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, (...) elas podem participar da explicação de performances ou capacidades cognitivas. A noção de representação apresentada, como deixaremos claro ao longo do artigo, não viola os preceitos metodológicos mais caros à cognição 4E em geral e ao enativismo em particular, podendo, portanto, ser utilizada como uma ferramenta teórica útil em investigações sobre a natureza corporificada e situada da mente. (shrink)
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.
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.
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. (...) Images can have rich syntactic structure, though of a markedly different form than sentences in language. (shrink)
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 (...) постижению сути того, что определяется как реальность, бытие, и сознание. Но поскольку никакого другого способа достичь этой цели нет, кроме изучения того, как это происходит в процессе восприятия и преобразования информации у живых существ, и в частности, у человека, необходимо разобраться в принципах этого процесса. (shrink)
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.
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 (...) technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research. (shrink)
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 (...) attention to the difference between language as used by humans and the sequences of inert symbols which arise when language is stored on hard drives or in books in libraries. (shrink)
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 (...) in each context, has been adopted as a guide. By using it, we’ve been able to describe a fundamental tension between context sensitivity and the mental representations used in cognition theories. The first chapter discusses the nature of the frame problem,as well as the reasons for its persistence. In the second and third chapters, the problem is used as a measure tool in order to inquiry a few representational approaches and check how well suited they are to deal with context dependencies. The problems found are then correlated with the frame problem. Throughout the discussion, we try to show that 1) none of the evaluated approaches is capable of dealing with context sensitivity in a proper manner, but 2) that’s not a reason to think that the frame problem constitutes an argument against representational approaches in general, and 3) that it constitutes a fundamental conceptual tool in contemporary research -/- A sensibilidade ao contexto é uma das marcas distintivas da inteligência humana. Compreender o modo flexível como o ser humano pensa e age em função de um número potencialmente infinito de circunstâncias, ainda que munido de recursos finitos e limitados, é um desafio central para a filosofia da mente e para a ciência cognitiva, em particular aos que fazem uso de teorias representacionalistas. Nesse trabalho, adotou-se como fio condutor o modo como isso se manifesta no "frame problem": a dificuldade em explicar como a cognição humana reconhece, de maneira eficiente, o que é ou não relevante em cada contexto. A partir dele, buscou-se caracterizar uma tensão fundamental entre a sensibilidade ao contexto e o uso de representações mentais em teorias da cognição. O primeiro capítulo discute a natureza do frame problem, bem como as razões de sua resiliência. No segundo e terceiro capítulos, faz-se uso do problema como métrica para investigar o quão adequado é o tratamento das dependências contextuais no âmbito de várias abordagens representacionais. No decorrer da discussão, realiza-se um esforço argumentativo para mostrar que 1) nenhuma das estratégias abordadas é capaz tratar adequadamente da sensibilidade ao contexto, mas que 2) apesar disso, o frame problem não constitui argumento fatal para teorias representacionalistas em geral, e que 3) ele constitui uma ferramenta conceitual fundamental para pesquisas contemporâneas. (shrink)
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 (...) classical logic and the other on reinforcement learning. Here we defend three substantive claims. First, we argue that artificial systems are not equal in the kinds of dispositional properties they instantiate. In particular, we show that logical systems instantiate merely object-centered dispositions whereas reinforcement learning systems allow for the instantiation of agent-based abilities. Second, we explore the similarities and differences between the agent-centered abilities of artificial systems and those of humans, especially as relates to the important distinction made in the human case between habits and skills/intelligent capacities. The upshot is that the agent-centered abilities of truly intelligent artificial systems are distinctive enough to constitute a third type of agent-based ability — blended agent-based ability — raising substantial questions as to how we understand the nature of their agency. Third, we explore one aspect of this problem, focussing on whether systems of this type are properly considered ‘responsible agents’, at least in some contexts and for some purposes. The ramifications of our analysis will turn out to be directly relevant to various ethical concerns of artificial intelligence. (shrink)
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 (...) systems capable of delivering psychotherapy in the future. To this end, we formulate and discuss three challenges central to this quest. Firstly, we might not be able to develop effective AI-based psychotherapy unless we deepen our understanding of what makes human-delivered psychotherapy effective. Secondly, assuming that it requires building a therapeutic relationship, it is not clear whether psychotherapy can be delivered by non-human agents. Thirdly, conducting psychotherapy might be a problem too complicated for narrow AI, i.e., AI proficient in dealing with only relatively simple and well-delineated tasks. If this is the case, we should not expect CAI to be capable of delivering fully-fledged psychotherapy until the so-called “general” or “human-like” AI is developed. While we believe that all these challenges can ultimately be overcome, we think that being mindful of them is crucial to ensure well-balanced and steady progress on our path to AI-based psychotherapy. (shrink)
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 (...) فريدًا متناميًا في مسيرة الإنسان الحضارية وتغيير الطريقة التي يعيش بها البشر حياتهم. ومع ذلك، لا ينطوي أي تعريف للإنترنت حتى الآن على إشارة للواقع الافتراضي، رغم تعايشنا معه وفيه بالفعل؛ فنحن نتفاعل ونتبادل المعلومات، ونشتري ونبيع، ونلعب ونضحك ونبكي، ونمارس أدق تفاصيل حياتنا عبر الإنترنت؛ وكل ما كنا نقوم به من قبل بالحركات الجسدية المكانية أوكلنا مهمة القيام به إلى عقولنا! ولعل هذا ما تفعله كلمة «ميتافيرس»، وهي كلمة استخدمها لأول مرة كاتب الخيال العلمي الأمريكي «نيل ستيفنسون» في روايته «تحطم الثلج» (1992)، للدلالة على تفاعل البشر مع بعضهم البعض ومع البرمجيات في فضاء افتراضي ثلاثي الأبعاد مشابه للعالم الفعلي. (shrink)
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 (...) control, and only when specific conditions obtain does this control require informative signalling. (shrink)
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. (...) The starting point of the scenario is the capability that had our pre-human ancestors for an elementary identification with conspecifics. We consider that the evolution of that capability has led to self-consciousness when identifications with conspecifics brought our ancestors to represent their own entity as existing, like conspecifics were represented. But the same identification process also took place with endangered and dying conspecifics. And this has produced a huge anxiety increase, source of important mental sufferings that our ancestors had to limit. Our hypothesis is that different modes of anxiety limitation have led to the pan-homo split. On one side the chimpanzee lineage would have limited an unbearable mental suffering by stopping the development of identifications with conspecifics, and by this also stopping a possible evolution toward self-consciousness. Such anxiety limitation process has led to today chimpanzees which possess a very limited consciousness of themselves. On the other side, our human lineage would have successfully developed anxiety limitation tools like caring, pleasure, anticipation, communication and imitation. With these tools accelerating the evolution of our lineage toward our human mind. The proposed pan/homo split process complements an existing evolutionary scenario for self-consciousness that has positioned anxiety management as a key contributor to the build-up of our human minds. Such overall perspective makes anxiety management a major source to many of our motivations and mental states, much more than assumed so far. Continuations are proposed for a better understanding about our modes of anxiety limitation (including evil behaviors), and also to introduce a possible evolutionary nature of phenomenal consciousness. (shrink)
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, (...) обращаться к её разработчикам с предложениями по усовершенствованию, или производить самопрограммирование без их участия. (shrink)
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 (...) methods could be adapted for feminist purposes and what role new technologies might play in addressing concerns raised by feminist epistemologists and theorists about algorithmic systems. In particular, we highlight distributed and federated learning as providing partial solutions to the power-oriented concerns that have stymied efforts to make machine learning systems more representative and pluralist. (shrink)
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.
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 (...) is distinctively human and what can be inferred from AI systems. The present article investigates to what extent recent developments in AI provide new elements to the debate and clarify the process of belief acquisition, consolidation, and recalibration. The article analyses and debates current issues and topics of investigation such as: different models to understand belief, the exploration of belief in an automated reasoning environment, the case of religious beliefs, and future directions of research. (shrink)
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 (...) problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes. (shrink)
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 (...) of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor) . (shrink)
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 (...) based on the idiosyncratic notions of symbol systems and the representational abilities they give rise to, in particular with respect to knowledge. While focusing on the period 1956-1982, this review cites both earlier and later literature and it attempts to make visible their potential relevance to today's greatest unifying AI challenge, to wit, the design of wholly autonomous artificial agents (a.k.a. robots) that are not only rational and ethical, but also self-conscious. (shrink)
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 (...) stable philosophical reference on Human Nature. Indeed, we find in the works of Aquinas some of the most productive elements that could form a base to our deeper understanding of, and possibly even solutions to some of the most perplexing questions raised in our times by the existence of AI. (shrink)
Talk of ‘robustness’ remains vague, despite the fact that it is clearly an important parameter in evaluating models in general and game-theoretic results in particular. Here we want to make it a bit less vague by offering a graphic measure for a particular kind of robustness— ‘matrix robustness’— using a three dimensional display of the universe of 2 x 2 game theory. In a display of this form, familiar games such as the Prisoner’s Dilemma, Stag Hunt, Chicken and Deadlock appear (...) as volumes, making comparison easy regarding the extent of different game-theoretic effects. We illustrate such a comparison in robustness between the triumph of Tit for Tat in a spatialized environment (Grim 1995, Grim, Mar, and St. Denis 1998) and a spatialized modeling of the Contact Hypothesis regarding prejudice reduction (Grim, et. al 2005a, 2005b). The geometrical representation of relative robustness also offers a possibility for links between geometrical theorems and results regarding robustness in game theory. (shrink)
CAT4 is proposed as a general method for representing information, enabling a powerful programming method for large-scale information systems. It enables generalised machine learning, software automation and novel AI capabilities. This is Part 3 of a five-part introduction. The focus here is on explaining the semantic model for CAT4. Points in CAT4 graphs represent facts. We introduce all the formal (data) elements used in the classic semantic model: sense or intension (1st and 2nd joins), reference (3rd join), functions (4th join), (...) time and truth (logical fields), and symbolic content (name/value fields). Concepts are introduced through examples alternating with theoretical discussion. Some concepts are assumed from Part 1 and 2, but key ideas are re-introduced. The purpose is to explain the CAT4 interpretation, and why the data structure and CAT4 axioms have been chosen: to make the semantic model consistent and complete. We start with methods to translate information from database tables into graph DBs and into CAT4. We conclude with a method for translating natural language into CAT4. We conclude with a comparison of the system with an advanced semantic logic, the hyper-intensional logic TIL, which also aims to translate NL into a logical calculus. The CAT4 Natural Language Translator is discussed in further detail in Part 4, when we introduce functions more formally. Part 5 discusses software design considerations. (shrink)
Semantics based on representational theories of mind has met challenges recently. Traditional accounts consider meaning as an entity with semantic properties, i.e. a mental object that denotes or represents a real-world object. The paper discusses ways of constructing meaning without representations, as shown in Rapaport’s syntactic semantics and Rosenberg’s eliminative theory of mind and language.
The Frame Problem is the problem of how one can design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation assumes (...) that mental processes are identical to certain kinds of computational processes, and so solving the Frame Problem is a matter of finding a computational architecture that can effectively represent relations of semantic relevance. Instead, we must take seriously the possibility that the way in which intelligent agents use information is inherently different. Whereas intelligent agents are plausibly genuinely causally sensitive to semantic properties as such (to what they perceive, desire, believe intend, etc.), computational systems can only be causally sensitive to the formal features that represent these properties. Indeed, it is this very substitution of formal generalizations for genuinely semantic ones that is responsible for the way current AI systems are brittle, inflexible, and highly specialized. What we need is a more sophisticated way of investigating the relationship between computational information processing and genuinely semantic information use, so that these two senses of using information are not conflated, but instead the question of how they are related to one another can be studied directly. I apply the generative methodology I have developed elsewhere for cognitive science and AI research (Miracchi, 2017, 2019a) to show how the Frame Problem can be appropriately updated. (shrink)
This paper aims to provide a mathematically tractable background against which to model both modal cognitivism and modal expressivism. I argue that epistemic modal algebras, endowed with a hyperintensional, topic-sensitive epistemic two-dimensional truthmaker semantics, comprise a materially adequate fragment of the language of thought. I demonstrate, then, how modal expressivism can be regimented by modal coalgebraic automata, to which the above epistemic modal algebras are categorically dual. I examine five methods for modeling the dynamics of conceptual engineering for intensions and (...) hyperintensions. I develop a novel topic-sensitive truthmaker semantics for dynamic epistemic logic, and develop a novel dynamic epistemic two-dimensional hyperintensional semantics. I examine then the virtues unique to the modal expressivist approach here proffered in the setting of the foundations of mathematics, by contrast to competing approaches based upon both the inferentialist approach to concept-individuation and the codification of speech acts via intensional semantics. (shrink)
Although there have been efforts to integrate Semantic Web technologies and artificial agents related AI research approaches, they remain relatively isolated from each other. Herein, we introduce a new ontology framework designed to support the knowledge representation of artificial agents’ actions within the context of the actions of other autonomous agents and inspired by standard cognitive architectures. The framework consists of four parts: 1) an event ontology for information pertaining to actions and events; 2) an epistemic ontology containing facts about (...) knowledge, beliefs, perceptions and communication; 3) an ontology concerning future intentions, desires, and aversions; and, finally, 4) a deontic ontology for modeling obligations and prohibitions which limit agents’ actions. The architecture of the ontology framework is inspired by deontic cognitive event calculus as well as epistemic and deontic logic. We also describe a case study in which the proposed DCEO ontology supports autonomous vehicle navigation. (shrink)
The electric activities of cortical pyramidal neurons are supported by structurally stable, morphologically complex axo-dendritic trees. Anatomical differences between axons and dendrites in regard to their length or caliber reflect the underlying functional specializations, for input or output of neural information, respectively. For a proper assessment of the computational capacity of pyramidal neurons, we have analyzed an extensive dataset of three-dimensional digital reconstructions from the NeuroMorphoOrg database, and quantified basic dendritic or axonal morphometric measures in different regions and layers of (...) the mouse, rat or human cerebral cortex. Physical estimates of the total number and type of ions involved in neuronal electric spiking based on the obtained morphometric data, combined with energetics of neurotransmitter release and signaling fueled by glucose consumed by the active brain, support highly efficient cerebral computation performed at the thermodynamically allowed Landauer limit for implementation of irreversible logical operations. Individual proton tunneling events in voltage-sensing S4 protein alpha-helices of Na+, K+ or Ca2+ ion channels are ideally suited to serve as single Landauer elementary logical operations that are then amplified by selective ionic currents traversing the open channel pores. This miniaturization of computational gating allows the execution of over 1.2 zetta logical operations per second in the human cerebral cortex without combusting the brain by the released heat. (shrink)
The development of ontologies for the purposes of data curation is an important element in modern-day data and information sciences. Unfortunately, much of the work on these applied ontologies is associated with a relativist or conceptualist point of view, according to which ontologies represent (for example) the concepts in the minds of human beings. The paper describes a series of problems with such views, and defends an alternative realist interpretation.
Abstract: Given an embodied evolutionary context, the (conscious) organism creates phenomenality and establishes a first-person point of view with its own agency, through intentional relations made by its own acts of fiat, in the same way that human observers create meaning in language.
What we have learnt in the last six or seven decades about virtual machinery, as a result of a great deal of science and technology, enables us to offer Darwin a new defence against critics who argued that only physical form, not mental capabilities and consciousness could be products of evolution by natural selection. The defence compares the mental phenomena mentioned by Darwin’s opponents with contents of virtual machinery in computing systems. Objects, states, events, and processes in virtual machinery which (...) we have only recently learnt how to design and build, and could not even have been thought about in Darwin’s time, can interact with the physical machinery in which they are implemented, without being identical with their physical implementation, nor mere aggregates of physical structures and processes. The existence of various kinds of virtual machinery (including both “platform” virtual machines that can host other virtual machines, e.g. operating systems, and “application” virtual machines, e.g. spelling checkers, and computer games) depends on complex webs of causal connections involving hardware and software structures, events and processes, where the specification of such causal webs requires concepts that cannot be defined in terms of concepts of the physical sciences. That indefinability, plus the possibility of various kinds of self-monitoring within virtual machinery, seems to explain some of the allegedly mysterious and irreducible features of consciousness that motivated Darwin’s critics and also more recent philosophers criticising AI. There are consequences for philosophy, psychology, neuroscience and robotics. (shrink)
This book concerns the foundations of epistemic modality and hyperintensionality and their applications to the philosophy of mathematics. I examine the nature of epistemic modality, when the modal operator is interpreted as concerning both apriority and conceivability, as well as states of knowledge and belief. The book demonstrates how epistemic modality and hyperintensionality relate to the computational theory of mind; metaphysical modality and hyperintensionality; the types of mathematical modality and hyperintensionality; to the epistemic status of large cardinal axioms, undecidable propositions, (...) and abstraction principles in the philosophy of mathematics; to the modal and hyperintensional profiles of the logic of rational intuition; and to the types of intention, when the latter is interpreted as a hyperintensional mental state. Chapter 2 argues for a novel type of expressivism based on the duality between the categories of coalgebras and algebras, and argues that the duality permits of the reconciliation between modal cognitivism and modal expressivism. I also develop a novel topic-sensitive truthmaker semantics for dynamic epistemic logic, and develop a novel dynamic epistemic two-dimensional hyperintensional semantics. Chapter 3 provides an abstraction principle for epistemic (hyper-)intensions. Chapter 4 advances a topic-sensitive two-dimensional truthmaker semantics, and provides three novel interpretations of the framework along with the epistemic and metasemantic. Chapter 5 applies the fixed points of the modal μ-calculus in order to account for the iteration of epistemic states in a single agent, by contrast to availing of modal axiom 4 (i.e. the KK principle). The fixed point operators in the modal μ-calculus are rendered hyperintensional, which yields the first hyperintensional construal of the modal μ-calculus in the literature and the first application of the calculus to the iteration of epistemic states in a single agent instead of the common knowledge of a group of agents. Chapter 6 advances a solution to the Julius Caesar problem based on Fine's `criterial' identity conditions which incorporate conditions on essentiality and grounding. Chapter 7 provides a ground-theoretic regimentation of the proposals in the metaphysics of consciousness and examines its bearing on the two-dimensional conceivability argument against physicalism. The topic-sensitive epistemic two-dimensional truthmaker semantics developed in chapter 4 is availed of in order for epistemic states to be a guide to metaphysical states in the hyperintensional setting. -/- Chapters 8-12 provide cases demonstrating how the two-dimensional hyperintensions of hyperintensional, i.e. topic-sensitive epistemic two-dimensional truthmaker, semantics, solve the access problem in the epistemology of mathematics. Chapter 8 examines the interaction between my hyperintensional semantics and the axioms of epistemic set theory, large cardinal axioms, the Epistemic Church-Turing Thesis, the modal axioms governing the modal profile of Ω-logic, Orey sentences such as the Generalized Continuum Hypothesis, and absolute decidability. These results yield inter alia the first hyperintensional Epistemic Church-Turing Thesis and hyperintensional epistemic set theories in the literature. Chapter 9 examines the modal and hyperintensional commitments of abstractionism, in particular necessitism, and epistemic hyperintensionality, epistemic utility theory, and the epistemology of abstraction. Chapter 10 examines the philosophical significance of hyperintensional Ω-logic in set theory. Chapter 11 provides a modal logic for rational intuition and provides a hyperintensional semantics. Chapter 12 avails of modal coalgebras to interpret the defining properties of indefinite extensibility, and avails of hyperintensional epistemic two-dimensional semantics in order to account for the interaction between the interpretational and objective modalities and truthmakers thereof. This yields the first hyperintensional category theory in the literature. I invent a new mathematical trick in which first order structures are treated as categories, and Vopenka's principle can be satisfied because of the elementary embeddings between the categories and generate Vopenka cardinals while bypassing the category of Set in category theory. Chapter 13 examines modal responses to the alethic paradoxes. Chapter 14 examines, finally, the modal and hyperintensional semantics for the different types of intention and the relation of the latter to evidential decision theory. (shrink)
1. Was ist ein Homunkulus-Fehlschluß? 2. Analyse des Mentalen und Naturalisierung der Intentionalität 3. Homunkulismus in Theorien der visuellen Wahrnehmung 4. Homunkulismus und Repräsentationalismus 5. Der homunkulare Funktionalismus 6. Philosophische Sinnkritik und empirische Wissenschaft Literatur .
The philosophy of AI has seen some changes, in particular: 1) AI moves away from cognitive science, and 2) the long term risks of AI now appear to be a worthy concern. In this context, the classical central concerns – such as the relation of cognition and computation, embodiment, intelligence & rationality, and information – will regain urgency.
The theory that all processes in the universe are computational is attractive in its promise to provide an understandable theory of everything. I want to suggest here that this pancomputationalism is not sufficiently clear on which problem it is trying to solve, and how. I propose two interpretations of pancomputationalism as a theory: I) the world is a computer and II) the world can be described as a computer. The first implies a thesis of supervenience of the physical over computation (...) and is thus reduced ad absurdum. The second is underdetermined by the world, and thus equally unsuccessful as theory. Finally, I suggest that pancomputationalism as metaphor can be useful. – At the Paderborn workshop in 2008, this paper was presented as a commentary to the relevant paper by Gordana Dodig-Crnkovic " Info-Computationalism and Philosophical Aspects of Research in Information Sciences". (shrink)
Report for "The Reasoner" on the conference "Philosophy and Theory of Artificial Intelligence", 3 & 4 October 2011, Thessaloniki, Anatolia College/ACT, http://www.pt-ai.org. --- Organization: Vincent C. Müller, Professor of Philosophy at ACT & James Martin Fellow, Oxford http://www.sophia.de --- Sponsors: EUCogII, Oxford-FutureTech, AAAI, ACM-SIGART, IACAP, ECCAI.
I see four symbol grounding problems: 1) How can a purely computational mind acquire meaningful symbols? 2) How can we get a computational robot to show the right linguistic behavior? These two are misleading. I suggest an 'easy' and a 'hard' problem: 3) How can we explain and re-produce the behavioral ability and function of meaning in artificial computational agents?4) How does physics give rise to meaning?
[Müller, Vincent C. (ed.), (2016), Fundamental issues of artificial intelligence (Synthese Library, 377; Berlin: Springer). 570 pp.] -- This volume offers a look at the fundamental issues of present and future AI, especially from cognitive science, computer science, neuroscience and philosophy. This work examines the conditions for artificial intelligence, how these relate to the conditions for intelligence in humans and other natural agents, as well as ethical and societal problems that artificial intelligence raises or will raise. The key issues this (...) volume investigates include the relation of AI and cognitive science, ethics of AI and robotics, brain emulation and simulation, hybrid systems and cyborgs, intelligence and intelligence testing, interactive systems, multi-agent systems, and superintelligence. Based on the 2nd conference on “Theory and Philosophy of Artificial Intelligence” held in Oxford, the volume includes prominent researchers within the field from around the world. (shrink)
Context has emerged as a central concept in a variety of contemporary approaches to reasoning. The conference at which the papers in this volume were presented, CONTEXT 2001, was the third international, interdisciplinary conference on the topic of context, and was held in Dundee, Scotland on July 27-30, 2001.
Since the 1990’s, social sciences are living their computational turn. This paper aims to clarify the epistemological meaning of this turn. To do this, we have to discriminate between different epistemic functions of computation among the diverse uses of computers for modeling and simulating in the social sciences. Because of the introduction of a new – and often more user-friendly – way of formalizing and computing, the question of realism of formalisms and of proof value of computational treatments reemerges. Facing (...) the spreading of computational simulations in all disciplines, some enthusiastic observers are claiming that we are entering a new era of unity for social sciences. Finally, the article shows that the conceptual and epistemological distinctions presented in the first sections lead to a more mitigated position: the transdisciplinary computational turn is a great one, but it is of a methodological nature. (shrink)
[Müller, Vincent C. (ed.), (2013), Philosophy and theory of artificial intelligence (SAPERE, 5; Berlin: Springer). 429 pp. ] --- Can we make machines that think and act like humans or other natural intelligent agents? The answer to this question depends on how we see ourselves and how we see the machines in question. Classical AI and cognitive science had claimed that cognition is computation, and can thus be reproduced on other computing machines, possibly surpassing the abilities of human intelligence. This (...) consensus has now come under threat and the agenda for the philosophy and theory of AI must be set anew, re-defining the relation between AI and Cognitive Science. We can re-claim the original vision of general AI from the technical AI disciplines; we can reject classical cognitive science and replace it with a new theory (e.g. embodied); or we can try to find new ways to approach AI, for example from neuroscience or from systems theory. To do this, we must go back to the basic questions on computing, cognition and ethics for AI. The 30 papers in this volume provide cutting-edge work from leading researchers that define where we stand and where we should go from here. (shrink)
Robustness has long been recognized as an important parameter for evaluating game-theoretic results, but talk of ‘robustness’ generally remains vague. What we offer here is a graphic measure for a particular kind of robustness (‘matrix robustness’), using a three-dimensional display of the universe of 2 × 2 game theory. In such a measure specific games appear as specific volumes (Prisoner’s Dilemma, Stag Hunt, etc.), allowing a graphic image of the extent of particular game-theoretic effects in terms of those games. The (...) measure also allows for an easy comparison between different effects in terms of matrix robustness. Here we use the measure to compare the robustness of Tit for Tat’s well-known success in spatialized games (Axelrod, R. (1984). The evolution of cooperation . New York: Basic Books; Grim, P. et al. (1998). The philosophical computer: Exploratory essays in philosophical computer modeling . Cambridge, Mass: MIT Press) with the robustness of a recent game-theoretic model of the contact hypothesis regarding prejudice reduction (Grim et al. 2005. Public Affairs Quarterly, 19 , 95–125). (shrink)
The field of machine perception is based on standard informational and computational approaches to perception. But naturalistic informational theories are widely regarded as being inadequate, while purely syntactic computational approaches give no account of perceptual content. Thus there is a significant need for a novel, purely naturalistic perceptual theory not based on informational or computational concepts, which could provide a new paradigm for mechanistic perception. Now specifically evolutionary naturalistic approaches to perception have been—perhaps surprisingly—almost completely neglected for this purpose. Arguably (...) perceptual mechanisms enhance evolutionary fitness by facilitating sensorily mediated causal interactions between an organism Z and items X in its environment. A ‘reflexive’ theory of perception of this kind is outlined, according to which an organism Z perceives an item X just in case X causes a sensory organ zi of Z to cause Z to acquire a disposition toward the very same item X that caused the perception. The rest of the paper shows how an intuitively plausible account of mechanistic perception can be developed and defended in terms of the reflexive theory. Also, a compatibilist option is provided for those who wish to preserve a distinct informational concept of perception. (shrink)
This paper outlines a quantitative theory of strongly semantic information (TSSI) based on truth-values rather than probability distributions. The main hypothesis supported in the paper is that the classic quantitative theory of weakly semantic information (TWSI), based on probability distributions, assumes that truth-values supervene on factual semantic information, yet this principle is too weak and generates a well-known semantic paradox, whereas TSSI, according to which factual semantic information encapsulates truth, can avoid the paradox and is more in line with the (...) standard conception of what generally counts as semantic information. After a brief introduction, section two outlines the semantic paradox implied by TWSI, analysing it in terms of an initial conflict between two requisites of a quantitative theory of semantic information. In section three, three criteria of semantic information equivalence are used to provide a taxonomy of quantitative approaches to semantic information and introduce TSSI. In section four, some further desiderata that should be fulfilled by a quantitative TSSI are explained. From section five to section seven, TSSI is developed on the basis of a calculus of truth-values and semantic discrepancy with respect to a given situation. In section eight, it is shown how TSSI succeeds in solving the paradox. Section nine summarises the main results of the paper and indicates some future developments. (shrink)
We discuss at some length evidence from the cognitive science suggesting that the representations of objects based on spatiotemporal information and featural information retrieved bottomup from a visual scene precede representations of objects that include conceptual information. We argue that a distinction can be drawn between representations with conceptual and nonconceptual content. The distinction is based on perceptual mechanisms that retrieve information in conceptually unmediated ways. The representational contents of the states induced by these mechanisms that are available to a (...) type of awareness called phenomenal awareness constitute the phenomenal content of experience. The phenomenal content of perception contains the existence of objects as separate things that persist in time and time, spatiotemporal information, and information regarding relative spatial relations, motion, surface properties, shape, size, orientation, color, and their functional properties. (shrink)
Насколько дефекты культурного окружения людей сбивают их с толку, поскольку находятся в противоречии с их биологическими, т.е. жизненно важными потребностями, я хотел бы проиллюстрировать на примере сна, приснившегося мне в ночь с 13 на 14 апреля 2023 года.
Subjectology (from Latin subject and logos) studies the internal states of living and nonliving systems capable of symbolic representation of any real content, i.e. to display sensory perceptible information and to transform it into world pictures, the elements of which are symbols whose meaning or sense is determined in the context of the symbolic representation. Субъектология (от лат. subject и logos) изучает внутренние состояния живых и неживых систем, способных к символической репрезентации какого–либо реального содержания, т.е. к отображению чувственно воспринимаемой информации (...) и её преобразованию в картины мира, элементами которых являются символы, значение или смысл которых определяется в контексте символического отображения. (shrink)
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