The Open Network of Collective Intelligence
The intelligence we are interested in emerges while information is being exchanged amongst a group of individuals.
A large number of individuals gathering in persons, at the same time and in the same place, rarely leads to collective intelligence, because the information exchange fluidity is met with obstacles that are often insurmountable.
Thanks to modern communication systems, the obstacle to exchange fluidity seems to be overcome. However, if message exchange between two persons seems greatly facilitated, these exchanges quickly become difficult to manage as soon as the number of exchanged messages, as well as the number of persons involved, grows.
Today, one realizes that potential access to a large volume of information, or to a large number of individuals, is not sufficient to access to more intelligence.
Access to large volumes of information requires interfaces able to organize, structure and hierarchically set, into elementary pieces of information, those that are too complex, too large, or just too numerous, and that, as a result, cannot be assimilated by one person in a given time.
Descending communication from mass media
In order to communicate to the most important number of individuals, the information society, has made every effort to produce messages that are simplified, normalized and smooth, and that are destined to satisfy a population of individuals but no individual in particular.
Mass information is, most often, delivered as “take it or leave it”. This is the only alternative left to the addressee.
Mass communication, or descending communication, is the media application of the communication system established by Claude Shannon with the mathematical theory of information. We owe to Warren Weaver the fundamental diagram of communication systems shown below.
Communication, as it is described in the theory, is not concerned with the semantic aspect of the message being sent. Information is described, in the message, as an increasing function of the uncertainty reduction that it brings; this makes this theory essentially a static one.
From information flow to intelligence flow
In order to set networks able to support flow of intelligent pieces of information, it is necessary to redefine the theory of information.
The theory, which in reality is only concerned with signals, does not take into account the meaning of the information being moved. This is the reason why we propose the design of a less restrictive theory, yet more ambitious: a general theory of intelligence flows.
By integrating in this new theory the sciences of signs, that is semiology, one can see that a fundamental notion, retroaction, was not part of the previous theory.
Roland Barthes gives the following definition of semiology: “a science that studies the life of signs within the social life”.
The semiotic triade is a schematic representation of the sign interpretation proposed by the American logician Charles S. Peirce (1839-1914). It makes it possible to understand the place of the interpreter in relation to the object, and the sign that represents it.
The necessity to develop new languages, particularly for robotic applications, made it possible to improve our understanding of the mechanisms involved. The semiotic cycle demonstrates that a representation cannot exist without the sharing of a common space between the one who issues and the one who interprets. (From the kind contribution of Luc Steels, VUB AI Lab, Brussels and Sony Computer Science Lab, Paris)
Continued…