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Significance of Models of Computation, from Turing Model to Natural Computation

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The increased interactivity and connectivity of computational devices along with the spreading of computational tools and computational thinking across the fields, has changed our understanding of the nature of computing. In the course of this development computing models have been extended from the initial abstract symbol manipulating mechanisms of stand-alone, discrete sequential machines, to the models of natural computing in the physical world, generally concurrent asynchronous processes capable of modelling living systems, their informational structures and dynamics on both symbolic and sub-symbolic information processing levels. Present account of models of computation highlights several topics of importance for the development of new understanding of computing and its role: natural computation and the relationship between the model and physical implementation, interactivity as fundamental for computational modelling of concurrent information processing systems such as living organisms and their networks, and the new developments in logic needed to support this generalized framework. Computing understood as information processing is closely related to natural sciences; it helps us recognize connections between sciences, and provides a unified approach for modeling and simulating of both living and non-living systems. KeywordsPhilosophy of computer science–Philosophy of computing–Theory of computation–Hypercomputing–Philosophy of information–Models of computation
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... Turing's model is a subset of the Actor Model. According to this viewpoint, the Internet conducts unconventional computations and appears to be governed by super-recursive algorithms [7]. 5 The so-called "domestication of physical entities" will be described in the following Section 4. 6 See, for example, the rich illustration furnished by Müller and Hoffmann [12]. ...
... According to this viewpoint, the Internet conducts unconventional computations and appears to be governed by super-recursive algorithms [7]. 5 The so-called "domestication of physical entities" will be described in the following Section 4. 6 See, for example, the rich illustration furnished by Müller and Hoffmann [12]. 7 The reader interested in deepening this new kind of research related to reservoir computing can refer to my recent article [16]. 8 Further details concerning this difference can be usefully found in Kari and Rozenberg [20]. ...
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Article
Eco-cognitive computationalism explores computing in context, adhering to some of the key ideas presented by modern cognitive science perspectives on embodied, situated, and distributed cognition. First of all, when physical computation is seen from the perspective of the ecology of cognition it is possible to clearly understand the role Turing assigned to the process of “education” of the machine, paralleling it to the education of human brains, in the invention of the Logical Universal Machine. It is this Turing’s emphasis on education that furnishes the justification of the conceptualization of machines as “domesticated ignorant entities”, that is proposed in this article. I will show that conceptualizing machines as dynamically active in distributed physical entities of various kinds suitably transformed so that data can be encoded and decoded to obtain appropriate results sheds further light on my eco-cognitive perspective. Furthermore, it is within this intellectual framework that I will usefully analyze the recent attention in computer science devoted to the importance of the simplification of cognitive and motor tasks caused in organic entities thanks to morphological features: ignorant bodies can be computationally domesticated to make an intertwined computation simpler, relying on the “simplexity” of animal embodied cognition, which represents one of the main qualities of organic agents. Finally, eco-cognitive computationalism allows us to clearly acknowledge that the concept of computation evolves over time as a result of historical and contextual factors, and we can construct an epistemological view that depicts the “emergence” of new types of computations that exploit new substrates. This new viewpoint demonstrates how the computational domestication of ignorant entities might result in the emergence of novel unconventional cognitive embodiments.
... Information is represented by mathematical formulas and computation realizes a physical process. [14] The abstract and syntactic formality allows eliminating the "ghost in the machine". Taking a representational view of the mind, all brain functions are pooled under information processing in the broad sense. ...
... Rather than disassembling the brain into all its structures in the style of the phrenologists, the general systems theory endeavors to subsume functions like homeostasis and control, or like computing, learning and memory, into a manageable number of categories and identifies a level of abstraction appropriate for tracing them. Computer sim- 14 We recall that the entropy S is not itself a statistical notion. It was introduced to account for the direction of heat transfer between two contiguous bodies at different temperatures. ...
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Conference Paper
Recently, computationally expensive iterative image reconstruction techniques and sophisticated processing methods are deployed to map the structure and function of the human brain non-invasively. Multiscale representations are available for the visual representation of the local neural activity. Perhaps, by doing so the difference between the imaged brain functioning and that of a computer is expunged. In fact, understanding brain activity is not so much a matter of evolutionary ecology, but rather of clarifying in which sense it fits natural laws.
... In other words, CT is widely associated with programming (Voogt et al., 2015), but programming is not the only approach studied in the literature (Tikva & Tambouris, 2021). As a matter of fact, CT has been widely used in different fields from mathematics to programming, from computer science to biology (Benakli et al., 2017;Bers et al., 2014;Dodig-Crnkovic, 2011;Evia et al., 2015;Grover et al., 2015;Mladenović et al., 2018;Rubinstein & Chor, 2014). There are studies in the CT literature which use development of operational thinking both in computer programming, and in such disciplines as mathemat-ics and biology to train students' logical concepts, CT, problem solving skills, and deductive ability . ...
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Article
Computational thinking (CT) has started to attract attention as an important research topic in recent years. It is important to describe the CT field in detail and to determine the research interests and trends of studies in this field. In this most comprehensive and first topic modeling based study in the field of CT, it was aimed to determine the current situation and research interests and trends in the articles on CT from past to present. For this aim, articles containing the term “computational thinking” in the title, keywords and abstract were retrieved by a search on January 18, 2022 from Scopus database. As a result of the search, a total of 1083 articles related to CT published in the Scopus database as of the end of 2021 were obtained. The bibliometric analysis findings of the study showed that there has been a significant increase in the number of publications in this field, especially since 2015. Studies are mostly of United States origin. Although the studies are interdisciplinary, they have been published mainly in journals in the field of educational technologies. The topic modeling analysis showed that the articles in this field were grouped under 13 topics. The first three of these topics, in order of volume, are “Game based learning”, “Programming skills” and “Early child coding”, respectively. When the acceleration of the topics is examined, the first three, whose weight increased over time compared to other topics, came to the fore as “Programming skills”, “Early child coding” and “robotic programming”, respectively. As a result, it is expected that this study will guide future studies in terms of determining research interests and trends in the field of CT.
... Other kinds of pancomputationalism, appropriately linked to paninformationalism, contend that the entire universe can be seen as computational and also arrive to maintain that information and/or computation possess a kind of priority with respect to physical materiality. Gordana Dodig-Crnkovic (2011 proposes a richer info-computational and more circumscribed and integrated view as a synthesis of pancomputationalism-naturalist computationalism-with informational structural realism, and defends it by noting the fundamental role of computing in nature (natural computing). ...
Chapter
In the first two chapters of this book we have stressed that eco-cognitive computationalism sees computation in context, following some of the main tenets advanced by the recent cognitive science views on embodied, situated, and distributed cognition. We have also described the new attention in computer science devoted to the relevance in computation of the morphological features. It is by further deepening and analyzing the perspective opened by these novel fascinating approach that we see ignorant bodies as domesticated to become useful “mimetic bodies” from a computational point of view.
Chapter
Taking advantage of the logical and cognitive studies illustrated in the previous chapters, which emphasize the crucial role played in abductive cognition by the so-called “optimization of eco-cognitive openness and situadedness”, “knowledge in motion”, and the concept of “epistemic irresponsibility”, the present chapter will introduce the concept of overcomputationalism, to help interpret the related concepts of pancognitivism, paninformationalism, and pancomputationalism and their impact on discoverability. In the second part of the chapter I will submit to the attention of the reader a question that in my opinion synthesizes many of the problems described in this book: will the future of eco-cognitive settings computationally-tailored or humanly-tailored? The challenges against human abduction and epistemic rigor on the part of what I call computational invasive “subcultures” and unwelcome effects of selective ignorance are illustrated.
Book
This book mainly focuses on the widely distributed nature of computational tools, models, and methods, ultimately related to the current importance of computational machines as mediators of cognition. An entirely new eco-cognitive approach to computation is offered, to underline the question of the overwhelming cognitive domestication of ignorant entities, which is persistently at work in our current societies. Eco-cognitive computationalism does not aim at furnishing an ultimate and static definition of the concepts of information, cognition, and computation, instead, it intends, by respecting their historical and dynamical character, to propose an intellectual framework that depicts how we can understand their forms of “emergence” and the modification of their meanings, also dealing with impressive unconventional non-digital cases. The new proposed perspective also leads to a clear description of the divergence between weak and strong levels of creative “abductive” hypothetical cognition: weak accomplishments are related to “locked abductive strategies”, typical of computational machines, and deep creativity is instead related to “unlocked abductive strategies”, which characterize human cognizers, who benefit from the so-called “eco-cognitive openness”.
Chapter
We have already delineated some basic aspects of the so-called eco-cognitive computationalism, for example the fact that computation is always seen in context, exploiting the ideas developed in those projects that have originated the recent views on embodied, situated, and distributed cognition. As illustrated in the previous chapter Turing’s original intellectual perspective has already clearly depicted the evolutionary emergence in humans of information, meaning, and of the first rudimentary forms of cognition, as the result of a complex interplay and simultaneous coevolution, in time, of the states of brain/mind, body, and external environment.
Chapter
The origin of social systems lies in the emergence of the structure of the human subject who can incorporate an internal image of the external world. This structure, established on the basis of the dynamic referral of the conscious subject (self) to its symbolic image, acquires the potential to rationally describe the external world through the semantic structure of language. It has been modeled in reflexive psychology using the algebra of simple relations which are, in fact, dynamic oppositions that generate, at the same time, opposite models of behavior and the diverse organizations of societies. The invention of new ideas and implementation of new technologies shift the probability pattern of reflexive choices, appearing as internal assessments by the individual agents within a society. They direct the changes in the preference of reflexive types. Typical examples of reflexive types are those that correspond to alternative social structures formed during social evolution. The dynamics of societies and of interactions between societies are thus based on the interference of dynamic oppositions appearing as opposite reflexive structures and on the establishment of different patterns during such interferences. These are opposite in respect of the types of relation to other members of society and to the type of social organization. At different times in the history of human civilization, these changing patterns resulted in the formation and splitting of large empires, the development and spreading of new technologies, and consequent periods of growth, well-being and decline and decay. This chapter summarizes and develops the approach to social science suggested in the paper of Igamberdiev (Igamberdiev in Prog. Biophys. Mol. Biol. 131:336–347, 2017).
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The language of computer science is laced with metaphor. We argue that computer science metaphors provide a conceptual framework in which to situate constantly emerging new ontologies in computational environments. But how computer science metaphors work does not fit neatly into prevailing general theories of metaphor. We borrow from these general theories while also considering the unique role of computer science metaphors in learning, design, and scientific analysis. We find that computer science metaphors trade on both preexisting and emerging similarities between computational and traditional domains, but owing to computer science's peculiar status as a discipline that creates its own subject matter, the role of similarity in metaphorical attribution is multifaceted.
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Designant par l'expression de mecanisme historique la proposition selon laquelle l'esprit est une machine, l'A. distingue, parmi les developpements de la these mecaniste au cours du XX e siecle, un mecanisme etroit (narrow) affirmant que l'esprit est une machine de Turing, d'une part, et un mecanisme etendu (wide) affirmant que l'esprit est une machine, certes, mais une machine qui contient la possibilite d'autres machines traitant des processus de l'information et qui ne se reduisent pas a la machine universelle de Turing. L'A. montre que Turing et Church eux-memes ne peuvent accepter la version etroite du mecanisme, refutee par les developpements recents des modeles de calcul non-conventionnels tels que l'hypothese dynamique dans la domaine des sciences cognitives
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