own data, you and I could find out about the cycles in our lives, uncover unforeseen influences from people and events. By knowing ourselves better we could certainly gain more control over our health, personal objectives and behavioral patterns.

The Personal Cloud is a popular concept these days. Like everything “cloud” it has a rather fuzzy meaning, or at least one that is very changing, depending on the context.

• it might be an online datastore, where people are able to store arbitrary data: images, documents, videos…
• it may refer to the idea of some private instance of a social network (ala buddypress), collaboration tools (zimbra), etc… where the software runs in the cloud but on a privately owned server
• finally, the most widely accepted definition is probably that of the aggregated personal data that a user has gathered online with all the different “free” services she uses: facebook, gmail, flickr…

The fundamental difference between all these definitions is probably the degree of isolation with other people’s data. The so-called social web is somehow blurring the notion of a digital identity, as people tend to define themselves more and more through the different social networks they are being part of. I.e. you are not anymore this mister so-and-so with a given profession and, possibly, a certain number of political/philosophical/artistic views; you are instead a member of a certain number of communities, and you are in constant interaction with their members on multiple subjects.

It is those interactions that, considered en masse, are creating the value of the social web. They are the driving force behind the enthusiasm for cloud computing, and for a good reason: intelligence gathered from the interactions of millions and millions of users is an incredibly powerful marketing tool.

In my opinion, the Personal Cloud is this elusive notion that, in addition to your being part of a certain number of online communities, you are your own personal individual, too; one particular person with their own tastes, feelings and aspirations that need not necessarily belong to a marketable and outbound community.

I am convinced that there is a need for everyone of us to reflect on the complex interactions we entertain with cyberspace. It is as we are learning to breathe and we need to understand how to inhale.

The Personal Cloud is yet to be invented.

I am a software engineer. My interest for IEML is philosophical and maybe even a bit esoteric, but I’m also interested in its power from a purely engineering standpoint. Here’s my take on it.

To begin with, IEML is a language in its own sake; one of its own kind, too, however, since it has a universal scope. It stems from a long philosophical ambition to conceive of a language that is the language of the mind. Foremost, it is also a script through which we can not only describe ideas, things or concepts, but, as we are doing so, it gives us the power to understand and to discover more about the reality we are trying to model. This process, in which we are constantly re-evaluating our own assumptions about our personal ideas, is what makes IEML a bridging technology for humans to collaborate accross disciplinary boundaries and language barriers, almost without us ever noticing it.

As a software engineering tool, IEML is the technology for IT experts to create systems that are not only technically interoperable, but whose functionality will be expressed in the most generic way and which will allow for a whole new level of reusability. It has the potential to create a breakthrough in design pattern engineering aswell as in SOA software design. Imagine a world where Web Service descriptors not only expose method signatures but also the true meaning and scope of these methods in a way that is understandable by machines. IEML is the empowering technology for software engineers to exponentially grow our ability as a species to think collectively and, ultimately, to better understand the world we are living in.

The enabling IEML technology for the generation of semantic tag clouds is the USL. An USL is a collection of sets of IEML *tags at each of the 6 different layers. Each *tag implies a set of semantic relationships with other *tags; at every layer a USL thus expresses a combination of the semantics of many *tags. At the most basic level, therefore, USLs are already small tag clouds really.

Let us try to understand what meaning does the addition of multiple *tags at each layer bear for us.

What do multiple level 0 elements mean?

This is a trivial case, because level 0 elements, by definition, have no semantic relationships to other elements. In addition, level 0 elements in the context of a USL represent “networks of Collective Intelligence”, and thus, by tagging a resource with multiple such elements, a user exactly means that the resource participates in multiple such networks.

What do multiple level 1 elements mean?

Things get a little bit more interesting here. Every level 1 element can be either nounish or verbish. A verbish expression is going to be either en energy or an act. A nounish expression can be a mutation or an entity. There is also a obvious symmetry between acts (O:M:.) and mutations (M:O:.). Finally, the categories O:M:., M:O:., M:M:. and O:O:. all have etymologic relationships with both their first and second role players. Let’s see how we could visualize these relationships for just one IEML * tag of layer 1:

Now consider another semantic relationships graph:

Imagine that some resource was tagged with these two *tags (this one might be a good candidate), i.e. want and perceive; what can we already conclude by examining their semantic graphs? These are very short graphs, of course, but counting the number of arrows between concepts, the Verbs one is certainly the most common destination. It means that, from just two *tags, we can already conclude that this resource will add a mostly verbish coloration to the tag cloud to which it would participate.

A tag cloud is a list of words in different sizes and colors, with or without a sense of depth (3D), meant to represent the statistical importance of keywords mentioned in a particular document base (a blog, a website, twitter,…). It serves as an indicator of the relative importance of the use of certain ideas in the document base at hand. It is a bottom-up, very fuzzy method for the synthesis of knowledge from an arbitrarily big aggregate of (text) data. Because it rests entirely on statistics, very often there is absolutely no relationships between the keywords of a tag cloud. Worse even, if they existed (by pure chance), there is absolutely no way of finding out about the meaning of those relationships.

On the other hand, ontologies propose a top-down approach to the same problem. They require first that some expert, or committee of experts, agree on a common description of a particular domain. Given an ontology, it is possible to map a document base to the concepts it describes: every concept would act as a filter to retrieve documents that match it. The clear advantage of this method is that it is possible to use the semantics expressed in the ontology to search for a specific combination of concepts. A tag cloud rendition of an ontology would, however, carry two major drawbacks with it:

1. you need an ontology per domain you wish to search for, hence ontology-based navigation is always going to be extremely specific;
2. no semantic value added is generated (note that there is indeed a minor semantic value added in the statistical tag cloud, albeit of a purely quantitative nature)

An IEML tag cloud would combine the advantages of both approaches, while overcoming their limitations: it would generate significant semantic value added through the discovery of the underlying conceptual network of a given document base; and it would allow for navigating in semantics. As for the statistical tag cloud, the conceptual network thus discovered would remain quantitative in nature to a certain degree. Indeed the salience of the semantic relationships is going to vary depending on the precise contents of the document base, as is going to be necessary to reflect in the rendition of the resulting tag cloud.

Another pending issue as far as rendition of an IEML-based tag cloud goes is deciding upon the relative positioning of concepts on the user’s display, in 2D and/or 3D.

As a computer programmer, I was always fascinated by the expressive power of languages, not only programming languages, but languages in general. My daily practice lets me tackle the incredible processing power of computers, but the raw material here, the meaning behind the calculations, is generally about very mundane things. I then resort to natural languages to relate to humans, and to literature to delight in art. Then I am long intrigued by the weird symbols of other languages, “magical languages” like astrology, the Ji-jing or the Tarot. Perhaps they shouldn’t really be called languages, maybe rather “symbolic systems”, but because of the combinatorics they rely on as a tool for divination for example, they tend to be used as languages. I was often struck by the complex network of ideas that arose from the contemplation of an astrological theme or a set of Tarot cards, when I got the adequate grammar to interpret them. Most importantly, such systems, when put to good use, have the power to “make you smarter”. That is why divination is really about revelation, rather than just a way to know about one’s future. A good Tarot or Ji-jing practitioner lets you explore the hidden parts of your mind, and lets you make connections with dimensions of your existence that you weren’t aware of, or were too shy or too anxious to take into account. This process of interpretation and re-appropriation of meaning can be extremelly helpful in daily life and is really to be considered as a way to resurect a very ancient and long tradition of wisdom.

IEML also stems from a thorough understanding of the history of thought. It shares many traits with traditional symbolic systems, in particular with the Ji-jing. It is however very original and unique in many ways:

• it is the first of its kind to incorporate the western tradition of thought: some of its basic building blocks (the Sign-Beign-Thing triad) relate to a mostly western mindset and its basic interpretation/dictionary, as authored by its inventor, maps clearly to a host of notions and concepts that stem from 20th century human sciences;
• it does, too, relate to other traditions from around the world and puts them all “on an equal footing”;
• it doesn’t imply a unique, orthodox interpretation;
• it is meant to be thoroughly computable;

These features make IEML a particularly interesting tool to assist in the understanding of today’s challenges in our global world. That’s quite fortunate you should say, because that’s what it’s been meant for. But its original (and true) purpose is to serve scientific needs. What I’m proposing here is to use it as a tool to interpret one’s personal, daily concerns, just as astrology or the Tarot. It’s a game of course and, just as the other systems, will rely on chance to create the symbols meant to be interpreted. But it could also be a very good learning tool for all of us interested in this fascinating technology.