Minds and Machines

ISSNs: 0924-6495, 1572-8641

23 found

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  1.  19
    An Alternative to Cognitivism: Computational Phenomenology for Deep Learning.Pierre Beckmann, Guillaume Köstner & Inês Hipólito - 2023 - Minds and Machines 33 (3):397-427.
    We propose a non-representationalist framework for deep learning relying on a novel method computational phenomenology, a dialogue between the first-person perspective (relying on phenomenology) and the mechanisms of computational models. We thereby propose an alternative to the modern cognitivist interpretation of deep learning, according to which artificial neural networks encode representations of external entities. This interpretation mainly relies on neuro-representationalism, a position that combines a strong ontological commitment towards scientific theoretical entities and the idea that the brain operates on symbolic (...)
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  2.  10
    The Non-theory-driven Character of Computer Simulations and Their Role as Exploratory Strategies.Juan M. Durán - 2023 - Minds and Machines 33 (3):487-505.
    In this article, I focus on the role of computer simulations as exploratory strategies. I begin by establishing the non-theory-driven nature of simulations. This refers to their ability to characterize phenomena without relying on a predefined conceptual framework that is provided by an implemented mathematical model. Drawing on Steinle’s notion of exploratory experimentation and Gelfert’s work on exploratory models, I present three exploratory strategies for computer simulations: (1) starting points and continuation of scientific inquiry, (2) varying the parameters, and (3) (...)
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  3.  7
    Can Deep CNNs Avoid Infinite Regress/Circularity in Content Constitution?Jesse Lopes - 2023 - Minds and Machines 33 (3):507-524.
    The representations of deep convolutional neural networks (CNNs) are formed from generalizing similarities and abstracting from differences in the manner of the empiricist theory of abstraction (Buckner, Synthese 195:5339–5372, 2018). The empiricist theory of abstraction is well understood to entail infinite regress and circularity in content constitution (Husserl, Logical Investigations. Routledge, 2001). This paper argues these entailments hold a fortiori for deep CNNs. Two theses result: deep CNNs require supplementation by Quine’s “apparatus of identity and quantification” in order to (1) (...)
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  4.  8
    Developing Artificial Human-Like Arithmetical Intelligence (and Why).Markus Pantsar - 2023 - Minds and Machines 33 (3):379-396.
    Why would we want to develop artificial human-like arithmetical intelligence, when computers already outperform humans in arithmetical calculations? Aside from arithmetic consisting of much more than mere calculations, one suggested reason is that AI research can help us explain the development of human arithmetical cognition. Here I argue that this question needs to be studied already in the context of basic, non-symbolic, numerical cognition. Analyzing recent machine learning research on artificial neural networks, I show how AI studies could potentially shed (...)
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  5.  12
    Avatars as Proxies.Paula Sweeney - 2023 - Minds and Machines 33 (3):525-539.
    Avatars will represent us online, in virtual worlds, and in technologically supported hybrid environments. We and our avatars will stand not in an identity relation but in a proxy relation, an arrangement that is significant not least because our proxies’ actions can be counted as our own. However, this proxy relation between humans and avatars is not well understood and its consequences under-explored. In this paper I explore the relation and its potential ethical consequences.
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  6.  73
    Explainable AI and Causal Understanding: Counterfactual Approaches Considered.Sam Baron - 2023 - Minds and Machines 33 (2):347-377.
    The counterfactual approach to explainable AI (XAI) seeks to provide understanding of AI systems through the provision of counterfactual explanations. In a recent systematic review, Chou et al. (Inform Fus 81:59–83, 2022) argue that the counterfactual approach does not clearly provide causal understanding. They diagnose the problem in terms of the underlying framework within which the counterfactual approach has been developed. To date, the counterfactual approach has not been developed in concert with the approach for specifying causes developed by Pearl (...)
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  7.  67
    The Role of A Priori Belief in the Design and Analysis of Fault-Tolerant Distributed Systems.Giorgio Cignarale, Ulrich Schmid, Tuomas Tahko & Roman Kuznets - 2023 - Minds and Machines 33 (2):293-319.
    The debate around the notions of a priori knowledge and a posteriori knowledge has proven crucial for the development of many fields in philosophy, such as metaphysics, epistemology, metametaphysics etc. We advocate that the recent debate on the two notions is also fruitful for man-made distributed computing systems and for the epistemic analysis thereof. Following a recently proposed modal and fallibilistic account of a priori knowledge, we elaborate the corresponding concept of a priori belief: We propose a rich taxonomy of (...)
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  8.  13
    The Blueprint for an AI Bill of Rights: In Search of Enaction, at Risk of Inaction.Emmie Hine & Luciano Floridi - 2023 - Minds and Machines 33 (2):285-292.
    The US is promoting a new vision of a “Good AI Society” through its recent AI Bill of Rights. This offers a promising vision of community-oriented equity unique amongst peer countries. However, it leaves the door open for potential rights violations. Furthermore, it may have some federal impact, but it is non-binding, and without concrete legislation, the private sector is likely to ignore it.
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  9.  58
    The Puzzling Resilience of Multiple Realization.Thomas W. Polger & Lawrence A. Shapiro - 2023 - Minds and Machines 33 (2):321-345.
    According to the multiple realization argument, mental states or processes can be realized in diverse and heterogeneous physical systems; and that fact implies that mental state or process kinds cannot be identified with particular kinds of physical states or processes. More specifically, mental processes cannot be identified with brain processes. Moreover, the argument provides a general model for the autonomy of the special sciences. The multiple realization argument is widely influential, but over the last thirty years it has also faced (...)
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  10.  15
    Meta’s Oversight Board: A Review and Critical Assessment.David Wong & Luciano Floridi - 2023 - Minds and Machines 33 (2):261-284.
    Since the announcement and establishment of the Oversight Board (OB) by the technology company Meta as an independent institution reviewing Facebook and Instagram’s content moderation decisions, the OB has been subjected to scholarly scrutiny ranging from praise to criticism. However, there is currently no overarching framework for understanding the OB’s various strengths and weaknesses. Consequently, this article analyses, organises, and supplements academic literature, news articles, and Meta and OB documents to understand the OB’s strengths and weaknesses and how it can (...)
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  11.  4
    Autonomous Force Beyond Armed Conflict.Alexander Blanchard - 2023 - Minds and Machines 33 (1):251-260.
    Proposals by the San Francisco Police Department (SFPD) to use bomb disposal robots for deadly force against humans have met with widespread condemnation. Media coverage of the furore has tended, incorrectly, to conflate these robots with autonomous weapon systems (AWS), the AI-based weapons used in armed conflict. These two types of systems should be treated as distinct since they have different sets of social, ethical, and legal implications. However, the conflation does raise a pressing question: what _if_ the SFPD had (...)
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  12.  12
    How a Minimal Learning Agent can Infer the Existence of Unobserved Variables in a Complex Environment.Benjamin Eva, Katja Ried, Thomas Müller & Hans J. Briegel - 2023 - Minds and Machines 33 (1):185-219.
    According to a mainstream position in contemporary cognitive science and philosophy, the use of abstract compositional concepts is amongst the most characteristic indicators of meaningful deliberative thought in an organism or agent. In this article, we show how the ability to develop and utilise abstract conceptual structures can be achieved by a particular kind of learning agent. More specifically, we provide and motivate a concrete operational definition of what it means for these agents to be in possession of abstract concepts, (...)
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  13.  26
    The Turing Test is a Thought Experiment.Bernardo Gonçalves - 2023 - Minds and Machines 33 (1):1-31.
    The Turing test has been studied and run as a controlled experiment and found to be underspecified and poorly designed. On the other hand, it has been defended and still attracts interest as a test for true artificial intelligence (AI). Scientists and philosophers regret the test’s current status, acknowledging that the situation is at odds with the intellectual standards of Turing’s works. This article refers to this as the Turing Test Dilemma, following the observation that the test has been under (...)
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  14. A dilemma for dispositional answers to Kripkenstein’s challenge.Andrea Guardo - 2023 - Minds and Machines 33 (1):135-152.
    Kripkenstein’s challenge is usually described as being essentially about the use of a word in new kinds of cases ‒ the old kinds of cases being commonly considered as non-problematic. I show that this way of conceiving the challenge is neither true to Kripke’s intentions nor philosophically defensible: the Kripkean skeptic can question my answering “125” to the question “What is 68 plus 57?” even if that problem is one I have already encountered and answered. I then argue that once (...)
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  15.  44
    A Model Solution: On the Compatibility of Predictive Processing and Embodied Cognition.Luke Kersten - 2023 - Minds and Machines 33 (1):113-134.
    Predictive processing (PP) and embodied cognition (EC) have emerged as two influential approaches within cognitive science in recent years. Not only have PP and EC been heralded as “revolutions” and “paradigm shifts” but they have motivated a number of new and interesting areas of research. This has prompted some to wonder how compatible the two views might be. This paper looks to weigh in on the issue of PP-EC compatibility. After outlining two recent proposals, I argue that further clarity can (...)
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  16.  18
    Enactivism Meets Mechanism: Tensions & Congruities in Cognitive Science.Jonny Lee - 2023 - Minds and Machines 33 (1):153-184.
    Enactivism advances an understanding of cognition rooted in the dynamic interaction between an embodied agent and their environment, whilst new mechanism suggests that cognition is explained by uncovering the organised components underlying cognitive capacities. On the face of it, the mechanistic model’s emphasis on localisable and decomposable mechanisms, often neural in nature, runs contrary to the enactivist ethos. Despite appearances, this paper argues that mechanistic explanations of cognition, being neither narrow nor reductive, and compatible with plausible iterations of ideas like (...)
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  17.  8
    Computers as Interactive Machines: Can We Build an Explanatory Abstraction?Alice Martin, Mathieu Magnaudet & Stéphane Conversy - 2023 - Minds and Machines 33 (1):83-112.
    In this paper, we address the question of what current computers are from the point of view of human-computer interaction. In the early days of computing, the Turing machine (TM) has been the cornerstone of the understanding of computers. The TM defines what can be computed and how computation can be carried out. However, in the last decades, computers have evolved and increasingly become interactive systems, reacting in real-time to external events in an ongoing loop. We argue that the TM (...)
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  18.  5
    Correction to: The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems.Jakob Mökander, Margi Sheth, David S. Watson & Luciano Floridi - 2023 - Minds and Machines 33 (1):249-249.
  19.  16
    The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems.Jakob Mökander, Margi Sheth, David S. Watson & Luciano Floridi - 2023 - Minds and Machines 33 (1):221-248.
    Organisations that design and deploy artificial intelligence (AI) systems increasingly commit themselves to high-level, ethical principles. However, there still exists a gap between principles and practices in AI ethics. One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope. Put differently, the question to which systems and processes AI ethics principles ought to apply remains unanswered. Of course, there exists no universally accepted definition of AI, and different systems pose different ethical (...)
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  20.  15
    Attitudinal Tensions in the Joint Pursuit of Explainable and Trusted AI.Devesh Narayanan & Zhi Ming Tan - 2023 - Minds and Machines 33 (1):55-82.
    It is frequently demanded that AI-based Decision Support Tools (AI-DSTs) ought to be both explainable to, and trusted by, those who use them. The joint pursuit of these two principles is ordinarily believed to be uncontroversial. In fact, a common view is that AI systems should be made explainable so that they can be trusted, and in turn, accepted by decision-makers. However, the moral scope of these two principles extends far beyond this particular instrumental connection. This paper argues that if (...)
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  21.  18
    Grounding the Vector Space of an Octopus: Word Meaning from Raw Text.Anders Søgaard - 2023 - Minds and Machines 33 (1):33-54.
    Most, if not all, philosophers agree that computers cannot learn what words refers to from raw text alone. While many attacked Searle’s Chinese Room thought experiment, no one seemed to question this most basic assumption. For how can computers learn something that is not in the data? Emily Bender and Alexander Koller ( 2020 ) recently presented a related thought experiment—the so-called Octopus thought experiment, which replaces the rule-based interlocutor of Searle’s thought experiment with a neural language model. The Octopus (...)
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  22.  69
    Reasoning with Concepts: A Unifying Framework.Peter Gärdenfors & Matías Osta-Vélez - 2023 - Minds and Machines 1 (3):451-485.
    Over the past few decades, cognitive science has identified several forms of reasoning that make essential use of conceptual knowledge. Despite significant theoretical and empirical progress, there is still no unified framework for understanding how concepts are used in reasoning. This paper argues that the theory of conceptual spaces is capable of filling this gap. Our strategy is to demonstrate how various inference mechanisms which clearly rely on conceptual information—including similarity, typicality, and diagnosticity-based reasoning—can be modeled using principles derived from (...)
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  23.  41
    Reasoning with Concepts: A Unifying Framework.Gardenfors Peter & Osta-Vélez Matías - 2023 - Minds and Machines.
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