This dissertation argues that cognition is a kind of natural agency. Natural agency is the capacity that certain systems have to act in accordance with their own norms. Natural agents are systems that bias their repertoires in response to affordances in the pursuit of their goals. Cognition is a special mode of this general phenomenon. Cognitive systems are agents that have the additional capacity to actively take their worlds to be certain ways, regardless of whether the world is really that (...) way. In this way, cognitive systems are desituated. Desituatedness is the root of specifically cognitive capacities for representation and abstraction. There are two main reasons why this view needs defending. First, natural agency is typically viewed as incompatible with natural science because it is committed to a teleological mode of explanation. Second, cognition is typically held to be categorically distinct from natural agency. This dissertation argues against both of these views. It argues against the incompatibility of agency and natural science by demonstrating that systems biology, general systems theory, and sciences that deal with complex systems have typically underappreciated conceptual and theoretical resources for grounding agency in the causal structure of the world. These conceptual resources do not, however, reduce agency to systems theory because the normativity inherent in agency demands descriptive resources beyond those of even the most sophisticated systems theory. It argues against the categorical difference between natural agency and cognition by pointing out that separating cognition from a richer web of situated, ecologically embedded relations between the agent and the world generates the frame problem, which is an insuperable obstacle to making cognition that is sufficiently responsive to the complexity of the world. Rooting cognition in natural agency is a more robust empirical bet for theorizing cognition and artificial intelligence. (shrink)

The objective of this work is to explore the notion of “action” and “agency” in artificial intelligence (AI). It employs a metaphysical notion of action and agency as an epistemological tool in the critique of the notion of “action” and “agency” in artificial intelligence. Hence, both a metaphysical and cognitive analysis is employed in the investigation of the quiddity and nature of action and agency per se, and how they are, by extension employed in the language and science of artificial (...) intelligence. The advent of the science of artificial intelligence and cognitive science, and the technological applications of artificial intelligence in the production of agents such as driverless cars and expert systems, have raised the question of moral, ethical and/or legal responsibility in AI agents. This has re-emphasized the importance of the philosophical discourse on the notions of action and agency, which in contemporary intellectual discourse are now perceived to be phenomena within the epistemic competence of the natural sciences. This paper argues that AI systems do not and cannot possess free agency and autonomy, thus cannot be morally and ethically responsible, hence, it recommends a socio-political response to the question of responsibility in AI. It is then the duty of individual nations, or the global community to define and enact policies on who shoulders the responsibility of actions executed by AIs. (shrink)

This thesis investigates how recurrent face-to-face social interactions engender relatively invariant patterns of being together that cause those who instantiate them to act in ways that support their reproduction. Existing accounts within both cognitive science and sociology offer important insights into the consideration of patterns of being together. However, given their explanatory strategies, they struggle to integrate both ‘social’ and ‘individual’ levels of explanation. Herein a compatibilist account is developed, intended as a ‘third way’ that obviates the limitations of existing (...) accounts. This compatibilist account — by integrating insights from across disciplines and theoretical frameworks — develops a philosophical vocabulary with which to maintain explanatory consistency when articulating patterns of being together and moving between individual and social levels of explanation. It relies heavily on an extension of the enactive notion of autonomous habits to the social domain, redescribing patterns of being together as habituated participatory sense-making frames. Participatory sense-making frames result from processes of coenhabiting, i.e. processes of ongoing social habit making implicit in the dynamics of recurrent social interactions. Such processes are one primary means by which we produce and reproduce the relatively stable forms that characterise our shared worlds. These habituated frames embed much of the normativity of social life, and can serve our felicitous coordinations therein, allowing us to feel well situated, particularly in contexts within which we have some history of interacting. However, when they are not well aligned, they lead to tensions that result in either the production of novel frames or breakdowns in social interaction. The account developed has implications for many domains of human action, from psychotherapy to epistemology, and from critical studies to the development of political and ecological praxes. (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)

Investigamos o problema da continuidade entre (1) cognições básicas e (2) complexas, especificamente em relação à linguagem. Nossa hipótese é a de que visões contemporâneas da linguagem não inviabilizam necessariamente a abordagem bottom-up ― abordagens tipicamente enativistas. Primeiro apresentamos a posição de Daniel Hutto e Erik Myin (2013, 2017), representantes do Radically Enactive Cognition (REC), a qual assume o desafio da continuidade e identifica na linguagem o critério para uma distinção de tipo entre (1) e (2). Em seguida, estabelecemos a (...) posição Wittgensteiniana defendida por Moyal-Sharrock (2019), na qual a linguagem seria apenas mais uma habilidade de possível desenvolvimento para (1) e (2). Haveria, portanto, somente uma distinção de grau , não de tipo. Por mais que a avaliação de Moyal-Sharrock seja interessante no sentido de que parece evitar um ceticismo de continuidade (identificado por ela no REC), também encontramos problemas em sua visão. (shrink)

This paper investigates the prospects of Rodney Brooks’ proposal for AI without representation. It turns out that the supposedly characteristic features of “new AI” (embodiment, situatedness, absence of reasoning, and absence of representation) are all present in conventional systems: “New AI” is just like old AI. Brooks proposal boils down to the architectural rejection of central control in intelligent agents—Which, however, turns out to be crucial. Some of more recent cognitive science suggests that we might do well to dispose of (...) the image of intelligent agents as central representation processors. If this paradigm shift is achieved, Brooks’ proposal for cognition without representation appears promising for full-blown intelligent agents—Though not for conscious agents. (shrink)

Artificial intelligence (AI) is a digital technology that will be of major importance for the development of humanity in the near future. AI has raised fundamental questions about what we should do with such systems, what the systems themselves should do, what risks they involve and how we can control these. - After the background to the field (1), this article introduces the main debates (2), first on ethical issues that arise with AI systems as objects, i.e. tools made and (...) used by humans; here, the main sections are privacy (2.1), manipulation (2.2), opacity (2.3), bias (2.4), autonomy & responsibility (2.6) and the singularity (2.7). Then we look at AI systems as subjects, i.e. when ethics is for the AI systems themselves in machine ethics (2.8.) and artificial moral agency (2.9). Finally we look at future developments and the concept of AI (3). For each section within these themes, we provide a general explanation of the ethical issues, we outline existing positions and arguments, then we analyse how this plays out with current technologies and finally what policy conse-quences may be drawn. (shrink)

In this reply to James H. Fetzer’s “Minds and Machines: Limits to Simulations of Thought and Action”, I argue that computationalism should not be the view that (human) cognition is computation, but that it should be the view that cognition (simpliciter) is computable. It follows that computationalism can be true even if (human) cognition is not the result of computations in the brain. I also argue that, if semiotic systems are systems that interpret signs, then both humans and computers are (...) semiotic systems. Finally, I suggest that minds can be considered as virtual machines implemented in certain semiotic systems, primarily the brain, but also AI computers. In doing so, I take issue with Fetzer’s arguments to the contrary. (shrink)

The acquisition of a new skill usually proceeds through five stages, from novice to expert, with a sixth stage of mastery available for highly motivated performers. In this chapter, we re-state the six stages of the Dreyfus Skill Model, paying new attention to the transitions and interrelations between them. While discussing the fifth stage, expertise, we unpack the claim that, “when things are proceeding normally, experts don’t solve problems and don’t make decisions; they do what normally works” (Dreyfus & Dreyfus, (...) 1988, pp. 30 – 31). This leads us to offer an account of the “perspectival deliberation” that arises for experts and masters and that is distinct from the calculative deliberation characteristic of the lower stages of skillfulness. (shrink)

In search of our highest capacities, cognitive scientists aim to explain things like mathematics, language, and planning. But are these really our most sophisticated forms of knowing? In this paper, I point to a different pinnacle of cognition. Our most sophisticated human knowing, I think, lies in how we engage with each other, in our relating. Cognitive science and philosophy of mind have largely ignored the ways of knowing at play here. At the same time, the emphasis on discrete, rational (...) knowing to the detriment of engaged, human knowing pervades societal practices and institutions, often with harmful effects on people and their relations. There are many reasons why we need a new, engaged—or even engaging—epistemology of human knowing. The enactive theory of participatory sense-making takes steps towards this, but it needs deepening. Kym Maclaren’s idea of letting be invites such a deepening. Characterizing knowing as a relationship of letting be provides a nuanced way to deal with the tensions between the knower’s being and the being of the known, as they meet in the process of knowing-and-being-known. This meeting of knower and known is not easy to understand. However, there is a mode of relating in which we know it well, and that is: in loving relationships. I propose to look at human knowing through the lens of loving. We then see that both knowing and loving are existential, dialectic ways in which concrete and particular beings engage with each other. (shrink)

In this article we analyze the strengths and weaknesses of mindreading versus embodied cognition approaches to emotion understanding. In the first part of the article we argue that mindreading explanations of how we understand the emotions of others (TT, ST or hybrid) face a version of the frame problem, i.e. the problem of how to limit the scope of the information that is relevant to mindreading. Also, we show that embodied cognition explanations are able to by-pass this problem because they (...) provide a characterization of social understanding as being essentially situated. However, embodied cognition explanations seem to be limited in scope insofar as they do not target the more sophisticated forms of emotion understanding that have traditionally been the main focus of mindreading explanations. In the second part of the article we discuss Goldie’s account of emotion understanding as a possible way to complement embodied cognition approaches without re-introducing the frame problem. We offer two suggestions that might further the integration of Goldie’s account of emotion understanding within the framework of embodied cognition. (shrink)

It has been commonplace in epistemology since its inception to idealize away from computational resource constraints, i.e., from the constraints of time and memory. One thought is that a kind of ideal rationality can be specified that ignores the constraints imposed by limited time and memory, and that actual cognitive performance can be seen as an interaction between the norms of ideal rationality and the practicalities of time and memory limitations. But a cornerstone of naturalistic epistemology is that normative assessment (...) is constrained by capacities: you cannot require someone to do something they cannot or, as it is usually put, ought implies can. This much we take to be uncontroversial. We argue that differences in architectures, goals and resources imply substantial differences in capacity, and that some of these differences are ineliminable. It follows that some differences in goals and architectural and computational resources matter at the normative level: they constrain what principles of normative epistemology can be used to describe and prescribe their behavior. As a result, we can expect there to be important epistemic differences between the way brains, individuals, and science work. (shrink)

In their enthusiasm for programming, computational linguists have tended to lose sight of what humansdo. They have conceived of conversations as independent of sound and the bodies that produce it. Thus, implicit in their simulations is the assumption that the text is the essence of talk. In fact, unlike electronic mail, conversations are acoustic events. During everyday talk, human understanding depends both on the words spoken and on fine interpersonal vocal coordination. When utterances are analysed into sequences of word-based forms, (...) however, these prosodic aspects of language disappear. Therefore, to investigate the possibility that machines might talk, we propose acommunion game that includes this interpersonal patterning. Humans and machines would talk together and, based on recordings of them, a panel would appraise the relevant merit of each machine's simulation by how true to life it sounded. Unlike Turing's imitation game, the communion game overtly focuses attention, not on intelligence, but on language. It is designed to facilitate the development of social groups of adaptive robots that exploit complex acoustic signals in real time. We consider how the development of such machines might be approached. (shrink)

Social science research on the anthropomorphisms of computers and robots has been devoted to studying intellectual anthropomorphism, emotional anthropomorphism, bodily anthropomorphism, and the limits of computer and robot anthropomorphism. Although these represent important patterns for studying the anthropomorphisms of computers and robots, there are other important patterns. The limitation of anthropomorphism is one of these patterns. The limitation of anthropomorphism is a discursive practice which places limits on anthropomorphism. Discursive practices are interactional and practical activities for making sense of who (...) and what we encounter. In this article, I analyze the limitation of anthropomorphism as another important activity to investigate. Drawing on an ethnographic study of a robotics research group, I show the importance of the limitation of anthropomorphism by documenting how it contributes to research group identity. In the final section, I describe some implications for future studies of the anthropomorphisms of computers and robots. (shrink)

The development of technology raises an array of ethical issues related to work. Many of these ethical issues are old issues surfacing under new guises. Technology has not changed the issues, but technology makes the issues' analysis and application more complex. This paper identifies several new ethical issues raised by technological change: computer crime, an over-reliance on computer controlled systems, bio-technical developments, degradation of quality-of-life at work and new categories of work-related injuries. These issues are discussed in the context of (...) management responsibilities and responses. The paper offers a five-step process for the effective management of these ethical issues: Be aware of the issues, develop an ethical framework, be consistent, communicate clearly, stay alert for future developments. (shrink)

In his PhD thesis (1938) Turing introduced what he described as 'a new kind of machine'. He called these 'O-machines'. The present paper employs Turing's concept against a number of currently fashionable positions in the philosophy of mind.

A survey of the field of hypercomputation, including discussion of a variety of objections.

Over the past ten years, we have worked in a collaboration between educators and computer scientists at the University of Illinois to imagine futures for education in the context of what is loosely called “artificial intelligence.” Unhappy with the first generation of digital learning environments, our agenda has been to design alternatives and research their implementation. Our starting point has been to ask, what is the nature of machine intelligence, and what are its limits and potentials in education? This paper (...) offers some tentative answers, first conceptually, and then practically in an overview of the results of a number of experimental implementations documented in greater detail elsewhere. Our key finding is that artificial intelligence—in the context of the practices of electronic computing developing over the past three quarters of a century—will never in any sense “take over” the role of teacher, because how it works and what it does are so profoundly different from human intelligence. However, within the limits that we describe in this paper, it offers the potential to transform education in ways that—counterintuitively perhaps—make education more human, not less. (shrink)

Technologies not only change “external reality” but also change our internal consciousness, shaping the way we experience the world. As the reality of intelligent environments is upon us—ushered along with the age of ubiquitous computing—we must be careful that the ideology these technologies embody is not blindly incorporated into the environment. As disciplines, engineering and computer science make implicit assumptions about the world that conflict with traditional modes of cultural production. For example, space is commonly understood to be the void (...) left behind when no objects are present. Unfortunately, once we see space in this way, we are unable to understand the role it plays in our everyday experience. In order to make computationally enhanced spaces that are meaningful at the level of the everyday, we must exorcise the notion of intelligence from their design and replace it with life. Henri Lefebvre’s discussions of the space of everyday life provide a framework to help conceive this transition. (shrink)

Erroneously attributing propositional attitudes (desires, beliefs...) to computational artefacts has become internationally commonplace in the public arena, especially amongst the new generation of non-initiated users. Technology for rendering machines “user-friendly” is often inspired by interpersonal human communication. This calls forth designers to conceptualise a major component of human intelligence: the sense ofcommunicability, and its logical consequences. The inherentincommunicability of machines subsequently causes a shift in design strategy. Though cataloguing components of bouts between person and machine with Speech Act Theory has (...) been popular, I will endeavour to present thesine qua non for their insertion into a larger unit of discourse — their societal embodiment. I shall argue that the so-called “intelligence” of the artificial should to be seenas a purposeful act that is socially generated, because it comes of Man,for Man. Designership will provide the forum for evolving user requirements and interface renewal. (shrink)

Between formal propositional knowledge and embodied skill lies ‘interactional expertise’—the ability to converse expertly about a practical skill or expertise, but without being able to practice it, learned through linguistic socialisation among the practitioners. Interactional expertise is exhibited by sociologists of scientific knowledge, by scientists themselves and by a large range of other actors. Attention is drawn to the distinction between the social and the individual embodiment theses: a language does depend on the form of the bodies of its members (...) but an individual within that community can learn the language without the body. The idea has significance for our understanding of colour-blindness, deafness and other abilities and disabilities. They say that love's a word. (shrink)

Is cybernetics good, bad, or indifferent? SherryTurkle enlists deconstructive theory to celebrate thecomputer age as the embodiment of difference. Nolonger just a theory, one can now live a virtual life. Within a differential but ontologically detachedfield of signifiers, one can construct and reconstructegos and environments from the bottom up andendlessly. Lucas Introna, in contrast, enlists theethical philosophy of Emmanuel Levinas to condemn thesame computer age for increasing the distance betweenflesh and blood people. Mediating the face-to-facerelation between real people, allowing and (...) encouragingcommunication at a distance, information technologywould alienate individuals from the social immediacyproductive of moral obligations and responsibilities. In this paper I argue against both of thesepositions, and for similar reasons. Turkle''scelebration and Introna''s condemnation of informationtechnology both depend, so I will argue, on the samemistaken meta-interpretation of it. Like Introna,however, but to achieve a different end, I will enlistLevinas''s ethical philosophy to make this case. (shrink)

Contemporary ICTs such as speaking machines and computer games tend to create illusions. Is this ethically problematic? Is it deception? And what kind of “reality” do we presuppose when we talk about illusion in this context? Inspired by work on similarities between ICT design and the art of magic and illusion, responding to literature on deception in robot ethics and related fields, and briefly considering the issue in the context of the history of machines, this paper discusses these questions through (...) the lens of stage magic and illusionism, with the aim of reframing the very question of deception. It investigates if we can take a more positive or at least morally neutral view of magic, illusion, and performance, while still being able to understand and criticize the relevant phenomena, and if we can describe and evaluate these phenomena without recourse to the term “deception” at all. This leads the paper into a discussion about metaphysics and into taking a relational and narrative turn. Replying to Tognazzini, the paper identifies and analyses two metaphysical positions: a narrative and performative non-dualist position is articulated in response to what is taken to be a dualist, in particular Platonic, approach to “deception” phenomena. The latter is critically discussed and replaced by a performative and relational approach which avoids a distant “view from nowhere” metaphysics and brings us back to the phenomena and experience in the performance relation. The paper also reflects on the ethical and political implications of the two positions: for the responsibility of ICT designers and users, which are seen as co-responsible magicians or co-performers, and for the responsibility of those who influence the social structures that shape who has power to deceive or to let others perform. (shrink)

Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding of the data. The space of possible recodings is, however, infinitely large – it is the space of applicable Turing machines. As a result, mappings that pivot on such attenuated regularities cannot, in general, be found by brute-force search. The class of problems that present such mappings we call the class of “type-2 problems.” Type-1 problems, by (...) contrast, present tractable problems of search insofar as the relevant regularities can be found by sampling the input data as originally coded. Type-2 problems, we suggest, present neither rare nor pathological cases. They are rife in biologically realistic settings and in domains ranging from simple animat behaviors to language acquisition. Not only are such problems rife – they are standardly solved! This presents a puzzle. How, given the statistical intractability of these type-2 cases, does nature turn the trick? One answer, which we do not pursue, is to suppose that evolution gifts us with exactly the right set of recoding biases so as to reduce specific type-2 problems to type-1 mappings. Such a heavy-duty nativism is no doubt sometimes plausible. But we believe there are other, more general mechanisms also at work. Such mechanisms provide general strategies for managing problems of type-2 complexity. Several such mechanisms are investigated. At the heart of each is a fundamental ploy – namely, the maximal exploitation of states of representation already achieved by prior, simpler learning so as to reduce the amount of subsequent computational search. Such exploitation both characterizes and helps make unitary sense of a diverse range of mechanisms. These include simple incremental learning, modular connectionism, and the developmental hypothesis of “representational redescription”. In addition, the most distinctive features of human cognition – language and culture – may themselves be viewed as adaptations enabling this representation/computation trade-off to be pursued on an even grander scale. (shrink)

In this article, we will attempt to understand Sellars’ puzzling notion of ‘adequate picturing’ and its relation to the Sellarsian ‘conceptual order’ through Spinoza’s intuitive knowledge. First, it will be suggested that there are important structural similarities between Sellarsian ‘adequate picturing’ and Spinoza’s intuitive knowledge which can illuminate some ‘dark’ and not so well understood features of Sellarsian picturing. However, there remain some deep differences between Sellars’ and Spinoza’s philosophy, especially with regard to their notion of ‘adequacy’ and the sense (...) in which an idea can be said to ‘correspond’ with its object. As a consequence of those differences it turns out that, from a Sellarsian viewpoint, Spinoza’s notion of intuitive knowledge is a version of the myth of the Given. However, Sellars’ own proposed way out of the Myth has problematic consequences by Sellars’ own lights: His sharp distinction between the ‘space of reasons’ level and the level of picturing makes the Sellarsian vision of the ‘stereoscopic fusion’ of those levels virtually unintelligible. Finally, it will be argued that a non-standard interpretation of Spinoza’s intuitive knowledge -proposed in Baltas -, combined with a Sellars-inspired bifurcated understanding of Dreyfusian expert knowledge, can provide the means 1) for constructing a notion of intuitive knowledge that evades the myth of the Given, and, 2) for making full sense of the claim that the ‘space of reasons’ dimension of understanding the world can in principle be fused with the ‘picturing’ dimension and provide us with a single complex coherent experience of reality and our place in it. (shrink)

This paper argues that even though massive technological unemployment will likely be one of the results of automation, we will not need to institute mass-scale redistribution of wealth to deal with its consequences. Instead, reasons are given for cautious optimism about the standards of living the newly unemployed workers may expect in the fully-automated future. It is not claimed that these predictions will certainly bear out. Rather, they are no less likely to come to fruition than the predictions of those (...) authors who predict that massive technological unemployment will lead to the suffering of the masses on such a scale that significant redistributive policies will have to be instituted to alleviate it. Additionally, the paper challenges the idea that the existence of a moral obligation to help the victims of massive unemployment justifies the coercive taking of anyone else’s property. (shrink)

In a previous article we have shown that Kuhn's theory of concepts is independently supported by recent research in cognitive psychology. In this paper we propose a cognitive re-reading of Kuhn's cyclical model of scientific revolutions: all of the important features of the model may now be seen as consequences of a more fundamental account of the nature of concepts and their dynamics. We begin by examining incommensurability, the central theme of Kuhn's theory of scientific revolutions, according to two different (...) cognitive models of concept representation. We provide new support for Kuhn 's mature views that incommensurability can be caused by changes in only a few concepts, that even incommensurable conceptual systems can be rationally compared, and that scientific change of the most radical sort—the type labeled revolutionary in earlier studies—does not have to occur holistically and abruptly, but can be achieved by a historically more plausible accumulation of smaller changes. We go on to suggest that the parallel accounts of concepts found in Kuhn and in cognitive science lead to a new understanding of the nature of normal science, of the transition from normal science to crisis, and of scientific revolutions. The same account enables us to understand how scientific communities split to create groups supporting new paradigms, and to resolve various outstanding problems. In particular, we can identify the kind of change needed to create a revolution rather precisely. This new analysis also suggests reasons for the unidirectionality of scientific change. (shrink)

The term “Contemplative sciences” refers to an interdisciplinary approach to mind that aims at a better understanding of alternative states of consciousness, like those obtained trough deep concentration and meditation, mindfulness and other “superior” or “spiritual” mental states. There is, however, a key discipline missing: artificial intelligence. AI has forgotten its original aims to create intelligent machines that could help us to understand better what intelligence is and is more worried about pragmatical stuff, so almost nobody in the field seems (...) to be interested to join this new effort of contemplative science. In this paper, I would like to accomplish the following: (1) To give a brief description of the field of “contemplative sciences;” (2) To argue why AI should actively join this new paradigm on the study of the mind; and (3) To set up a research program on artificial wisdom: that is to design computational systems that can model at least some relevant aspects of human wisdom. (shrink)

The artificial intelligence models with machine learning that exhibit the best predictive accuracy, and therefore, the most powerful ones, are, paradoxically, those with the most opaque black-box architectures. At the same time, the unstoppable computerization of advanced industrial societies demands the use of these machines in a growing number of domains. The conjunction of both phenomena gives rise to a control problem on AI that in this paper we analyze by dividing the issue into two. First, we carry out an (...) epistemological examination of the AI’s opacity in light of the latest techniques to remedy it. And second, we evaluate the rationality of delegating tasks in opaque agents. (shrink)

Since its introduction, Hubert Dreyfus’ account of expertise has been a topic of debate and continues to be. This article focuses on one particular critique: Selinger and Crease :245–279, 2002) argument that Dreyfus wrongfully denies expertise to those whose expertise is a matter of propositional knowledge, which they call an ‘expert in x’. This article sets out to defend Dreyfus against the ‘expert in x’ by showing that Selinger and Crease’s use of Gilbert Ryle’s distinction between know-how and know-that as (...) the foundation of their argument is vulnerable to Ryle’s regress argument and arguments from Radical Anti-Intellectualism. (shrink)

Nowadays, philosophers and scientists tend to agree that, even though human and artificial intelligence work quite differently, they can still illuminate aspects of each other, and knowledge in one domain can inspire progress in the other. For instance, the notion of “artificial” or “synthetic” phenomenology has been gaining some traction in recent AI research. In this paper, we ask the question: what (if anything) is the use of thinking about phenomenology in the context of AI, and in particular machine learning? (...) We will isolate one sense of “phenomenology”, namely the sense in which it is commonly understood within analytic philosophy of perception. Then, we will give examples of projects within sensory substitution and restoration science that rely heavily on machine learning and to which, according to us, phenomenology in the sense specified makes a relevant contribution. Finally, we will shed some light on what this contribution looks like and why it is important. (shrink)

This paper analyzes ethical aspects of the new paradigm of Ambient Intelligence, which is a combination of Ubiquitous Computing and Intelligent User Interfaces (IUI’s). After an introduction to the approach, two key ethical dimensions will be analyzed: freedom and privacy. It is argued that Ambient Intelligence, though often designed to enhance freedom and control, has the potential to limit freedom and autonomy as well. Ambient Intelligence also harbors great privacy risks, and these are explored as well.

Amidst the progress being made in the various (sub-)disciplines of the behavioural and brain sciences a somewhat neglected subject is the problem of how everything fits into one world and, derivatively, how the relation between different levels of discourse should be understood and to what extent different levels, domains, approaches, or disciplines are autonomous or dependent. In this paper I critically review the most recent proposals to specify the nature of interdiscourse relations, focusing on the concept of supervenience. Ideally supervenience (...) is a relation between different discourses which has all the advantages of reduction, but without its disadvantages. I apply the more abstract considerations to two concrete cases: schizophrenia and colour. Usually an interlevel or interdiscourse relation is seen as asymmetrical: the overlaying discourse depends on the underlying discourse (and not vice versa), where the out- or un-spoken assumption is that the ultimate underlying discourse is physical. Instead I argue that scientific categories referred to in interdiscourse relations are, ultimately, dependent on common sense categories and common sense normative criteria. It is the manifest categories and common sense ideas about what is reasonable and what is right that determine the relevant categorisations at the deeper, underlying levels. I suggest that the implications of this are not merely methodological or epistemological. (shrink)

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)

Formal tools are attributed central roles in organizing work within many modern workplaces. How should one comprehend the power of these tools? Taking the medical record as an example, this article builds on recent calls to overcome the dichotomy between the formal and the informal and proposes an understanding of the generative power of such tools that does not attribute mythical capacities to either tool or human work. To do so, it is important to look both at the history offormal (...) tools and to their real-time use in concrete work practices. These steps toward a sociology of the formal are crucial for a more comprehensive understanding and evaluation offormal systems in practice. (shrink)

An alternative to objectifying approaches to understanding Post-traumatic Stress Disorder grounded in hermeneutic phenomenology is presented. Nurses who provided care for soldiers injured in the Iraq and Afghanistan wars, and sixty-seven wounded male servicemen in the rehabilitation phase of their recovery were interviewed. PTSD is the one major psychiatric diagnosis where social causation is established, yet PTSD is predominantly viewed in terms of the usual neuro-physiological causal models with traumatic social events viewed as pathogens with dose related effects. Biologic models (...) of causation are applied reductively to both predisposing personal vulnerabilities and strengths that prevent PTSD, such as resiliency. However, framing PTSD as an objective disease state separates it from narrative historical details of the trauma. Personal stories and cultural meanings of the traumatic events are seen as epiphenomenal, unrelated to the understanding of, and ultimately, the therapeutic treatment of PTSD. Most wounded service members described classic symptoms of PTSD: flashbacks, insomnia, anxiety etc. All experienced disturbance in their sense of time and place. Rather than see the occurrence of these symptoms as decontextualized mechanistic reverberations of war, we consider how these symptoms meaningfully reflect actual war experiences and sense of displacement experienced by service members. (shrink)

This paper proposes a methodological redirection of the philosophical debate on artificial moral agency in view of increasingly pressing practical needs due to technological development. This “normative approach” suggests abandoning theoretical discussions about what conditions may hold for moral agency and to what extent these may be met by artificial entities such as AI systems and robots. Instead, the debate should focus on how and to what extent such entities should be included in human practices normally assuming moral agency and (...) responsibility of participants. The proposal is backed up by an analysis of the AMA debate, which is found to be overly caught in the opposition between so-called standard and functionalist conceptions of moral agency, conceptually confused and practically inert. Additionally, we outline some main themes of research in need of attention in light of the suggested normative approach to AMA. (shrink)

In CyberPhilosophy: The Intersection of Philosophy and Computing, edited by James H. Moor and Terrell Ward Bynum (Oxford, UK: Blackwell, 2002), 66-77. Also in Metaphilosophy 33.1/2 (2002): 70-82.

There is a deluge of AI-assisted decision-making systems, where our data serve as proxy to our actions, suggested by AI. The closer we investigate our data (raw input, or their learned representations, or the suggested actions), we begin to discover “bugs”. Outside of their test, controlled environments, AI systems may encounter situations investigated primarily by those in other disciplines, but experts in those fields are typically excluded from the design process and are only invited to attest to the ethical features (...) of the resulting system or to comment on demonstrations of intelligence and aspects of craftmanship after the fact. This communicative impasse must be overcome. Our idea is that philosophical and engineering considerations interact and can be fruitfully combined in the AI design process from the very beginning. We embody this idea in the role of a philosopher engineer. We discuss the role of philosopher engineers in the three main design stages of an AI system: deployment management (what is the system’s intended use, in what environment?); objective setting (what should the system be trained to do, and how?); and training (what model should be used, and why?). We then exemplify the need for philosopher engineers with an illustrative example, investigating how the future decisions of an AI-based hiring system can be fairer than those contained in the biased input data on which it is trained; and we briefly sketch the kind of interdisciplinary education that we envision will help to bring about better AI. (shrink)

This article addresses the question of the mechanisms of the emergence of structure and meaning in the biological and physical sciences. It proceeds from an examination of the concept of intentionality and proposes a model of intentional behavior on the basis of results of computer simulations of structural and functional self-organization. Current attempts to endow intuitive aspects of meaningful complexity with operational content are analyzed and the metaphor of DNA as a computer program (the `genetic program') is critically examined in (...) relation to an alternative metaphor of DNA as data. It is argued that relatively simple networks of boolean automata can classify and recognize patterns of binary strings on the basis of non-programmed, self-generated criteria, but lack a capacity for self-observation and interpretation. To overcome this problem it is necessary to clarify the relationships between the goals and underlying mechanisms of a process and between a system and its environment. It will be shown that memory devices that record the histories of interactions are essential for models of conscious and unconscious intentional behavior and that the possibility of infinitely sophisticated - and therefore unprogrammable - machines cannot be avoided. It will be argued that the notion of infinite sophistication allows the ideas of self-organization and physical determinism to be reconciled. These models will be used to suggest how the voluntary aspect of decision-making in general can emerge out of functional self-organizing processes. The conclusion will introduce the notion of `underdetermination' of theories, which imposes an intrinsic limitation on models of complex natural systems - a limitation that, at the same time, may be precisely what makes possible mutual understanding and intersubjectivity. (shrink)

Information modeling (also known as conceptual modeling or semantic data modeling) may be characterized as the formulation of a model in which information aspects of objective and subjective reality are presented (the application), independent of datasets and processes by which they may be realized (the system).A methodology for information modeling should incorporate a number of concepts which have appeared in the literature, but should also be formulated in terms of constructs which are understandable to and expressible by the system user (...) as well as the system developer. This is particularly desirable in connection with certain intimate relationships, such as being the same as or being a part of. (shrink)

The Phenomenological Mind, by Shaun Gallagher and Dan Zahavi, is part of a recent initiative to show that phenomenology, classically conceived as the tradition inaugurated by Edmund Husserl and not as mere introspection, contributes something important to cognitive science. (For other examples, see “References” below.) Phenomenology, of course, has been a part of cognitive science for a long time. It implicitly informs the works of Andy Clark (e.g. 1997) and John Haugeland (e.g. 1998), and Hubert Dreyfus explicitly uses it (e.g. (...) 1992). But where the former use phenomenology in the background as broad context and Dreyfus uses it primarily (though not exclusively) as a critique of conventional AI, Gallagher and Zahavi wish to indicate a positive and constructive place for it within cognitive science. They do not recommend that we simply accept pronouncements of thinkers like Husserl, Heidegger, Sartre and Merleau‐Ponty and apply them to questions of cognition, but that we use revised forms of phenomenology to illuminate dimensions of cognitive experience that are missing in current research. The book is presented as an “introduction to philosophy of mind and cognitive science” written from a phenomenological perspective. It seeks to justify the use of phenomenology in cognitive science by showing what kinds of questions it asks and answers, the variety of uses to which it has recently been put and the fruitfulness of some of its findings. The catalog of topics, for the most part, matches other introductions to the philosophy of mind, such as questions of method, consciousness, perception, intentionality, embodiment, action, agency and other minds. One issue presented here that is not generally dealt with in existing philosophy of mind and cognitive science texts is temporality, a mainstay of the continental tradition. After an introductory chapter that places phenomenology in the context of other approaches, the book lays out the main tenets of phenomenological method. Here, one encounters expected components of phenomenology: the epoché (described below), phenomenological reduction, eidetic variation, and so on. This traditional fare is soon followed by some potential surprises, namely, attempts to “naturalize” phenomenology, a few attempts to formalize it, and the emergence of ‘neurophenomenology’. Each of these is a bit surprising because Husserl was a vocal critic of naturalism, seeing transcendental phenomenology as an alternative to the empirical study of consciousness. He was also skeptical about the possibilities of mathematizing phenomenology. Gallagher and Zahavi acknowledge these points, but since they are not repeating history or undertaking exegesis, strict adherence to canonical phenomenology is not required. Naturalizing phenomenology means recognizing that “the phenomena it studies are part of nature and are therefore also open to empirical investigation” (p.. (shrink)

We argue that abduction does not work in isolation from other inference mechanisms and illustrate this through an inference scheme designed to evaluate multiple hypotheses. We use game theory to relate the abductive system to actions that produce new information. To enable evaluation of the implications of this approach we have implemented the procedures used to calculate the impact of new information in a computer model. Experiments with this model display a number of features of collective belief-revision leading to consensus-formation, (...) such as the influence of bias and prejudice. The scheme of inferential calculations invokes a Peircian concept of ‘belief’ as the propensity to choose a particular course of action. (shrink)