Our aim in the present paper is to approach the nature of life from the perspective of autonomy, showing that this perspective can be helpful for overcoming the traditional Cartesian gap between the physical and cognitive domains. We first argue that, although the phenomenon of life manifests itself as highly complex and multidimensional, requiring various levels of description, individual organisms constitute the core of this multifarious phenomenology. Thereafter, our discussion focuses on the nature of the organization of individual living entities, (...) proposing autonomy as the main concept to grasp it. In the second part of the article we show how autonomy is also fundamental to explaining major evolutionary transitions, in an attempt to rethink evolution from the point of view of the organizational structure of the entities/organisms involved. This gives further support to the idea of autonomy not only as a key to understanding life in general but also the complex expressions of it that we observe on our planet. Finally, we suggest a possible general principle that underlies those evolutionary transitions, which allow for the open-ended redefinition of autonomous systems: namely, the relative dynamic decoupling that must be articulated among distinct parts, modules or modes of operation in these systems. (shrink)

The notion of computation has changed the world more than any previous expressions of knowledge. However, as know-how in its particular algorithmic embodiment, computation is closed to meaning. Therefore, computer-based data processing can only mimic life’s creative aspects, without being creative itself. AI’s current record of accomplishments shows that it automates tasks associated with intelligence, without being intelligent itself. Mistaking the abstract for the concrete has led to the religion of “everything is an output of computation”—even the humankind that conceived (...) the computer. The hypostatized role of computers explains the increased dependence on them. The convergence machine called deep learning is only the most recent form through which the deterministic theology of the machine claims more than what it actually is: extremely effective data processing. A proper understanding of complexity, as well as the need to distinguish between the reactive nature of the artificial and the anticipatory nature of the living are suggested as practical responses to the challenges posed by machine theology. (shrink)

A systematic effort is here made to express some of the general results of quantum mechanics in a conceptual form closer to ordinary language than is the case with most modern physics. Many of the implications of the theory appear much more clearly thereby, in particular the fact that the laws of quantum mechanics are only statistical propositions about classes, not referring to individual objects. Conversely, the microscopic structure of an object cannot be precisely defined in quantum mechanical terms. To (...) say that an object has a definite microscopic structure is an operationally meaningless statement; it is on the same level as saying in relativity that a selected coordinate system is “at rest.” This raises the need for a “theory of matching”: What is the best statement about the structure of an object, given that the tools of description are only statistical? A technique is known for this: the theory of inductive probabilities. There is no longer one true description but a manifold of statistical descriptions, some of which have a greater likelihood of being satisfactory than others. The main concrete application of this type of conceptual reasoning lies in its power to clean out the speculative underbrush which so far has prevented the transition from theoretical physics to theoretical biology. (shrink)