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Computational domestication of ignorant entities

Abstract

Eco-cognitive computationalism considers computation in context, following some of the main tenets advanced by the recent cognitive science views on embodied, situated, and distributed cognition. It is in the framework of this eco-cognitive perspective that we can usefully analyze the recent attention in computer science devoted to the importance of the simplification of cognitive and motor tasks caused in organic entities by the morphological features: ignorant bodies can be domesticated to become useful “mimetic bodies”, that is able to render an intertwined computation simpler, resorting to that “simplexity” of animal embodied cognition, which represents one of the main quality of organic agents. Through eco-cognitive computationalism we can clearly acknowledge that the concept of computation changes, depending on historical and contextual causes, and we can build an epistemological view that illustrates the “emergence” of new kinds of computations, such as the one regarding morphological computation. This new perspective shows how the computational domestication of ignorant entities can originate new unconventional cognitive embodiments. In the last part of the article I will introduce the concept of overcomputationalism, showing that my proposed framework helps us see the related concepts of pancognitivism, paniformationalism, and pancomputationalism in a more naturalized and prudent perspective, avoiding the excess of old-fashioned ontological or metaphysical overstatements.

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Notes

  1. 1.

    As I will explain below in this article the Physical Reservoir Computing (Physical RC) represents an interesting example of the novel perspective of morphological computation.

  2. 2.

    In the following section I will treat both non-living and living bodies as “ideally” ignorant because I am interested in the “active” exploitation of both entities to the aim of getting conventional and unconventional computational performances endowed with cognitive consequences.

  3. 3.

    Also Burgin and Dodig-Crnkovic (2015) present a rich taxonomy of existing notions of computation and propose to adopt an open-ended and evolving concept of computation that will be able to continuously absorb new insights from sciences.

  4. 4.

    Traditional classical studies that see life as cognition and cognition as life are due to Maturana and Varela (1980, 1987), Maturana (1988) and Varela (1979, 1997), as also emphasized by Stewart (1996).

  5. 5.

    The term abduction refers to all the cognitive activities related to the generation of hypothesis, also in the creative cases. I have extendedly studied abductive cognition, see the most recent (Magnani 2009, 2017).

  6. 6.

    I have provided further examples that illustrate the role played by cognition in animals and plants in chapter five of (Magnani 2009).

  7. 7.

    On the concept of “natural computing” and its variegated meanings see below Sect. 6. In Magnani (2018a) I have illustrated some epistemological problems related to this area of research.

  8. 8.

    A further note about these theories is illustrated in Sect. 7.1.

  9. 9.

    Edwin Hutchins provided a new description of problem solving processes in actual work settings opening the door of distributed cognition theories, and to supply a new framework for cognitive science generally. In his seminal study Hutchins (1995) describes how agents exploit tools and instruments (and so external cognitive representations) to produce, create, manipulate, and maintain representational states, taking advantage of distributed physical and “material” properties of the external representational media at stake. On the interaction and on the function of external representations as material “anchors” for conceptual blends see Hutchins (2005).

  10. 10.

    In Sect. 4.2 below, I will exploit the Peircean concept of semiosis to explain how a physical computational system itself becomes a special domesticated actuator of semiosis.

  11. 11.

    The reader does not have to misunderstand my claim: obviously, even if knowledge about cognition and its relation to information processing/computation is steadily increasing, that does not prevent us from looking into the present state of the art of the domain knowledge within research fields that study it. It is well-known that there are extremely quickly developing fields such as for example machine learning or AI, but the very fact that they are rapidly evolving does not prevent us from talking about them. Neural information processing is the whole research area with dedicated conferences, such as NeurIPS 2019, Thirty-third Conference on Neural Information Processing Systems [https://neurips.cc/Conferences/2019] and ICONIP2019, The 26th International Conference on Neural Information Processing [http://ajiips.com.au/iconip2019]. ICONIP2019 aims at presenting professional research results and enthusiastic discussions among researchers, scientists, and industry professionals who are working in neural network, deep learning, and related bio-inspired computing fields. The theme of the conference is Human Centred Computing: use of human sensor data to predict or model human emotion, intentions and goals with an overall aim to produce computer systems which are useful, usable, and sympathetic to users. Apart from the field of neural information processing/neural computation there is a whole field of material computing, active matter, programmable matter or similar.

  12. 12.

    I have proposed this expression in Magnani (2001): it is derived from the cognitive anthropologist Hutchins, I have already quoted in the previous section, who illustrated the so-called “mediating structures” to explain the cognitive role played by various external tools, instruments, and props, in a situation of navigation. Anyway, also the externalized written language is an everyday example of a “mediating structure” with cognitive features, so mathematical symbols, diagrams, simulations, computer representations. etc.: “Language, cultural knowledge, mental models, arithmetic procedures, and rules of logic are all mediating structures too. So are traffic lights, supermarkets layouts, and the contexts we arrange for one another’s behavior. Mediating structures can be embodied in artifacts, in ideas, in systems of social interactions [...]” (Hutchins 1995, pp. 290–291) that function as an enormous new source of information and knowledge.

  13. 13.

    In Magnani (2018a) I have recently described Turing’s interesting perspective, illustrated in Intelligent Machinery (Turing 1969).

  14. 14.

    The reasons that explain the adoption of this adjective, endowed with a moral halo, are illustrated below in this section.

  15. 15.

    I instead used the term “mimetic minds” to refer to the classical digital computation: Turing’s Practical Computing Machines (PCMs) are mimetic minds. They are capable to mime the mind in a real universal way: we do not have to adopt many several machines that do a variety of tasks, it is just sufficient to “program” this universal machine to have all the required tasks performed. I think these mimetic minds represent a kind of triumph of that process of externalization of cognitive powers to the external environment at work since our ancestors times I have illustrated in Magnani (2009, chapter three).

  16. 16.

    Simplexity regards the possible complementary relationship between complexity and simplicity in the framework of a dynamic interplay between means and ends (cf. Berthoz and Petit 2014).

  17. 17.

    Cfr. also below, Sect. 7.

  18. 18.

    Vallverdú is also inclined to attribute a relevant epistemological dignity to computational modeling (he calls this aspect “computational epistemology”), not only because scientific knowledge (for example creative reasoning) is currently obviously performed/mediated by computational tools and models, but also for its capacity to “directly” produce knowledge. He even says: “‘Knowing’ no longer constitutes the strict scope of the human mind (Clark 2003). The automatization of the problem of the four colors or the conjecture of Kepler, both already theorems, are examples of this new form of obtaining knowledge” (Vallverdú i Segura 2009, pp. 565–566). In a similar and even more optimistic vein (Kari and Rozenberg 2008, p. 83) observe: “In these times brimming with excitement, our task is nothing less than to discover a new, broader, notion of computation, and to understand the world around us in terms of information processing. Let us step up to this challenge. Let us befriend our fellow the biologist, our fellow the chemist, our fellow the physicist, and let us together explore this new world. Let us, as computers in the future will, embrace uncertainty. Let us dare to ask afresh: ‘What is computation?’, ‘What is complexity?’, ‘What are the axioms that define life? Let us relax our hardened ways of thinking and, with deference to our scientific forebears, let us begin anew”’.

  19. 19.

    An interesting objection provided by an anonymous reviewer to this idea of prediction is the following: “Computers are not always used to predict. For example, the operating system is used to control processes in the computer. Computers today are used a lot to generate behavior. Some are used to operate data bases. Some computational processes are run to show movies, play music, communicate and enable communication. Some are simply calculating values of given equations for given inputs. While performing calculation, computer is typically not predicting. So if a process fails to predict it still can be a computational process. Wolfram and Chaitin talk about computing universe that computes its own next state. It does not predict as it is an extremely complex process, but it unfolds in real time”. To avoid misunderstandings I have to make clearer that Horsman et al.’s idea of a computer as a physical system used to predict the outcome of an abstract dynamics does not refer to the final performance or “use” of a computational program, that not necessarily regards predictive performances. It just refers to the fact that what I call an ignorant technological entity can be domesticated to perform a “physical behavior” which it is itself to be considered predictable and so reliable.

  20. 20.

    The reader that is interested in the interplay Discrete State Machine (endowed with a Laplacian behavior) and continuous systems in computer science can refer to Longo (2009a, b) and to my recent Magnani (2018a, b).

  21. 21.

    However, that which Horsman et al. see as a problem, Kari and Rozenberg, in “The many facets of natural computing” (Kari and Rozenberg 2008) instead see as inspiration and challenge of two-way learning processes endowed with a heuristic worth. To make an example, we learn about neurons applying information-processing model on them and comparing to the empirical results. We adjust both the model of the physical system and the model of computation, recursively, and improve both in the process.

  22. 22.

    Interestingly “This is how we can escape from falling into the trap of ‘everything is information’ or ‘the universe is a computer’: a system may potentially be a computer, but without an encode and a decode step it is just a physical system” (Horsman et al. 2014, p. 15). I have to clearly note that the process of encoding and decoding refers here to an act of delegation intentionally performed. As recently remarked by Horsman et al. (2017a, b) this is also happening in the case of the “computational domestication” of unconventional substrates such as in the case of DNA computing. Indeed, how about DNA computing going on in the cell? This does not have to be considered as an “objective” natural process of encoding and decoding, but just another case of computational delegation to biological entities that render possible that kind of computational/cognitive domestication I am illustrating in this article. I will provide further details on this issue below in the Sect. 6.1 “Information Units Unconventionally Computed by Biological Systems”.

  23. 23.

    Cf. the clear and synthetic illustration given in Müller and Hoffmann (2017).

  24. 24.

    More details are provided by Müller and Hoffmann (2017, p. 3) and Hauser (2011, 2014).

  25. 25.

    Several concrete physical systems—certainly not directly and technological constructed for computation—can play the role as reservoirs: a well-known example is the soft silicone based octopus arm, actually used to emulate desired nonlinear dynamical systems and borrowed to perform computations and to implement a feedback controller. For further details cf. Hauser et al. (2011, 2014) and Nakajima et al. (2015).

  26. 26.

    On this issue, cf. also Horsman et al. (2014, pp. 19–22).

  27. 27.

    It is important to remember that, in 1952, though, Turing too dealt with the problems of morphology (Turing 1952). He indeed published an article on morphogenesis which presented a very original, but non computational, non-linear system of action-reaction and dynamic diffusion, in which he proposed what he called a model of the physical phenomenon in question. He thus sought to propose a structure of determination, by means of the equations describing causal interaction in the action-reaction process (Longo 2009a).

  28. 28.

    On JVCI-syn3.0 as “a working approximation of a minimal cell” see Hutchison III et al. (2016, p. aad6253-1).

  29. 29.

    Indeed synthetic organisms are currently designed at the DNA level, which limits the complexity of the systems.

  30. 30.

    A 2018 AAAI Fall Symposium has been devoted to these studies: “Artificial Intelligence for Synthetic Biology”, October 18–20, 2018, Arlington, Virginia, USA. See also Yaman et al. (2018).

  31. 31.

    Furthermore, on how dynamical systems in general “beyond the digital hegemony” store and process information as a fundamental question that touches a remarkably wide set of contemporary issues, cf. Crutchfield et al. (2010).

  32. 32.

    In Magnani (2018a) I have illustrated the fundamental epistemological aspects of this kind of studies.

  33. 33.

    We also have to remember that important contributions of molecular computing also favored the understanding of some central issues of the nanosciences, for example self-assembly.

  34. 34.

    Further details concerning this difference can be usefully found in Kari et al. (2012). On the very recent exploitation of various artificial molecular devices, including some made of DNA or RNA to develop robotic systems cf. Hagiya et al. (2016). A molecular robot is composed by sensors, computers, and actuators, all made of molecular devices, and “reacts autonomously to its environment by observing the environment, making decisions with its computers, and performing actions upon the environment. Molecular computers should thus be the intelligent controllers of such molecular robots. Such controllers can naturally be regarded as hybrid systems because the environment, the robot, and the controller are all state transition systems having discrete and continuous states and transitions” (cit., p. 4).

  35. 35.

    Still in Magnani (2018a), taking advantage of the studies provided by Longo (2009a,2012,2017) I have described the dangers that can arise by thinking that mimetic computational digital modeling “is” directly, ipso facto, scientific knowledge: for example, in the case of biological organisms, the gap between simulation and intelligibility is very strong, also because the variability is dominant. A different view is advanced by Denning (2007): information processes and computation continue to be found abundantly in the deep structures of many fields. Computing is not—in fact, never was—a science only of the artificial, cf. also Denning and Martell (2015). A good example of the interplay between natural science and computational modeling is given by Fisher and Henzinger (2007), who call the approach of constructing computational models of biological systems “executable biology”, which focuses on the design of executable computer algorithms that mimic biological phenomena, an approach that the authors think must be integrated into biological research related to mathematical modeling.

  36. 36.

    Pancomputationalism is also usefully discussed in the recent Mollo (2019) in the framework of an interesting and intricate comparison between the so-called “computational perspectivalism” and the mainstream accounts of physical computation, especially the teleologically-based mechanistic view.

  37. 37.

    I have treated in detail this problem in Magnani (2018a).

  38. 38.

    See for example my own book Magnani (2007).

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Acknowledgements

Funds: Blue Sky Research 2017 (Grant No. BSR1780130)—University of Pavia, Pavia, Italy. For the instructive criticisms and precedent discussions and correspondence that helped me to develop my analysis of the relationship between ignorance, abduction, and computation I am indebted and grateful to Gordana Dodig-Crnkovic, Joseph Brenner, Marcin J. Schroeder, Giuseppe Longo, Vincent Müller, Jordi Vallverdú, John Woods, Atocha Aliseda, Woosuk Park, Luís Moniz Pereira, Ping Li, to the really helpful and constructive observations of the four reviewers who have allowed to expand and enrich the content of the article, and to my collaborator Selene Arfini.

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Correspondence to Lorenzo Magnani.

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Magnani, L. Computational domestication of ignorant entities. Synthese (2020). https://doi.org/10.1007/s11229-020-02530-5

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Keywords

  • Ignorant bodies
  • Domestication of ignorant entities
  • Eco-cognitive computationalism
  • Morphological computation
  • Mimetic bodies
  • Abduction
  • Overcomputationalization