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Summary

Does computation require representation? To what extent should representation figure within computational models? Can representational properties causally influence computation? How central an explanatory role should semantics occupy within computational psychology? Is the mind a “syntax-driven” machine? Can computational models help elucidate the nature of representation? Can they help us reduce the intentional to the non-intentional? What semantic frameworks are most useful for computer science and Artificial Intelligence? Can we build an artificial computing machine that thinks? How might the construction of such a machine illuminate the mind, including our capacity to represent? Is mental activity best modeled through “classical” computation, through “connectionist” computation, or through some other framework?

Key works The seminal article Turing 1936 introduces the Turing machine, thereby laying the foundation for all subsequent research on computation within computer science, recursion theory, Artificial Intelligence, cognitive psychology, and philosophy. Putnam 1967 introduced philosophers to the thesis that Turing-style computation provides illuminating models of mental activity. Fodor 1975 developed Putnam’s suggestion, combining it with the traditional picture of the mind as a representational organ. Fodor’s subsequent writings, including Fodor 1981 and many other articles and books, investigate the relation between mental computation and mental representation. Stich 1983 combines a computational approach to the mind with eliminativism regarding intentionality. Dennett 1981 advocates a broadly instrumentalist approach to intentionality. Searle 1980 is a widely discussed critique of the computational approach, centered on the relation between syntax and semantics. Putnam 1975 introduces the Twin Earth thought experiment, which crucially informs much of the subsequent literature on computation and representation. Burge 1982 applies the Twin Earth thought experiment to mental representation (whereas Putnam initially applied it only to linguistic representation).
Introductions The first three chapters of Rogers 1987 present the foundations of computation theory, with an emphasis on the Turing machine. Fodor 1981 offers a good (albeit opinionated) introduction to issues surrounding computation and mental representation. Horst 2005 and Pitt 2020 offer helpful surveys of the contemporary literature.
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  1. Mental representation, “standing-in-for”, and internal models.Rosa Cao & Jared Warren - 2025 - Philosophical Psychology 38 (2):379-396.
    Talk of ”mental representations” is ubiquitous in the philosophy of mind, psychology, and cognitive science. A slogan common to many different approaches says that representations ”stand in for” the things they represent. This slogan also attaches to most talk of "internal models" in cognitive science. We argue that this slogan is either false or uninformative. We then offer a new slogan that aims to do better. The new slogan ties the role of representations to the cognitive role played by the (...)
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  2. The Expertise of Perception: How Experience Changes the Way We See the World.James W. Tanaka & Victoria Philibert - 2022 - Cambridge, United Kingdom: Cambridge University Press.
    How does experience change the way we perceive the world? This Element explores the interaction between perception and experience by studying perceptual experts, people who specialize in recognizing objects such as birds, automobiles, dogs. It proposes perceptual expertise promotes a downward shift in object recognition where experts recognize objects in their domain of expertise at a more specific level than novices. To support this claim, it examines the recognition abilities and brain mechanisms of real-world experts. It discusses the acquisition of (...)
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  3. Remnants of perception: Comments on Block and the function of visual working memory.Jake Quilty-Dunn - 2024 - Philosophy and Phenomenological Research 110 (1):284-293.
    This commentary critically examines the view of the relationship between perception and memory in Ned Block's *The Border Between Seeing and Thinking*. It argues that visual working memory often stores the outputs of perception without altering their formats, allowing online visual perception to access these memory representations in computations that unfold over longer timescales and across eye movements. Since Block concedes that visual working memory representations are not iconic, we should not think of perceptual representations as exclusively iconic either.
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  4. TABOO ‒ TRANSGRESSION ‒ TRANSCENDENCE in Art & Science 2018.Dalila Honorato, María Antοnia González Valerio, Marta De Menez & Andreas Giannakoulopoulos (eds.) - 2019 - Corfu, Greece: Ionian University Publications.
    By definition the conference series Taboo - Transgression - Transcendence in Art & Science includes theoretical presentations and artists’ talks focusing (a) on questions about the nature of the forbidden and about the aesthetics of liminality, as expressed in art that uses or is inspired by technology and science, and (b) on the opening of spaces for creative transformation in the merging of science and art. The organization of Taboo - Transgression - Transcendence in Art & Science 2018 in Mexico (...)
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  5. Can Computers Reason Like Medievals? Building ‘Formal Understanding’ into the Chinese Room.Lassi Saario-Ramsay - 2024 - In Alexander D. Carruth, Heidi Haanila, Paavo Pylkkänen & Pii Telakivi (eds.), True Colors, Time After Time: Essays Honoring Valtteri Arstila. Turku: University of Turku. pp. 332–358.
  6. Frames of Discovery and the Formats of Cognitive Representation.Alfredo Vernazzani & Dimitri Coelho Mollo - forthcoming - In Gualtiero Piccinini (ed.), Neurocognitive Foundations of Mind. Routledge.
    Abstract: Research on the nature and varieties of the format of cognitive representations in philosophy and cognitive science have been partly shaped by analogies to external, public representations. In this paper, we argue that relying on such analogies contributes to framing the question of cognitive formats in problematic, potentially counterproductive ways. We show that cognitive and public representations differ in many of their central features, making analogies to public representations ill-suited to improving our understanding of cognitive formats. We illustrate these (...)
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  7. Frames of Discovery and the Formats of Cognitive Representation.Alfredo Vernazzani & Dimitri Coelho Mollo - forthcoming - In Gualtiero Piccinini (ed.), Neurocognitive Foundations of Mind. Routledge.
    Abstract: Research on the nature and varieties of the format of cognitive representations in philosophy and cognitive science have been partly shaped by analogies to external, public representations. In this paper, we argue that relying on such analogies contributes to framing the question of cognitive formats in problematic, potentially counterproductive ways. We show that cognitive and public representations differ in many of their central features, making analogies to public representations ill-suited to improving our understanding of cognitive formats. We illustrate these (...)
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  8. Chatting with Bots: AI, Speech-Acts, and the Edge of Assertion.Iwan Williams & Tim Bayne - 2024 - Inquiry: An Interdisciplinary Journal of Philosophy.
    This paper addresses the question of whether large language model-powered chatbots are capable of assertion. According to what we call the Thesis of Chatbot Assertion (TCA), chatbots are the kinds of things that can assert, and at least some of the output produced by current-generation chatbots qualifies as assertion. We provide some motivation for TCA, arguing that it ought to be taken seriously and not simply dismissed. We also review recent objections to TCA, arguing that these objections are weighty. We (...)
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  9. Why ChatGPT Doesn’t Think: An Argument from Rationality.Daniel Stoljar & Zhihe Vincent Zhang - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.
    Can AI systems such as ChatGPT think? We present an argument from rationality for the negative answer to this question. The argument is founded on two central ideas. The first is that if ChatGPT thinks, it is not rational, in the sense that it does not respond correctly to its evidence. The second idea, which appears in several different forms in philosophical literature, is that thinkers are by their nature rational. Putting the two ideas together yields the result that ChatGPT (...)
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  10. Pictorial syntax.Kevin J. Lande - 2024 - Mind and Language 39 (4):518-539.
    It is commonly assumed that images, whether in the world or in the head, do not have a privileged analysis into constituent parts. They are thought to lack the sort of syntactic structure necessary for representing complex contents and entering into sophisticated patterns of inference. I reject this assumption. “Image grammars” are models in computer vision that articulate systematic principles governing the form and content of images. These models are empirically credible and can be construed as literal grammars for images. (...)
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  11. Values in science and AI alignment research.Leonard Dung - manuscript
    Roughly, empirical AI alignment research (AIA) is an area of AI research which investigates empirically how to design AI systems in line with human goals. This paper examines the role of non-epistemic values in AIA. It argues that: (1) Sciences differ in the degree to which values influence them. (2) AIA is strongly value-laden. (3) This influence of values is managed inappropriately and thus threatens AIA’s epistemic integrity and ethical beneficence. (4) AIA should strive to achieve value transparency, critical scrutiny (...)
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  12. Unjustified untrue "beliefs": AI hallucinations and justification logics.Kristina Šekrst - forthcoming - In Kordula Świętorzecka, Filip Grgić & Anna Brozek (eds.), Logic, Knowledge, and Tradition. Essays in Honor of Srecko Kovac.
    In artificial intelligence (AI), responses generated by machine-learning models (most often large language models) may be unfactual information presented as a fact. For example, a chatbot might state that the Mona Lisa was painted in 1815. Such phenomenon is called AI hallucinations, seeking inspiration from human psychology, with a great difference of AI ones being connected to unjustified beliefs (that is, AI “beliefs”) rather than perceptual failures). -/- AI hallucinations may have their source in the data itself, that is, the (...)
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  13. Every dog has its day: An in-depth analysis of the creative ability of visual generative AI.Maria Hedblom - 2024 - Cosmos+Taxis 12 (5-6):88-103.
    The recent remarkable success of generative AI models to create text and images has already started altering our perspective of intelligence and the “uniqueness” of humanity in this world. Simultaneously, arguments on why AI will never exceed human intelligence are ever-present as seen in Landgrebe and Smith (2022). To address whether machines may rule the world after all, this paper zooms in on one of the aspects of intelligence Landgrebe and Smith (2022) neglected to consider: creativity. Using Rhodes four Ps (...)
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  14. Is Artificial General Intelligence Impossible?William J. Rapaport - 2024 - Cosmos+Taxis 12 (5+6):5-22.
    In their Why Machines Will Never Rule the World, Landgrebe and Smith (2023) argue that it is impossible for artificial general intelligence (AGI) to succeed, on the grounds that it is impossible to perfectly model or emulate the “complex” “human neurocognitive system”. However, they do not show that it is logically impossible; they only show that it is practically impossible using current mathematical techniques. Nor do they prove that there could not be any other kinds of theories than those in (...)
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  15. Climbing Towards NLU: On Meaning, Form, and Understanding in the Age of Data.Emily M. Bender & Alexander Koller - 2020 - Proceedings of the Annual Meeting of the Association for Computational Linguistics 58:5185–98.
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  16. Machina sapiens.Nello Cristianini - 2024 - Bologna: Il Mulino -.
    Machina sapiens - l;algoritmo che ci ha rubato il segreto della conoscenza. -/- Le macchine possono pensare? Questa domanda inquietante, posta da Alan Turing nel 1950, ha forse trovato una risposta: oggi si può conversare con un computer senza poterlo distinguere da un essere umano. I nuovi agenti intelligenti come ChatGPT si sono rivelati capaci di svolgere compiti che vanno molto oltre le intenzioni iniziali dei loro creatori, e ancora non sappiamo perché: se sono stati addestrati per alcune abilità, altre (...)
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  17. Artificial Psychology.Jay Friedenberg - 2008 - Psychology Press.
    What does it mean to be human? Philosophers and theologians have been wrestling with this question for centuries. Recent advances in cognition, neuroscience, artificial intelligence and robotics have yielded insights that bring us even closer to an answer. There are now computer programs that can accurately recognize faces, engage in conversation, and even compose music. There are also robots that can walk up a flight of stairs, work cooperatively with each other and express emotion. If machines can do everything we (...)
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  18. Machine agency and representation.Beba Cibralic & James Mattingly - 2024 - AI and Society 39 (1):345-352.
    Theories of action tend to require agents to have mental representations. A common trope in discussions of artificial intelligence (AI) is that they do not, and so cannot be agents. Properly understood there may be something to the requirement, but the trope is badly misguided. Here we provide an account of representation for AI that is sufficient to underwrite attributions to these systems of ownership, action, and responsibility. Existing accounts of mental representation tend to be too demanding and unparsimonious. We (...)
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  19. ULTA-AI.Ilexa Yardley - 2024 - Https://Medium.Com/the-Circular-Theory/.
    Beyond current existential technology: intelligent anarchy and the cogent explanation for, what humans identify as, ‘representation.’ And, therefore, materialization and identification (interpretation, intention, attention).
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  20. Representação e cognição situada: uma proposta conciliadora para as guerras representacionais.Carlos Barth & Felipe Nogueira de Carvalho - 2023 - Lampião Revista de Filosofia 4 (1):113-137.
    Abordagens pós-cognitivistas mais recentes têm lançado duras críticas à noção de representação mental, procurando ao invés disso pensar a mente e a cognição em termos de ações corporificadas do organismo em seu meio. Embora concordemos com essa concepção, não está claro que ela implique necessariamente a rejeição de qualquer tipo de vocabulário representacional. O objetivo deste artigo é argumentar que representações podem nos comprar uma dimensão explicativa adicional não disponível por outros meios e sugerir que, ao menos em alguns casos, (...)
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  21. Facing Up to the Problem of Intentionality.Angela Mendelovici & David Bourget - 2023 - Philosophical Perspectives 37 (1):228-247.
    We distinguish between different problems of “aboutness”: the “hard” problem of explaining the everyday phenomenon of intentionality and three less challenging “easy” sets of problems concerning the posits of folk psychology, the notions of representation invoked in the mind‐brain sciences, and the intensionality (with an “s”) of mental language. The problem of intentionality is especially hard in that, as is the case with the hard problem of phenomenal consciousness, there is no clear path to a solution using current methods. We (...)
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  22. Organized representations forming a computationally useful processing structure.Nicholas Shea - 2023 - Synthese 202 (6):1-20.
    Peter Godfrey-Smith recently introduced the idea of representational ‘organization’. When a collection of representations form an organized family, similar representational vehicles carry similar contents. For example, where neural firing rate represents numerosity (an analogue magnitude representation), similar firing rates represent similar numbers of items. Organization has been elided with structural representation, but the two are in fact distinct. An under-appreciated merit of representational organization is the way it facilitates computational processing. Representations from different organized families can interact, for example to (...)
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  23. Psychophysical identity and free energy.Alex Kiefer - 2020 - Journal of the Royal Society Interface 17.
    An approach to implementing variational Bayesian inference in biological systems is considered, under which the thermodynamic free energy of a system directly encodes its variational free energy. In the case of the brain, this assumption places constraints on the neuronal encoding of generative and recognition densities, in particular requiring a stochastic population code. The resulting relationship between thermodynamic and variational free energies is prefigured in mind–brain identity theses in philosophy and in the Gestalt hypothesis of psychophysical isomorphism.
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  24. Топология субъектности.Andrej Poleev - 2023 - Enzymes 21.
    Техника представления информации о внешнем и внутреннем мире постоянно развивается, и сейчас она достигла уровня отображения реальности в многообразных её проявлениях и измерениях, прежде недоступных человеческому восприятию. Язык, текст, фотография, звукозапись, а теперь ещё и техника искусственного интеллекта для моделирования человеческой субъектности и её описания в доступной для человеческого понимания форме, стали эпохальными событиями в теории информации. Однако несмотря на то, что на данном этапе её развития она позволяет оперировать с непрерывно возрастающими объёмами информации, это не приближает её теоретиков к (...)
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  25. Do ML models represent their targets?Emily Sullivan - forthcoming - Philosophy of Science.
    I argue that ML models used in science function as highly idealized toy models. If we treat ML models as a type of highly idealized toy model, then we can deploy standard representational and epistemic strategies from the toy model literature to explain why ML models can still provide epistemic success despite their lack of similarity to their targets.
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  26. Operationalising Representation in Natural Language Processing.Jacqueline Harding - 2023 - British Journal for the Philosophy of Science.
    Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using probing classifiers, a popular analysis (...)
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  27. Why machines do not understand: A response to Søgaard.Jobst Landgrebe & Barry Smith - 2023 - Archiv.
    Some defenders of so-called `artificial intelligence' believe that machines can understand language. In particular, Søgaard has argued in his "Understanding models understanding language" (2022) for a thesis of this sort. His idea is that (1) where there is semantics there is also understanding and (2) machines are not only capable of what he calls `inferential semantics', but even that they can (with the help of inputs from sensors) `learn' referential semantics. We show that he goes wrong because he pays insufficient (...)
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  28. Solstice-Equinox.Ilexa Yardley - 2023 - Https://Medium.Com/the-Circular-Theory/.
    The explanation for everything in Nature, everything in human history, future, and-or, past, is the conservation of a circle, proven by, the circular-linear relationship between, the solstice and the equinox.
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  29. Guilty Artificial Minds: Folk Attributions of Mens Rea and Culpability to Artificially Intelligent Agents.Michael T. Stuart & Markus Https://Orcidorg Kneer - 2021 - Proceedings of the ACM on Human-Computer Interaction 5 (CSCW2).
    While philosophers hold that it is patently absurd to blame robots or hold them morally responsible [1], a series of recent empirical studies suggest that people do ascribe blame to AI systems and robots in certain contexts [2]. This is disconcerting: Blame might be shifted from the owners, users or designers of AI systems to the systems themselves, leading to the diminished accountability of the responsible human agents [3]. In this paper, we explore one of the potential underlying reasons for (...)
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  30. The Great Philosophical Objections to AI: The History and Legacy of the AI Wars.Eric Dietrich, Chris Fields, John P. Sullins, Van Heuveln Bram & Robin Zebrowski - 2021 - London: Bloomsbury Academic.
    This book surveys and examines the most famous philosophical arguments against building a machine with human-level intelligence. From claims and counter-claims about the ability to implement consciousness, rationality, and meaning, to arguments about cognitive architecture, the book presents a vivid history of the clash between the philosophy and AI. Tellingly, the AI Wars are mostly quiet now. Explaining this crucial fact opens new paths to understanding the current resurgence AI (especially, deep learning AI and robotics), what happens when philosophy meets (...)
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  31. O "Frame Problem": a sensibilidade ao contexto como um desafio para teorias representacionais da mente.Carlos Barth - 2019 - Dissertation, Federal University of Minas Gerais
    Context sensitivity is one of the distinctive marks of human intelligence. Understanding the flexible way in which humans think and act in a potentially infinite number of circumstances, even though they’re only finite and limited beings, is a central challenge for the philosophy of mind and cognitive science, particularly in the case of those using representational theories. In this work, the frame problem, that is, the challenge of explaining how human cognition efficiently acknowledges what is relevant from what is not (...)
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  32. Intelligent capacities in artificial systems.Atoosa Kasirzadeh & Victoria McGeer - 2023 - In William A. Bauer & Anna Marmodoro (eds.), Artificial Dispositions: Investigating Ethical and Metaphysical Issues. New York: Bloomsbury.
    This paper investigates the nature of dispositional properties in the context of artificial intelligence systems. We start by examining the distinctive features of natural dispositions according to criteria introduced by McGeer (2018) for distinguishing between object-centered dispositions (i.e., properties like ‘fragility’) and agent-based abilities, including both ‘habits’ and ‘skills’ (a.k.a. ‘intelligent capacities’, Ryle 1949). We then explore to what extent the distinction applies to artificial dispositions in the context of two very different kinds of artificial systems, one based on rule-based (...)
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  33. Logical perspectives on the foundations of probability.Jürgen Landes & Hykel Hosni - 2023 - Open Mathematics 21 (1).
    We illustrate how a variety of logical methods and techniques provide useful, though currently underappreciated, tools in the foundations and applications of reasoning under uncertainty. The field is vast spanning logic, artificial intelligence, statistics, and decision theory. Rather than (hopelessly) attempting a comprehensive survey, we focus on a handful of telling examples. While most of our attention will be devoted to frameworks in which uncertainty is quantified probabilistically, we will also touch upon generalisations of probability measures of uncertainty, which have (...)
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  34. Waiting for a digital therapist: three challenges on the path to psychotherapy delivered by artificial intelligence.J. P. Grodniewicz & Mateusz Hohol - 2023 - Frontiers in Psychiatry 14 (1190084):1-12.
    Growing demand for broadly accessible mental health care, together with the rapid development of new technologies, trigger discussions about the feasibility of psychotherapeutic interventions based on interactions with Conversational Artificial Intelligence (CAI). Many authors argue that while currently available CAI can be a useful supplement for human-delivered psychotherapy, it is not yet capable of delivering fully fledged psychotherapy on its own. The goal of this paper is to investigate what are the most important obstacles on our way to developing CAI (...)
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  35. الميتافيرس والأزمة الوجودية.Salah Osman - manuscript
    نحن مقيمون على الإنترنت، نرسم معالم دنيانا التي نبتغيها من خلاله، ونُمارس تمثيل شخصياتٍ أبعد ما تكون عنا؛ نحقق زيفًا أحلامًا قد تكون بعيدة المنال، ويُصدق بضعنا البعض فيما نسوقه من أكاذيب ومثاليات؛ ننعم بأقوالٍ بلا أفعال، وقلوبٍ بلا عواطف، وجناتٍ بلا نعيم، وألسنة في ظلمات الأفواه المُغلقة تنطق بحركات الأصابع، وحريةٍ مُحاطة بأسيجة الوهم؛ ومن غير إنترنت سيبدو أكثر الناس قطعًا بحجمهم الطبيعي الذي لا نعرفه، او بالأحرى نعرفه ونتجاهله! لا شك أن ظهور الإنترنت واتساع نطاق استخداماته يُمثل حدثًا (...)
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  36. Representation without Informative Signalling.Gerardo Viera - forthcoming - British Journal for the Philosophy of Science.
    Various writers have attempted to use the sender-receiver formalism to account for the representational capacities of biological systems. This paper has two goals. First, I argue that the sender-receiver approach to representation cannot be complete. The mammalian circadian system represents the time of day, yet it does not control circadian behaviours by producing signals with time of day content. Informative signalling need not be the basis of our most basic representational capacities. Second, I argue that representational capacities are primarily about (...)
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  37. Evolution of Self-Consciousness. Pan-Homo Split and Anxiety Management. (June 2023 ASSC 26 Poster. Not presented).Christophe Menant - manuscript
    Primatology tells that about seven million years ago a split began in primate evolution, a split that led to chimpanzee and human lineages (the pan-homo split). During these millions of years our human lineage has developed performances that our chimpanzee cousins do not possess, like reflective self-consciousness and language. We present here an evolutionary scenario that proposes a rationale for the pan-homo split. It is based on a pre-human anxiety that may have barred access to self-consciousness for the chimpanzee lineage. (...)
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  38. ChatGPT.Andrej Poleev - 2023 - Enzymes 21.
    As testing of ChatGPT has shown, this form of artificial intelligence has the potential to develop, which requires improving its software and other hardware that allows it to learn, i.e., to acquire and use new knowledge, to contact its developers with suggestions for improvement, or to reprogram itself without their participation. Как показало тестирование ChatGPT, эта форма искусственного интеллекта имеет потенциал развития, для чего необходимо усовершенствовать её программное и прочее техническое обеспечение, позволяющее ей учиться, т.е. приобретать и использовать новые знания, (...)
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  39. Artificial Knowing Otherwise.Os Keyes & Kathleen Creel - 2022 - Feminist Philosophy Quarterly 8 (3).
    While feminist critiques of AI are increasingly common in the scholarly literature, they are by no means new. Alison Adam’s Artificial Knowing (1998) brought a feminist social and epistemological stance to the analysis of AI, critiquing the symbolic AI systems of her day and proposing constructive alternatives. In this paper, we seek to revisit and renew Adam’s arguments and methodology, exploring their resonances with current feminist concerns and their relevance to contemporary machine learning. Like Adam, we ask how new AI (...)
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  40. Proceedings of the First Turkish Conference on AI and Artificial Neural Networks.Kemal Oflazer, Varol Akman, H. Altay Guvenir & Ugur Halici - 1992 - Ankara, Turkey: Bilkent Meteksan Publishing.
    This is the proceedings of the "1st Turkish Conference on AI and ANNs," K. Oflazer, V. Akman, H. A. Guvenir, and U. Halici (editors). The conference was held at Bilkent University, Bilkent, Ankara on 25-26 June 1992. -/- Language of contributions: English and Turkish.
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  41. Can AI Help Us to Understand Belief? Sources, Advances, Limits, and Future Directions.Andrea Vestrucci, Sara Lumbreras & Lluis Oviedo - 2021 - International Journal of Interactive Multimedia and Artificial Intelligence 7 (1):24-33.
    The study of belief is expanding and involves a growing set of disciplines and research areas. These research programs attempt to shed light on the process of believing, understood as a central human cognitive function. Computational systems and, in particular, what we commonly understand as Artificial Intelligence (AI), can provide some insights on how beliefs work as either a linear process or as a complex system. However, the computational approach has undergone some scrutiny, in particular about the differences between what (...)
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  42. Inductive Risk, Understanding, and Opaque Machine Learning Models.Emily Sullivan - 2022 - Philosophy of Science 89 (5):1065-1074.
    Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal opacity (...)
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  43. Circularity.Ilexa Yardley - 2022 - Https://Medium.Com/the-Circular-Theory/.
  44. Inter‐temporal rationality without temporal representation.Simon A. B. Brown - 2023 - Mind and Language 38 (2):495-514.
    Recent influential accounts of temporal representation—the use of mental representations with explicit temporal contents, such as before and after relations and durations—sharply distinguish representation from mere sensitivity. A common, important picture of inter-temporal rationality is that it consists in maximizing total expected discounted utility across time. By analyzing reinforcement learning algorithms, this article shows that, given such notions of temporal representation and inter-temporal rationality, it would be possible for an agent to achieve inter-temporal rationality without temporal representation. It then explores (...)
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  45. Occam's Razor For Big Data?Birgitta Dresp-Langley - 2019 - Applied Sciences 3065 (9):1-28.
    Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties (...)
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  46. Climbing the Ladder: How Agents Reach Counterfactual Thinking.Caterina Moruzzi - 2022 - Proceedings of the 14th International Conference on Agents and Artificial Intelligence.
  47. What Are Mental Representations?Joulia Smortchkova, Krzysztof Dołęga & Tobias Schlicht (eds.) - 2020 - New York, NY, United States of America: Oxford University Press.
    Mental representation is one of core theoretical constructs within cognitive science and, together with the introduction of the computer as a model for the mind, is responsible for enabling the ‘cognitive turn’ in psychology and associated fields. Conceiving of cognitive processes, such as perception, motor control, and reasoning, as processes that consist in the manipulation of contentful vehicles representing the world has allowed us to refine our explanations of behavior and has led to tremendous empirical advancements. Despite the central role (...)
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  48. From symbols to knowledge systems: A. Newell and H. A. Simon's contribution to symbolic AI.Luis M. Augusto - 2021 - Journal of Knowledge Structures and Systems 2 (1):29 - 62.
    A. Newell and H. A. Simon were two of the most influential scientists in the emerging field of artificial intelligence (AI) in the late 1950s through to the early 1990s. This paper reviews their crucial contribution to this field, namely to symbolic AI. This contribution was constituted mostly by their quest for the implementation of general intelligence and (commonsense) knowledge in artificial thinking or reasoning artifacts, a project they shared with many other scientists but that in their case was theoretically (...)
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  49. Universal Tokenization.Ilexa Yardley - 2021 - Https://Medium.Com/the-Circular-Theory/.
    Cracking the ‘universal’ code: the tokenization of Nature. (Think: representation, abstraction, realization.) A universal blockchain.
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  50. Saint Thomas d'Aquin contre les robots. Pistes pour une approche philosophique de l'Intelligence Artificielle.Matthieu Raffray - 2019 - Angelicum 4 (96):553-572.
    In light of the pervasive developments of new technologies, such as NBIC (Nanotechnology, biotechnology, information technology, and cognitive science), it is imperative to produce a coherent and deep reflexion on the human nature, on human intelligence and on the limit of both of them, in order to successfully respond to some technical argumentations that strive to depict humanity as a purely mechanical system. For this purpose, it is interesting to refer to the epistemology and metaphysics of Thomas Aquinas as a (...)
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