Rivista Italiana di Filosofia del Linguaggio 2020-12-30T23:18:16+01:00 Giusy Gallo Open Journal Systems <p><em><span lang="EN-US">Italian Journal of Philosophy of Language</span></em><span lang="EN-US"> (RIFL – Rivista Italiana di Filosofia del linguaggio) is a online-only blind peer reviewed journal publishing articles regarding theoretical and empirical research on Language, mainly in Philosophy, Semiotics, Psychology, Psychoanalysis and Epistemology. RIFL research team privileges an interdisciplinary approach, for a more broad view of language. For this reason, RIFL invites and accepts contributions from different research traditions. RIFL publishes papers in Italian, English, French, German, Spanish and Russian. Each issue is divided in two parts: a monographic one, and another for papers on different subjects (<em>Varia</em>). A specific section of RIFL is devoted to reviews.</span></p> The Natural Connectivity of Autonomous Systems 2020-12-30T23:18:16+01:00 Steve Battle <p><span class="fontstyle0">The principle of biological autonomy, introduced by Francisco J. Varela, addresses the dilemma of Cartesian mind-body dualism by re-casting mind and body, or subject and object, observer and observed, not as irreconcilable categories, but as complementary perspectives on the same biological phenomena. Indeed, this distinction between self and non-self may be seen as a necessary pre-condition for autonomy. An autonomous system is self-governing in that it is concerned with preserving its unique character, or unity. Furthermore, an autonomous system is operationally closed in that it forms a self-referential network without reference to an external world. This paper develops these ideas in relation to thinking about embodied, enactive robotics. As well as being constructed artefacts, what is it to look at robots as truly autonomous agents? In this context we begin to explore the concept of operational closure analytically. We utilise natural connectivity as a quantitative measure of the cyclicity of these operationally closed internal processes. In doing so we discover that increased natural connectivity of an autonomous system confers a greater behavioural robustness when it is coupled with the external world.</span> </p> 2020-12-30T22:17:46+01:00 Copyright (c) 2020 Steve Battle Scientific knowledge, algorithms and language creativity 2020-12-30T23:18:16+01:00 Giusy Gallo Claudia Stancati <p>The paper deals with the controversial problem of the definition of creativity in Artificial Intelligence research, in the recent framework of machine learning. The starting point is to consider in which sense creativity is considered in the recent researches in Artificial Intelligence, highlighting that there is not just one kind of definition researchers refer to. Then we will consider creativity in scientific theories and language.</p> 2020-12-30T22:34:44+01:00 Copyright (c) 2020 Giusy Gallo, Claudia Stancati Kazimierz Twardowski’s conception of imagination. The early-analytical example and contemporary contexts 2020-12-30T23:18:16+01:00 Rafal Kur <p>A tribute to the early-analytical provenience of reflections on the phenomenon of the imagination is not only a historical reference. In the absence of a consensus in current theories of imagination, referring to Twardowski can be philosophically refreshing and methodologically inspiring. What’s more, it seems that without establishing at least an overall topology of this mental phenomenon, we will not create a formal structure, necessary for logical machine inferences, which would also deal with other issues such as the interpretation of emotions. The problem is not trivial, because the mechanism of imagination is very complex. And that’s what Twardowski noticed when proposing a comprehensive (interdisciplinary) approach, so similar at times to some of the current existing proposals.</p> 2020-12-30T22:39:49+01:00 Copyright (c) 2020 Rafal Kur Learning through creativity: how creativity can help machine learning achieving deeper understanding 2020-12-30T23:18:16+01:00 Caterina Moruzzi <p>In this paper, I address the difficult task of analysing the nature of creativity by suggesting a more objective way of defining it. In particular, I propose a minimal account of creativity as autonomous problem-solving process. This definition is aimed at providing a baseline that researchers working in different fields can agree on and that can then be refined on a case by case basis. Developing our insight on the nature of creativity is increasingly necessary in the light of recent developments in the field of Artificial Intelligence. In the second part of the paper, I discuss how an investigation on the main features of human creativity can support the advancement of machine learning models in their current areas of weakness, such as intuition, originality, innovation, and flexibility. I suggest how methods such as modelling the human brain or simulation can be useful to extract the main mechanisms underlying creative processes and to translate them to machine learning applications. This can eventually aid both the development of machine learning systems that achieve a deeper and more intuitive understanding and our exploration of human creativity.</p> 2020-12-30T22:45:20+01:00 Copyright (c) 2020 Caterina Moruzzi Glanville’s ‘Black Box’: what can an observer know? 2020-12-30T23:18:16+01:00 Lance Nizami <p>A ‘Black Box’ cannot be opened to reveal its mechanism. Rather, its operations are inferred through input from (and output to) an ‘observer’. All of us are observers, who attempt to understand the Black Boxes that are Minds. The Black Box and its observer constitute a system, differing from either component alone: a ‘greater’ Black Box to any further-external-observer. To Glanville (1982), the further-external-observer probes the greater-Black-Box by interacting directly with its core Black Box, ignoring that Box’s immediate observer. In later accounts, however, Glanville’s greater-Black-Box inexplicably becomes unitary. Why the discrepancy? To resolve it, we start with von Foerster’s archetype ‘machines’, that are of two kinds: ‘Trivial’ (predictable) and ‘Non-Trivial’ (non-predictable). Early-on, Glanville treated the core Black Box and its observer as Trivial Machines, that gradually ‘whiten’ (reveal) each other though input and output, becoming ‘white boxes’. Later, however, Black Box and observer became Non-Trivial Machines, never fully ‘whitenable’. But Non-Trivial Machines can be concatenated from Trivial Machines, and are the only true Black Boxes; any greater-Black-Box (Non-Trivial Machine) may (within its core Black Box) involve white boxes (that are Trivial Machines). White boxes, therefore, could be the ultimate source of the greatest Black Box of all: the Mind.</p> 2020-12-30T22:50:29+01:00 Copyright (c) 2020 Lance Nizami The Communication Problem 2020-12-30T23:18:16+01:00 Michael Straeubig <p>Analysis, interpretation and construction of artificial and natural languages have been central concerns of artificial intelligence since the 1950s.</p> <p>Current applications for automated language progressing range from real-time translation of spoken language through automated discovery of sentiment in online postings to conversational agents embedded in everyday devices. Recent developments in machine learning, combined with the availability of large amounts of labelled training data, have enabled non-structural approaches to largely surpass classical techniques based on formal grammars, conceptual ontologies and symbolic representations. As the complexity and opaqueness of those stochastic models becomes more and more evident, however, the question arises if we trade gains in observable performance with a literal loss of understanding. This article presents a distinction-based approach to critically re-visit fundamental theoretical concepts such as code, information, language, communication and meaning. I will follow Niklas Luhmann’s theory of social systems by locating communication firmly within social systems. Departing from Luhmann, I do invite machines as participants into some of these systems. Finally, I propose to employ Friedemann Schulz von Thun’s 4-sided communication model in order to overcome the current information-theoretic emphasis of communication.</p> 2020-12-30T22:56:24+01:00 Copyright (c) 2020 Michael Straeubig From tools to social agents 2020-12-30T23:18:16+01:00 Anna Strasser <p>Up to now, our understanding of sociality is neatly tied to living beings. However, recent developments in Artificial Intelligence make it conceivable that we may entertain social interactions with artificial systems in the near future. With reference to minimal approaches describing socio-cognitive abilities, this paper presents a strategy of how social interactions between humans and artificial agents can be captured. Taking joint actions as a paradigmatic example, minimal necessary conditions for artificial agents are elaborated. To this end, it is first argued that multiple realizations of socio-cognitive abilities can lead to asymmetric cases of joint actions. In a second step, minimal conditions of agency and coordination in order to qualify as social agents in joint actions are discussed.</p> 2020-12-30T23:02:15+01:00 Copyright (c) 2020 Anna Strasser Jean Piaget - Evert Willem Beth, 2019, Epistemologia matematica e Psicologia. Ricerca sulle relazioni tra la logica formale e il pensiero reale, Edizioni Studium, Roma. 2020-12-30T23:18:16+01:00 Francesco Panizzoli 2020-12-30T23:05:34+01:00 Copyright (c) 2020 Francesco Panizzoli Angelo Nizza, Linguaggio e lavoro nel XXI secolo. Natura e storia di una relazione, Mimesis, Milano-Udine 2020. 2020-12-30T23:18:15+01:00 Adriano Bertollini 2020-12-30T23:08:49+01:00 Copyright (c) 2020 Adriano Bertollini