Analogicity in computer science is understood in two, not mutually exclusive ways: 1) with regard to the continuity feature (of data or computations), 2) with regard to the analogousness feature (i.e. similarity between certain natural processes and computations). Continuous computations are the subject of three methodological questions considered in the paper: 1a) to what extent do their theoretical models go beyond the model of the universal Turing machine (defining digital computations), 1b) is their computational power greater than that of the (...) universal Turing machine, 1c) under what conditions are continuous computations realizable in practice? The analogue-analogical computations lead to two other issues: 2a) in what sense and to what extent their accuracy depends on the adequacy of certain theories of empirical sciences, 2b) are there analogue-analogical computations in nature that are also continuous? The above issues are an important element of the philosophical discussion on the limitations of contemporary computer science. (shrink)

According to the methodology of cognitive science we consider a hypothesis (justified partially by cognitive applications of computer science), that the mind functions similarly to a computer. Philosophical consequences of this thesis are as follows: (1) there exists a mental code (similar to the code of computer program); (2) this code can be represented as one unique number; (3) this number can be computable or non-computable.

Alan M. Turing’s last published work and some posthumously published manuscripts were dedicated to the development of his theory of organic pattern formation. In “The Chemical Basis of Morphogenesis” (1952), he provided an elaborated mathematical formulation of the theory of the origins of biological form that had been first proposed by Sir D’Arcy Wendworth Thompson in On Growth and Form (1917/1942). While arguably his most mathematically detailed and his systematically most ambitious effort, Turing’s morphogenetical writings also form the most thematically (...) self-contained and least philosophically explored part of his work. We dedicate our inquiry to the reasons and the implications of Turing’s choice of biological topic and viewpoint. We will probe for possible factors in Turing’s choice that go beyond availability and acquaintance with On Growth and Form. On these grounds, we will explore how and to what extent his theory of morphogenesis actually ties in with his concept of mechanistic computation. Notably, Thompson’s pioneering work in biological ‘structuralism’ was organicist in outlook and explicitly critical of the Darwinian approaches that were popular with Turing’s cyberneticist contemporaries—and partly used by Turing himself in his proto-connectionist models of learning. Resolving this apparent dichotomy, we demonstrate how Turing’s quest for mechanistic explanations of how organisation emerges in nature leaves room for a non-mechanist view of nature. (shrink)

Two different types of analog computations are discussed in the paper: 1) analog-continuous computations (performed physically upon continuous signals), 2) analog-analogical computations (performed naturally by means of so called natural analogons of mathematical operations). They are analyzed with regard to such questions like: a) are continuous computations physically implementable? b) what is the actual computational power of different analog techniques? c) can natural (empirical) computations be such reliable as digital? d) is it possible to develop universal analog computers (assuming that (...) they should be functionally similar to universal Turing machine)? Presented analyses are rather methodological than formal. (shrink)

Virtual objects, such as online shops, the elements that go to make up virtual life in computer games, virtual maps, e-books, avatars, cryptocurrencies, chatbots, holograms, etc., are a phenomenon we now encounter at every turn: they have become a part of our life and our world. Philosophers—and ontologists in particular—have sought to answer the question of what, exactly, they are. They fall into two camps: some, pointing to the chimerical character of virtuality, hold that virtual objects are like dreams, illusions (...) and fictions, while others, citing the real impact of virtuality on our world, take them to be real—an actual part of the real world, just like other real objects. In this article, we defend the thesis that both sides are wrong. Using Roman Ingarden’s phenomenological ontology, we advocate a position according to which a virtual object is a computationally grounded intentional object that has its existential foundation in computational processes, which are compliant with a certain model of computation. We point out that virtuality is framed by some kind of ideal mathematical objects: i.e., mathematical models of computation, which in turn fall, each of them, under their respective ideas. We also refer to the idea of natural computation, which in conjunction with the ontological analysis carried out leads to the thesis that an object can be more or less virtual. (shrink)

In this paper, I propose a populational schema of modeling that consists of: a linear AFSV schema, and a higher-level schema employing the genetic algorithm. The basic ideas of the proposed solution are as follows: whole populations of models are considered at subsequent stages of the modeling process, successive populations are subjected to the activity of genetic operators and undergo selection procedures, the basis for selection is the evaluation function of the genetic algorithm. The schema can be applied to automate (...) the modeling of the mind/brain by means of artificial neural networks: the structure of each network is modified by genetic operators, modified networks undergo a learning cycle, and successive populations of networks are verified during the selection procedure. The whole process can be automated only partially, because it is the researcher who defines the evaluation function of the genetic algorithm. (shrink)

Niniejszy artykuł dotyczy modelowania różnych zjawisk przy użyciu pojęć i narzędzi informatycznych, związanych głównie z badaniami nad sztuczną inteligencją (SI). Po przedstawieniu idei sformalizowanego modelu informatycznego (MI) omawiamy ogólnie interaktywną procedurę modelowania (która składa się z czterech, powtarzanych cyklicznie, etapów: abstrakcji, formalizacji, symplifikacji i weryfikacji), a następnie charakteryzujemy ją w kontekście szczególnym, tj informatycznym. Omawiając różne typy MI, odwołujemy się przede wszystkim do badań nad AI; np. rozróżniamy między modelami regułowymi (implementowanymi często w postaci systemów eksperckich), sieciowymi (realizowanymi często w (...) postaci sztucznych sieci neuronowych) oraz ewolucyjnymi (nawiązującymi, na przykład, do teorii algorytmów genetycznych). Co jest jednak najważniejsze, przedstawiamy pogląd, zgodnie z którym różne, przynależne do badań nad AI, techniki automatycznego uczenia się mogą, a w gruncie rzeczy powinny, być stosowane do automatyzacji poszczególnych etapów procedury modelowania. (shrink)