Foreword

In these tormented times when time itself is swirling, this book is like a breath of fresh air, at the same time as it warms the heart: it is about the knowledge to which the sciences give us access, but not only this, insofar as it is difficult to get rid of a few doses of more or less reliable beliefs which sometimes enter it surreptitiously. For Bernard Ancori’s epistemology, which encompasses but goes beyond a philosophy of science, a set of logical and critical reflections on the nature of knowledge lead us into what Spinoza calls “a certain kind of eternity”, i.e. into timelessness. But it is this very fact that allows us to further question our diverse experiences of the passing of time. And, in particular, a certain acceleration that would characterize our present time, making it lean towards a kind of permanent present. Already at the end of the 19th Century, as Bernard Ancori points out, William James, a pioneer in psychophysics, had shown the specious, almost misleading, nature of our perceptions of the present between past and future. The concept of propensity to communicate introduced by Bernard Ancori constitutes a possible formalization of the notion of a specious present, and extends this notion to the spatial dimension of the network of individual actors whose model he proposes. As a result, this concept constitutes the pivotal point on which the spatial and temporal dimensions of this network are articulated, which justifies the notion of space/time of the latter, as it is highlighted here.

The timeless aspect mentioned above is not an aboveground level, which would take us out of this world. We recognize in it a way of approaching one of the crises that the knowledge accumulated by the human species over the past two centuries has been going through. This is the gap reported by chemist-writer Charles Snow in the 1950s between the “two cultures”, that of the natural sciences and that of the social sciences and the humanities in general.

From this point of view, we can ask ourselves whether the 21st Century will really be a new era or only a continuation of the physical and biological revolutions experienced by the 20th Century. Probably both, because the division of history’s time into hundreds of solar years is after all only a convention that is not without arbitrariness. And everything happens as if Bernard Ancori took up the challenge of closing this gap in his own original way, by building a bridge between these two aspects of Homo sapiens, a living being, the object of biology and a being of language, psychosocial. His multidisciplinary background confronted with the applications of the mathematical theory of information allows him first to identify, at the level of this meeting, a breaking point. Seeing it as at least one of the origins of the growing gap between the two cultures, he has found a way to clear a path and, in a way, to mend them.

In doing so, he contributes to the ongoing realization of John von Neumann’s prediction of the evolution of 20th Century science. Von Neumann, a physicist and co-inventor of the electronic computer, also predicted in the 1950s that this century would be for the sciences the century of complexity, just as the 19th Century had been the century of energy. This prediction seems to be coming true, albeit with some delay, since we have entered the 21st Century. This raises a question about the passage of time and its possible creative role, which constitutes the basis of Bernard Ancori’s reflection and gives the book its title. This ambitious work addresses the diverse nature of our psychological, social, physical and temporal experiences, combined with the different ways in which we learn about things. Only the past is rigorously the object of knowledge, constituted by bases memorized and connected in different ways. The present seems to be perceived as such, felt or sensed, before being forgotten or memorized. As for the future, it is imagined or predicted with varying degrees of success based on projections from the past, while it is shaped in a concrete and largely unconscious way. But from all this results a form of timeless knowledge, that of what philosophers have called “eternal truths”, of which mathematics serves as a model. The use of reason tends to bring scientific activity closer to this ideal, in a more or less approximate way depending on the disciplines. The result is the creation of new concepts in the history of science, which seem to be all the closer to this ideal because they are supported by mathematical formulation and operations. But not all objects of investigation are equally suitable because the times of experience and experimentation, with their difficulties to overcome, cannot be neglected.

Thus, according to von Neumann’s prediction, scientific knowledge has already been enriched in physics by the notion of information, mathematized in the eponymous theory, in relation to that of complexity – as well as of energy through that of entropy. And it is here that there is indeed a breaking point and a possible meeting between its uses in the natural sciences, first physical and then extended to biological physico-chemistry, and its possible but more problematic applications in the social sciences.

Indeed, as in the case of force in the 18th Century and energy in the 19th Century, information has been rigorously defined as a physico-mathematical quantity by borrowing the word from everyday language. But the latter is still a purely qualitative and relatively vague notion, used in the psychosocial context of interpersonal, language and other relationships. And the statistical theory of information produced by Claude Shannon and his successors, as well as the theory of algorithmic complexity of Kolmogorov–Chaitin’s computer programs, makes it undergo a transformation, by which it becomes precise and univocal enough to enter the language of the natural sciences.

But this transformation makes it lose what was thought to be its very essence, namely, the meaning of the information processed, sent and received.

In other words, the definition by theory has moved from the vagueness and polysemy of natural language to the univocal precision of the logico-mathematical form and its techno-scientific uses, first in telecommunications engineering, then in computer science. But this passage, as is often the case, makes us lose in semantic richness what it makes us gain in precision and operational efficiency. And what is lost in this case is precisely the meaning of the information usually transmitted in communications between speakers of a natural language. In exchange, mathematical theory, which allows its quantification and measurement, thus extends its applications to all kinds of non-human, physical and biological entities, known as “information carriers” in the sense of theory and treated as if they were telecommunications channels and computer machines.

It is in this sense that the great successes of molecular biology have benefited from the discovery of molecules carrying information in the linear structure of DNA, RNA and proteins, which have been treated as sets of alphabetical or numerical letters. The genetic code has thus been treated as a communication channel whose physical material is recognized in the chemical mechanisms of protein synthesis. But in all this, as in computer science, the meaning of the information thus quantified is not taken into account. In the mathematical theory, the amount of information is expressed by a number of bits that say nothing about its meaning. This is why we can say that an algorithm or a computer does not understand what it does because the transmission of meaning involves speakers who understand it.

This flaw in the theory is not such when dealing with message communication systems between speakers who transmit and receive, and are assumed to understand their meaning, without the need to formalize it in the theory. Similarly, algorithmic complexity does not suffer from the seemingly paradoxical fact that its definition implies maximum complexity for a random sequence of 0 and 1, as if it were meaningless.

This is why the transmission of meaning in communication channels and computer programs is carried out by specific additional operations: those of coding through several levels of programming languages up to the “machine language” reduced to sequences of 0 and 1, at the input of artificial machines, designed and manufactured for this purpose, followed by decoding at the output, for the use of human speakers.

Hence the search, by analogy, for such coding systems in self-organized natural machines, particularly those constituted by organisms. The discovery of what is called the “genetic code”, the same in all organisms – which is in fact only a projection of the linear structures of DNA on those of proteins and thus carries out a transmission of information in the strict sense of the theory – is probably the most spectacular success of this research, although it is not strictly speaking the coding of a computer program, in line with what was believed for a long time. Indeed, the meaning of genetic information here is metaphorically reduced to the effects observed at the output of the communication pathway of a particular protein synthesis and its effects on the structure and functioning of the cells where it takes place; but we now know that these effects, because of the three-dimensional structure of proteins, depend only partially on their linear structure, the only one coded by that of DNA.

Bernard Ancori opposes, or rather...