Authors:Roman Krzanowski Pages: 7 - 13 Abstract: This special edition of Philosophical Problems in Science (Zagadnienia Filozoficzne w Nauce or ZFN) focuses on concepts of information and computing. On reading this issue, you may be surprised by the absence of traditional perspectives and themes that one would usually expect from such collections, but this apparent oversight is deliberate. The eight papers collected in this special edition of ZFN bring together perspectives that aim to inspire readers rather than confirm concepts that have already been researched. The main motivation behind this collection is a desire to explore the philosophical dimensions of computing and information sciences. Thus, for anyone looking for new ideas related to the philosophy of computing and information and wondering what is on the horizon, this special edition of ZFN may be the place to start. PubDate: 2022-12-30
Authors:Gordana Dodig-Crnkovic Pages: 17 - 46 Abstract: Learning from contemporary natural, formal, and social sciences, especially from current biology, as well as from humanities, particularly contemporary philosophy of nature, requires updates of our old definitions of cognition and intelligence. The result of current insights into basal cognition of single cells and evolution of multicellular cognitive systems within the framework of extended evolutionary synthesis (EES) helps us better to understand mechanisms of cognition and intelligence as they appear in nature. New understanding of information and processes of physical (morphological) computation contribute to novel possibilities that can be used to inspire the development of abiotic cognitive systems (cognitive robotics), cognitive computing and artificial intelligence. PubDate: 2022-12-30
Authors:Javier Toscano Pages: 47 - 76 Abstract: Describing an algorithm can provide a formalization of a specific process. However, different ways of conceptualizing algorithms foreground certain issues while obscuring others. This article attempts to define an algorithm in a broad sense as a cultural activity of key importance to make sense of socio-cognitive structures. It also attempts to develop a sharper account on the interaction between humans and tools, symbols and technologies. Rather than human or machine-centered analyses, I draw upon sociological and anthropological theories that underline social practices to propose expanding our understanding of an algorithm through the notion of ‘collective intentionalities’. To make this term clear, a brief historical review is presented, followed by an argumentation on how to incorporate it in an integral perspective. The article responds to recent debates in critical algorithm studies about the significance of the term. It develops a discussion along the lines of cognitive anthropology and the cognitive sciences, therefore advancing a definition that is grounded in observed practices as well as in modeled descriptions. The benefit of this approach is that it encourages scholars to explore cognitive structures via archaeologies of technological assemblages, where intentionalities play a defining role in understanding socio-structured practices and cognitive ecologies. PubDate: 2022-12-30
Authors:Alice Martin, Mathieu Magnaudet, Stéphane Conversy Pages: 77 - 119 Abstract: Computers are increasingly interactive. They are no more transformational systems producing a final output after a finite execution. Instead, they continuously react in time to external events that modify the course of computing execution. While philosophers have been interested in conceptualizing computers for a long time, they seem to have paid little attention to the specificities of interactive computing. We propose to tackle this issue by surveying the literature in theoretical computer science, where one can find explicit proposals for a model of interactive computing. In that field, the formal modelling of interactive computing systems has been brought down to whether the new interaction models are reducible to Turing Machines. There are three areas where interaction models are framed. The comparison between TMs and interactive system models is at stake in all of them. These areas are namely some works on concurrency by Milner, on Reactive Turing Machines, and on interaction as a new computing paradigm. For each of the three identified models, we present its motivation, sum up its account for interaction and its legacy, and point out issues regarding the understanding of computers. The survey shows difficulties for epistemologists. The reason is that these analyses focus on the formal equivalence between interactive models of computation and classic ones. Such a project is different from addressing how a computing machine can be interactive: in other words, which mechanisms allow it. PubDate: 2022-12-30
Authors:Hyungrae Noh Pages: 121 - 145 Abstract: Post-comatose patients are classified as being in a minimally conscious state when they have executive functions. Because traditional behavioral assessments may not capture signs of executive functions in post-comatose patients, clinicians look to localized brain activities in response to task instructions, such as imagining wiggling toes, to diagnose minimal consciousness. This paper critically assesses the assumption underlying such alternative methods: that brain activities are neural signals conveying information about minimal consciousness. Based on a Shannon-inspired idea of information, I distinguish between informational and engineering aspects of clinical tasks. The informational aspect concerns the conditional probability that, for example, given activity in the motor areas of the brain in response to task instructions, a patient is imagining wiggling toes. The engineering aspect concerns efficient activation of the relevant brain areas in a patient under the task conditions. This distinction shows that the current alternative methods are not informationally problematic, but are structurally “ill-formed.” For instance, the toe-imagery task requires the capacity to comprehend syntactically complex sentences, which can be dissociated from minimal consciousness. I propose a misrepresentation task, which tests the capacity to misconceptualize lukewarm water as melting wax, as a supplement to the current alternative methods. This task is as informationally reliable as these methods, but is structurally “well-formed,” as it does not rely methodologically on prerequisites such as language comprehension. PubDate: 2022-12-30
Authors:Łukasz Mścisławski Pages: 147 - 169 Abstract: As one may have noticed, the title of this paper is somewhat provocative. We found Roman Krzanowski’s (2020a,b,c; 2022) proposed approach to the problem of information very intriguing. Our aim here is to highlight some advantages when it comes to answering some fundamental questions in the philosophy of physics and metaphysics, as well as the philosophy of information and computer science. This issue is of great importance, so we propose that the introduction of some subtle distinctions between ontological and epistemological information can be regarded as being analogous to G.F.R. Ellis’s analyses of the passage of time in his concept of the Crystallizing Block Universe (Ellis and Goswami, 2012). This analogy could be useful when further studying the relations between different types of information. We also suggest some subjects for further study, ones where Krzanowski’s proposal could serve as a very solid foundation for examining traditional metaphysical issues by combining classical philosophical doctrines with the new approach. PubDate: 2022-12-30
Authors:Kristina Šekrst, Sandro Skansi Pages: 171 - 196 Abstract: Machine learning and essentialism have been connected in the past by various researchers, in order to state that the main paradigm in machine learning processes is equivalent to choosing the “essential” attributes for the machine to search for. Our goal in this paper is to show that there are connections between machine learning and essentialism, but only for some kinds of machine learning, and often not including deep learning methods. Similarity-based approaches, more connected to the overall prototype theory, spanning from psychology and linguistics, seem more suited for pattern recognition and complex deep-learning issues, while for classification problems, mostly for unsupervised learning, essentialism seems like the best choice. In order to illustrate the difference better, we will connect both paths to their sources in other disciplines and see how human psychology influences our decision in machine-learning modeling as well. This leads to a philosophically very interesting consequence: even in the setting of supervised machine learning, essences are not present in data, but in targets, which in turn means that the categories which purport to be essences are in fact human-made, and hand-coded in the targets. The success of machine learning, therefore, does not give any substantial evidence for the independent existence of essential properties. Our stance here is to state that neither the existence nor the lack of “essential” properties in machine learning can lead to metaphysical, i.e., ontological claims. PubDate: 2022-12-30
Authors:Roman Krzanowski, Pawel Polak Pages: 197 - 230 Abstract: In this paper, we examine the meta-ontology of AI systems with human-level intelligence, with us denoting such AI systems as AIE. Meta-ontology in philosophy is a discourse centered on ontology, ontological commitment, and the truth condition of ontological theories. We therefore discuss how meta-ontology is conceptualized for AIE systems. We posit that the meta-ontology of AIE systems is not concerned with computational representations of reality in the form of structures, data constructs, or computational concepts, while the ontological commitment of AIE systems is directed toward what exists in the outside world. Furthermore, the truth condition of the ontology (which is meta-ontological assumption) of AIE systems does not require consistency with closed conceptual schema or ontological theories but rather with reality, or in other words, “what is the world” (Smith, 2019, p.57). In addition, the truth condition of AIE systems is verified through operational success rather than by coherence with theories. This work builds on ontological postulates about AI systems that were formulated by Brian Cantwell Smith (2019). PubDate: 2022-12-30
Authors:Krzysztof Sołoducha Pages: 231 - 255 Abstract: In this paper, we focus on the analysis of Eliezer Yudkowsky’s concept of “coherent extrapolated volition” (CEV) as a response to the need for a post-conventional, persuasive morality that meets the criteria of active trust in the sense of Anthony Giddens, which could be used in the case of autonomous machines. Based on the analysis of the results of the Moral Machine project, we formulate some guidelines for transformation of the idea of a coherent extrapolated volition into the concept of a coherent, extrapolated and clustered volition. The argumentation used in the paper is intended to show that the idea of CEV transformed into its clustered version can be used to build a technically and socially efficient decision-making pattern database for autonomous machines. PubDate: 2022-12-30
Authors:Adam Olszewski Pages: 259 - 280 Abstract: The title question of the paper has its empirical origin in the form of an individual’s existential experience arising from the personal use of a computer, which we attempt to describe in the first section. The rest of the entire paper can be understood as a philosophical essay answering the question posed. First the connection between the main problem of the article and its “premonition” by mankind, which was expressed in the form of ancient myths and legends, is briefly suggested. After shortly discussing the problems that early considerations of AI focused on, i.e. whether machines can think at all, we move on to reformulate our title question, about the possibility of outsmarting AI. This outsmarting will be understood by us in a rather limited way as to prevent a machine from completing its implemented task. To achieve this objective, after softly clarifying the basic terms, an analogy is built between the “outsmarting” of a machine by a human (the target domain) and the playing of a mathematical game between two players (the base domain), where this outsmarting is assigned a “winning strategy” in the certain game. This mathematical model is formed by games similar to Banach-Mazur games. The strict theorems of such games are then proved and applied to the target of the analogy. We then draw conclusions and look for counter-examples to our findings. The answer to the title question posed is negative, and it is not clear how far it should be taken seriously. PubDate: 2022-12-30
Authors:Kazimierz Trzęsicki Pages: 281 - 332 Abstract: Scientific knowledge is acquired according to some paradigm. Galileo wrote that the “book of nature” was written in mathematical language and could not be understood unless one first understood the language and recognized the characters with which it was written. It is argued that Turing planted the seeds of a new paradigm. According to the Turing Paradigm, the “book of nature” is written in algorithmic language, and science aims to learn how the algorithms change the physical, social, and human universe. Some sources of the Turing Paradigm are pointed out, and a few examples of the application of the Turing Paradigm are discussed. PubDate: 2022-12-30
Authors:Paweł Polak Pages: 335 - 345 Abstract: This review article discusses the book of Roman Krzanowski, Ontological Information: Information in the Physical World, which is published by World Scientific. Krzanowski’s book makes a very important contribution to the contemporary discussion about the nature of information. The author analyzes the concept of ontological information and its uses in the works of scientists from various disciplines, resulting in an innovative and inspiring analysis that every philosopher involved in the philosophy of information should read. PubDate: 2022-12-30
Authors:Paweł Polak Pages: 347 - 356 Abstract: This review article discusses Andrzej Bielecki’s book Models of Neurons and Perceptrons: Selected Problems and Challenges, as published by Springer International Publishing. This work exemplifies “philosophy in science” by adopting a broad, multidisciplinary perspective for the issues related to the simulation of neurons and neural networks, and the author has addressed many of the important philosophical assumptions that are entangled in this area of modeling. Bielecki also raises several important methodological issues about modeling. This book is recommended for any philosophers who wish to learn more about the current state of neural modeling and find inspiration for a deeper philosophical reflection on the subject. PubDate: 2022-12-30
Authors:Łukasz Mścisławski Pages: 357 - 369 Abstract: Wrocław University of Science and Technology, Poland This article is a review of the book Making AI Intelligible. Philosophical Foundations, written by Herman Cappelen and Josh Dever, and published in 2021 by Oxford University Press. The authors of the reviewed book address the difficult issue of interpreting the results provided by AI systems and the links between human-specific content handling and the internal mechanisms of these systems. Considering the potential usefulness of various frameworks developed in philosophy to solve the problem, they conduct a thorough analysis of a wide spectrum of them, from the use of Saul Kripke’s work to a critical analysis of the explainable AI current. PubDate: 2022-12-30
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