What is creativity? One new idea may be creative, whereas another is merely new: What's the difference? And how is creativity possible? These questions about human creativity can be answered, at least in outline, using computational concepts. There are two broad types of creativity, improbabilist and impossibilist. Improbabilist creativity involves novel combinations of familiar ideas. A deeper type involves METCS: the mapping, exploration, and transformation of conceptual spaces. It is impossibilist, in that ideas may be generated which – with respect (...) to the particular conceptual space concerned – could not have been generated before. The more clearly conceptual spaces can be defined, the better we can identify creative ideas. Defining conceptual spaces is done by musicologists, literary critics, and historians of art and science. Humanist studies, rich in intuitive subtleties, can be complemented by the comparative rigour of a computational approach. Computational modelling can help to define a space, and to show how it may be mapped, explored, and transformed. Impossibilist creativity can be thought of in “classical” Al terms, whereas connectionism illuminates improbabilist creativity. Most Al models of creativity can only explore spaces, not transform them, because they have no self-reflexive maps enabling them to change their own rules. A few, however, can do so. A scientific understanding of creativity does not destroy our wonder at it, nor does it make creative ideas predictable. Demystification does not imply dehumanization. (shrink)

We can readily identify goal-directed systems and distinguish them from non-goal-directed systems. A woodpecker hunting for grubs is the first, a pendulum returning to rest is the second. But what is it to be a goal-directed system? Perhaps the dominant answer to this question, inspired by systems theories such as cybernetics, is that goal-directed systems are distinguished by their tendency to seek, aim at, or maintain some more-or-less easily identifiable goal. Cybernetics and the like would hold that physical systems subject (...) only to physical laws can exhibit such behavior. If sound, this systems approach to teleology would unify a diverse range of goal-directed phenomena and neatly side-step many traditional bogey-men of teleology, such as anthropomorphism and future causation. Goal-directed phenomena would be a normal feature of the natural causal world that could be described in purely descriptive and quantitative terms, and receive ordinary causal explanations. Thus, the systems approach promises to provide a naturalistic-cum-descriptive account of teleology suitable for use in naturalistic accounts of other phenomena, including the intentionality of mental states and even self-consciousness. (shrink)

Reflection on the fine-grained information required for visual guidance of action has suggested that visual content is non-conceptual. I argue that in a common type of visually guided action, namely the use of manipulable artefacts, vision has conceptual content. Specifically, I show that these actions require visual attention and that concepts are involved in directing attention. In acting with artefacts, there is a way of doing it right as determined by the artefact’s conventional use. Attention must reflect our understanding of (...) the function and appropriate ways to use these artefacts, understanding that requires possession of the relevant concept. As a result, we attend to the artefact’s relevant functional properties. In these cases, attention is structured by concepts. This discussion has a bearing on the dual visual stream hypothesis. While it is often held that the two visual streams are functionally independent, the argument of this essay is that the constraints on attention suggest a functional interaction between them. (shrink)

The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions between research on Bayesian networks in machine learning and research on causality and probability in the philosophy of science.

En los inicios del siglo XXI está desarrollándose una e-ciencia, una ciencia electrónica y altamente computarizada que exige un replanteamiento sobre la epistemología científica. A través del ejemplo de la Bioinformática y las Biotecnologías, el autor muestra algunas características de esta nueva e-ciencia e indica algunos de los problemas con los que deben enfrentarse los filósofos de la ciencia contemporáneos. Right at the beginning of the 21st century an e-Science is emerging, a highly computerized electronic science which demands a new (...) way of understanding scientific epistemology. Through Bioinformatics and Biotechnologies, the author analyzes some of the characteristics of this new e-Science and shows several problems which contemporary philosophers of science must face. (shrink)

The project of a 'naive physics' has been the subject of attention in recent years above all in the artificial intelligence field, in connection with work on common-sense reasoning, perceptual representation and robotics. The idea of a theory of the common-sense world is however much older than this, having its roots not least in the work of phenomenologists and Gestalt psychologists such as K hler, Husserl, Schapp and Gibson. This paper seeks to show how contemporary naive physicists can profit from (...) a knowledge of these historical roots of their discipline, which are shown to imply above alla critique of the set-theory-based models of reality typically presupposed by contemporary work in common-sense ontology [1]. (shrink)

The peculiarity of the relationship between philosophy and Artificial Intelligence (AI) has been evidenced since the advent of AI. This paper aims to put the basis of an extended and well founded philosophy of AI: it delineates a multi-layered general framework to which different contributions in the field may be traced back. The core point is to underline how in the same scenario both the role of philosophy on AI and role of AI on philosophy must be considered. Moreover, this (...) framework is revised and extended in the light of the consideration of a type of multiagent system devoted to afford the issue of scientific discovery both from a conceptual and from a practical point of view. (shrink)

In this paper we review some problems with traditional approaches for acquiring and representing knowledge in the context of developing user interfaces. Methodological implications for knowledge engineering and for human-computer interaction are studied. It turns out that in order to achieve the goal of developing human-oriented (in contrast to technology-oriented) human-computer interfaces developers have to develop sound knowledge of the structure and the representational dynamics of the cognitive system which is interacting with the computer.We show that in a first step (...) it is necessary to study and investigate the different levels and forms of representation that are involved in the interaction processes between computers and human cognitive systems. Only if designers have achieved some understanding about these representational mechanisms, user interfaces enabling individual experiences and skill development can be designed. In this paper we review mechanisms and processes for knowledge representation on a conceptual, epistemological, and methodologieal level, and sketch some ways out of the identified dilemmas for cognitive modeling in the domain of human-computer interaction. (shrink)

I aim to show that, contrary to standard deflationary or eliminativist theories of the self, we can argue from the phenomenology of pre-reflective self-awareness for the thesis that subjects of experience are substances. The phenomenological datum of subjectivity points to a specific metaphysical structure of our experience, that is, towards the substance view rather than the bundle or the minimal self view. Drawing on modern philosophical accounts of pre-reflective self-awareness, mineness and (self-) acquaintance, I will argue that a subject is (...) aware of being the one individual who has many experiences and that it is revealed to the subject that it is the bearer of experiences and their unifier. The subject is present in pre-reflective awareness and known as the subject of experiences, and even this minimal self-awareness gives us reason to favour the substance view. Thus, one can demonstrate how the debates on the phenomenology of pre-reflective self-awareness and the metaphysics of selfhood intersect. (shrink)

John Searle’s Chinese Room Argument purports to demonstrate that syntax is not sufficient for semantics, and, hence, because computation cannot yield understanding, the computational theory of mind, which equates the mind to an information processing system based on formal computations, fails. In this paper, we use the CRA, and the debate that emerged from it, to develop a philosophical critique of recent advances in robotics and neuroscience. We describe results from a body of work that contributes to blurring the divide (...) between biological and artificial systems; so-called animats, autonomous robots that are controlled by biological neural tissue and what may be described as remote-controlled rodents, living animals endowed with augmented abilities provided by external controllers. We argue that, even though at first sight, these chimeric systems may seem to escape the CRA, on closer analysis, they do not. We conclude by discussing the role of the body–brain dynamics in the processes that give rise to genuine understanding of the world, in line with recent proposals from enactive cognitive science. (shrink)

This paper represents such an amateur approach; hence any comments backed up by professional erudition will be highly appreciated. Let me start from an attempt to sketch a relationship between professionals’ and amateurs’ contributions. The latter may be compared with the letters to the Editor of a journal, written by perceptive readers, while professionals contribute to the very content of the journal in question. Owing to such letters, the Editor and his professional staff can become more aware of the responses (...) of educated public to the journal’s output. (shrink)

The philosophy of information (PI) is a new area of research with its own field of investigation and methodology. This article, based on the Herbert A. Simon Lecture of Computing and Philosophy I gave at Carnegie Mellon University in 2001, analyses the eighteen principal open problems in PI. Section 1 introduces the analysis by outlining Herbert Simon's approach to PI. Section 2 discusses some methodological considerations about what counts as a good philosophical problem. The discussion centers on Hilbert's famous analysis (...) of the central problems in mathematics. The rest of the article is devoted to the eighteen problems. These are organized into five sections: problems in the analysis of the concept of information, in semantics, in the study of intelligence, in the relation between information and nature, and in the investigation of values. (shrink)

Disertační práce se zabývá problematikou připisování myšlení jiným entitám, a to pomocí imitační hry navržené v roce 1950 britským filosofem Alanem Turingem. Jeho kritérium, známé v dějinách filosofie jako Turingův test, je podrobeno detailní analýze. Práce popisuje nejen původní námitky samotného Turinga, ale především pozdější diskuse v druhé polovině 20. století. Největší pozornost je věnována těmto kritikám: Lucasova matematická námitka využívající Gödelovu větu o neúplnosti, Searlův argument čínského pokoje konstatující nedostatečnost syntaxe pro sémantiku, Blockův návrh na použití brutální síly pro (...) řešení imitační hry, Frenchova teorie subkognitivních informací a Michieho skepticismus ohledně možnosti umělého vědomí. Závěr práce zachycuje současný stav recepce Turingova testu a představuje pokusy o jeho praktickou realizaci, například v každoroční soutěži o Loebnerovu cenu. Autor práce zastává názor, že ani po více než šedesáti letech od uveřejnění Turingova paradigmatického eseje stále neexistují žádné vážné důvody pro zamítnutí jeho tvrzení. Tradiční komputační funkcionalismus možná není ideální teorií vysvětlující činnost myslí a jako slibnější se může jevit vývoj v neurálních vědách, ale Turingův test je přesto užitečným a snad i jediným nástrojem pro detekci inteligence u lidmi vytvořených strojů. (shrink)

This paper deals with the rationalist assumptions behind researches of artificial intelligence (AI) on the basis of Hubert Dreyfus’s critique. Dreyfus is a leading American philosopher known for his rigorous critique on the underlying assumptions of the field of artificial intelligence. Artificial intelligence specialists, especially those whose view is commonly dubbed as “classical AI,” assume that creating a thinking machine like the human brain is not a too far away project because they believe that human intelligence works on the basis (...) of formalized rules of logic. In contradistinction to classical AI specialists, Dreyfus contends that it is impossible to create intelligent computer programs analogous to the human brain because the workings of human intelligence is entirely different from that of computing machines. For Dreyfus, the human mind functions intuitively and not formally. Following Dreyfus, this paper aims to pinpointing the major flaws classical AI suffers from. The author of this paper believes that pinpointing these flaws would inform inquiries on and about artificial intelligence. Over and beyond this, this paper contributes something indisputably original. It strongly argues that classical AI research programs have, though inadvertently, falsified an entire epistemological enterprise of the rationalists not in theory as philosophers do but in practice. When AI workers were trying hard in order to produce a machine that can think like human minds, they have in a way been testing—and testing it up to the last point—the rationalist assumption that the workings of the human mind depend on logical rules. Result: No computers actually function like the human mind. Reason: the human mind does not depend on the formal or logical rules ascribed to computers. Thus, symbolic AI research has falsified the rationalist assumption that ‘the human mind reaches certainty by functioning formally’ by virtue of its failure to create a thinking machine. (shrink)

The development of computers as ‘mind tools’ has generated intriguing and provocative views about their potential human-like qualities. In this paper an attempt is made to explore the ‘real’ nature of computers by an examination of three widely different perspective, (1) the common-sense view of computers as tools; (2) the provocative view of computers as persons; and (3) the challenging view of computers as texts. In the course of the discussion an extended critique of the use of anthropomorphic terms in (...) relation to machines is conducted and the fundamental differences between human persons and machines is reasserted. (shrink)

Philosophers have often argued that ascriptions of content are appropriate only to the personal level states of folk psychology. Against this, this paper defends the view that the familiar propositional attitudes and states defined over them are part of a larger set of cognitive proceses that do not make constitutive reference to concept possession. It does this by showing that states with nonconceptual content exist both in perceptual experience and in subpersonal information-processing systems. What makes these states content-involving is their (...) satisfaction of certain basic conditions deriving from a general account of representation-driven behaviour that is neutral on the question of concept possession. It is also argued that creatures can be in states with nonconceptual content even though they possess no conceptual abilities at all. (shrink)

The presence of intelligence and rationality in Artificial Intelligence and the Internet requires a new context of analysis in which Herbert Simon’s approach to the sciences of the artificial is surpassed in order to grasp the role of information in our contemporary setting. This new framework requires taking into account some relevant aspects. In the historical endeavor of building up AI and the Internet, minds and machines have interacted over the years and in many ways through the interrelation between scientific (...) creativity and technological innovation. Philosophically, minds and machines can have epistemological, methodological and ontological differences, which are based on the distinct configuration of human intelligence and artificial intelligence. Their comparison with rationality and its various forms is particularly relevant. Scientifically, AI and the Internet belong to the sciences of the artificial, because they work on designs that search for specific aims, following selected processes in order to achieve expected results. Technologically, AI and the Internet require the support of information and communication technologies. These have an instrumental role regarding the existence of AI and the Internet. Also ICT shape their diverse forms of configuration over the years.Within this framework, this paper offers a new context of analysis that goes beyond Simon’s and follows four main steps: the interaction between scientific creativity and technological innovation as the philosophico-methodological setting for Artificial Intelligence and the Internet; artificial intelligence and human intelligence as epistemological basis for machines and minds, where the differences between artificial intelligence and human intelligence are made explicit and the analysis of minds and machines is made from the perspective of rationality ; intention and its difference with design of machine learning are considered to distinguish human intelligence from artificial intelligence; and the internal and external aspects of artificial designs in contemporary society are considered through the perspective of rationality, which leads to the transition from intelligence to rationality in the Internet as well as to the historicity of information. (shrink)

The project of a naive physics has been the subject of attention in recent years above all in the artificial intelligence field, in connection with work on common-sense reasoning, perceptual representation and robotics. The idea of a theory of the common-sense world is however much older than this, having its roots not least in the work of phenomenologists and Gestalt psychologists such as Kohler, Husserl, Schapp and Gibson. This paper seeks to show how contemporary naive physicists can profit from a (...) knowledge of these historical roots of their discipline, which are shown to imply above all a critique of the set-theory-based models of reality typically presupposed by contemporary work in common-sense ontology. (shrink)

What does it mean to know how to do something? This book develops a comprehensive account of know-how, a crucial epistemic goal for all who care about getting things right, not only with respect to the facts, but also with respect to practice. It proposes a novel interpretation of the seminal work of Gilbert Ryle, according to which know-how is a competence, a complex ability to do well in an activity in virtue of guidance by an understanding of what it (...) takes to do so. This idea is developed into a full-fledged account, Rylean responsibilism, which understands know-how in terms of the normative guidance and responsible control of one's acts. Within the complex current debate about know-how, this view occupies a middle ground position between the intellectualist claim that know-how just is propositional or objectual knowledge and the anti-intellectualist claim that know-how just is ability. In genuine know-how, practical ability and guiding intellect are both necessary, but essentially intertwined. (shrink)

This piece is a commentary on a precis of Maggie Boden's book "The creative mind" published in Behavioral and Brain Sciences.

Notes on Contributors • Preface • Christopher Peacocke, Introduction: The Issues and their Further Development I OBJECTIVE THOUGHT • John Campbell, Objects and Objectivity Commentaries • Bill Brewer, Thoughts about Objects, Places and Times • John O'Keefe, Cognitive Maps, Time and Causality II OBJECTIVITY AND THE UNITY OF CONSCIOUSNESS • Susan Hurley, Unity and Objectivity Commentaries • Anthony Marcel, What is Relevant to the Unity of Consciousness? • Michael Lockwood, Issues of Unity and Objectivity III UNDERSTANDING THE MENTAL:THEORY OR SIMULATION (...) • MARTIN DAVIES, The Mental Simulation Debate Commentaries • Jane Heal, Simulation vs. Theory Theory: What is at Issue? • Josef Perner, The Necessity and Impossibility of Simulation. (shrink)

Die zeitgenössischen Philosophen haben zwar der Sprache, die wir verwenden, um die Welt der alltäglichen Erfahrung zu beschreiben oder um uns in dieser Welt zurechtzufinden, große Aufmerksamkeit geschenkt, sie haben sich jedoch – von einigen Ausnahmen abgesehen – geweigert, diese Welt selbst als passendes Objekt theoretischer Betrachtungen anzusehen. Im folgenden werde ich versuchen zu zeigen, wie es möglich ist, die Common-Sense-Welt als ontologisch eigenständiges Untersuchungsobjekt zu verstehen. Gleichzeitig werde ich mich bemühen, deutlich zu zeigen, wie eine solch eigenständige Behandlung uns (...) möglicherweise hilft, die Strukturen sowohl der physikalischen Welt als auch der menschlichen Erkenntnis philosophisch besser zu verstehen. (shrink)

Eliminative functionalism is the view that mental attributes, of humans and other machines, consist ultimately in behavioural abilities or dispositions. Hence, ‘Strong AI’: if a machine consistently acts as if it were fully conscious, then conscious it is. From these assumptions, optimistic futurists have derived a variety of remarkable visions of our ‘post-human’ future; from widely-recognised ‘robot rights’ to ‘mind uploading’, immortality, ‘apotheosis’ and beyond. It is argued here, however, that eliminative functionalism is false; for at least on our present (...) knowledge, the subjectively qualitative characteristics of conscious experience are neither deducible from, nor logically required to generate, the performance of any sort of overtly ‘intelligent’, or indeed, characteristically human behaviour. Thus, a machine could easily be designed to report awareness of phenomenal qualities, without necessarily possessing them; and Alan Turing’s ‘Imitation Game’ test for artificial thinking is unable to determine whether or not a machine is sentient. An alternative test is proposed, in which the machine is asked phenomenological questions under conditions designed to detect any form of cheating—whilst also, potentially revealing evidence for the occurrence of genuine qualitative experience. (shrink)

Purpose: Ernst von Glasersfeld’s question concerning the relationship between scientific/ rational knowledge and the domain of wisdom and how these forms of knowledge come about is the starting point. This article aims at developing an epistemological as well as methodological framework that is capable of explaining how profound change can be brought about in various contexts, such as in individual cultivation, in organizations, in processes of radical innovation, etc. This framework is based on the triple-loop learning strategy and the U-theory (...) approach, which opens up a perspective on how the domains of scientific/rational knowledge, constructivism, and wisdom could grow together more closely. Design/Structure: This article develops a strategy which is referred to as “tripleloop learning,” which is not only the basis for processes of profound change, but also brings about a new dimension in the field of learning and knowledge dynamics: the existential realm and the domain of wisdom. A concrete approach that puts into practice the tripleloop learning strategy is presented. The final section shows, how these concepts can be interpreted in the context of the constructivist approach and how they might offer some extensions to this paradigm. Findings: The process of learning and change has to be extended to a domain that concerns existential issues as well as questions of wisdom. Profound change can only happen if these domains are taken into consideration. The tripleloop learning strategy offers a model that fulfills this criterion. It is an “epistemo-existential strategy” for profound change on various levels. Conclusions: The (cognitive) processes and attitudes of receptivity, suspension, redirecting, openness, deep knowing, as well as “profound change/innovation from the interior” turn out to be core concepts in this process. They are compatible with constructivist concepts. Von Glasersfeld’s concept of functional fitness is carried to an extreme in the suggested approach of profound change and finds an extension in the existential domain. Key words: Double-loop learning, individual cultivation, (radical) innovation, knowledge creation, knowledge society, personality development, presencing, profound change, triple-loop learning, U-theory, wisdom. (shrink)

This chapter reviews some interesting research trends in the philosophy of information (PI). First, PI is defined; then, a series of open problems in PI on which philosophers are currently working is considered. The conclusion highlights the innovative character of this new area of research.

In this paper I attempt to sketch a preliminary framework for understanding the cognitive basis of the engagement of the mind with the material world. I advance the hypothesis that contrary to some of our most deeply-entrenched assumptions the relationship between the world and human cognition is not one of abstract representation or some other form of action at a distance but one of ontological inseparability. That is, what we have traditionally construed as an active or passive but always clearly (...) separated external stimulus for setting an internal cognitive mechanism into motion, may be after all a continuous part of the machinery itself; at least, ex hypothesi. (shrink)