Under what circumstances if ever ought we to grant that Artificial Intelligences (AI) are persons? The question of whether AI could have the high degree of moral status that is attributed to human persons has received little attention. What little work there is employs western conceptions of personhood, while non-western approaches are neglected. In this article, I discuss African conceptions of personhood and their implications for the possibility of AI persons. I focus on an African account of personhood that is (...) prima facie inimical to the idea that AI could ever be ‘persons’ in the sense typically attributed to humans. I argue that despite its apparent anthropocentrism, this African account could admit AI as persons. (shrink)

In the last decades of the 20th century we have seen media theories and cognitive sciences grow, mature and reach their pinnacles by analysing, each from their own disciplinary perspective, two of the same core phenomena: that of media as the environment, transmitter and creator of stimuli, and that of embodied human mind as the stimuli receiver, interpreter, experiencer, and also how both are affected by each other. Even though treating a range of very similar problems and coming to similar (...) conclusions, this still has not brought these two disciplines closer together or resulted in their interdisciplinary approach. They did coalesce in regards to traditional media such as film, but more points of connection are needed for untangling interactive and immersive media environments and their effects on human cognition, action, and perception. With the rise of VR and VR-like systems, especially as they start to evolve into the Metaverse as their main platform of interconnectivity, the tissue of the body becomes almost physically intertwined with that of the virtual surrounding it inhabits through immersion. Simultaneously, the interest in these disciplines arises anew, and especially the need to use their concepts in an interdisciplinary way. This paper?s main interest is to bring these disciplines together in problematising the position of a physical body and its sensory-motor capabilities and their development within synthetic surroundings as Metaverse and anticipate potential downsides of Metaverse?s uncontrolled growth. We will do so also by looking into Metaverse as an artificial-life-like phenomenon, following artificial-life rules and evolving a completely new?corporeality?, a body which is completely adapted to virtual spaces. We call this body the Dry Body, an entity sharing cognitive resources with the physical body it is not a physical part of, but has to extend to. (shrink)

Cancer is typically spoken of as a “complex” disease. But, in what sense are cancers “complex”? Is there one sense, or several? What implications does this complexity have – both for how we study, and how we intervene upon cancers? The aim of this paper is first, to clarify the variety of senses in which cancer is spoken of as "complex" in the scientific literature, and second, to discover what explanatory and predictive roles such features play.

The mainstream concept of death—the biological one—identifies death with the cessation of an organism. In this article, I challenge the mainstream position, showing that there is no single well-established concept of an organism and no universal concept of death in biological terms. Moreover, some of the biological views on death, if applied in the context of bedside decisions, might imply unacceptable consequences. I argue the moral concept of death—one similar to that of Robert Veatch—overcomes such difficulties. The moral view identifies (...) death with the irreversible cessation of a patient’s moral status, that is, a state when she can no longer be harmed or wronged. The death of a patient takes place when she is no longer capable of regaining her consciousness. In this regard, the proposal elaborated herein resembles that of Veatch yet differs from Veatch’s original project since it is universal. In essence, it is applicable in the case of other living beings such as animals and plants, provided that they have some moral status. (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)

I investigate the relationship between adaptation, as defined in evolutionary theory through natural selection, and the concept of emergence. I argue that there is an essential correlation between the former, and “emergence” defined in the field of algorithmic simulations. I first show that the computational concept of emergence (in terms of incompressible simulation) can be correlated with a causal criterion of emergence (in terms of the specificity of the explanation of global patterns). On this ground, I argue that emergence in (...) general involves some sort of selective processes. Finally, I show that a second criterion, concerning novel explanatory regularities following the emergence of a pattern, captures the robustness of emergence displayed by some cases of emergence (according to the first criterion). Emergent processes fulfilling both criteria are therefore exemplified in evolutionary biology by some so-called “innovations”, and mostly by the new units of fitness or new kinds of adaptations (like sexual reproduction, multicellular organisms, cells, societies) sometimes called “major transitions in evolution”, that recent research programs (Maynard-Smith and Szathmary 1995 ; Michod 1999 ) aims at explaining. (shrink)

The study of law and information technology comes with an inherent contradiction in that while technology develops rapidly and embraces notions such as internationalization and globalization, traditional law, for the most part, can be slow to react to technological developments and is also predominantly confined to national borders. However, the notion of the rule of law defies the phenomenon of law being bound to national borders and enjoys global recognition. However, a serious threat to the rule of law is looming (...) in the form of an assault by technological developments within artificial intelligence (AI). As large strides are made in the academic discipline of AI, this technology is starting to make its way into digital decision-making systems and is in effect replacing human decision-makers. A prime example of this development is the use of AI to assist judges in making judicial decisions. However, in many circumstances this technology is a ‘black box’ due mainly to its complexity but also because it is protected by law. This lack of transparency and the diminished ability to understand the operation of these systems increasingly being used by the structures of governance is challenging traditional notions underpinning the rule of law. This is especially so in relation to concepts especially associated with the rule of law, such as transparency, fairness and explainability. This article examines the technology of AI in relation to the rule of law, highlighting the rule of law as a mechanism for human flourishing. It investigates the extent to which the rule of law is being diminished as AI is becoming entrenched within society and questions the extent to which it can survive in the technocratic society. (shrink)

Cognitive science continues to make a compelling case for having a coherent, unique, and fundamental subject of inquiry: What is the nature of minds, where do they come from, and how do they work? Central to this inquiry is the notion of agents that have goals, one of which is their own persistence, who use dynamically constructed knowledge to act in the world to achieve those goals. An agentive perspective explains why a special class of systems have a cluster of (...) co‐occurring capacities that enable them to exhibit adaptive behavior in a complex environment: perception, attention, memory, representation, planning, and communication. As an intellectual endeavor, cognitive science may not have achieved a hard core of uncontested assumptions that Lakatos (1978) identifies as emblematic of a successful research program, but there are alternative conceptions according to which cognitive science has been successful. First, challenges of the early, core tenet of “Mind as Computation” have helped put cognitive science on a stronger foundation—one that incorporates relations between minds and their environments. Second, even if a full cross‐disciplinary theoretic consensus is elusive, cognitive science can inspire distant, deep, and transformative connections between pairs of fields. To be intellectually vital, cognitive science need not resemble a traditional discipline with its associated insularity and unchallenged assumptions. Instead, there is strength and resilience in the diverse perspectives and methods that cognitive science assembles together. This interdisciplinary enterprise is fragile and perhaps inherently unstable, as the looming absorption of cognitive science into psychology shows. Still, for many researchers, the excitement and benefits of triangulating on the nature of minds by integrating diverse cases cannot be secured by a stable discipline with an uncontested core of assumptions. (shrink)

This paper deals with the question: how is computation best individuated? -/- 1. The semantic view of computation: computation is best individuated by its semantic properties. 2. The causal view of computation: computation is best individuated by its causal properties. 3. The functional view of computation: computation is best individuated by its functional properties. -/- Some scientific theories explain the capacities of brains by appealing to computations that they supposedly perform. The reason for that is usually that computation is individuated (...) semantically. I criticize the reasons in support of this view and its presupposition of representation and semantics. Furthermore, I argue that the only justified appeal to a representational individuation of computation might be that it is partly individuated by implicit intrinsic representations. (shrink)

We examine the philosophical disputes among computer scientists concerning methodological, ontological, and epistemological questions: Is computer science a branch of mathematics, an engineering discipline, or a natural science? Should knowledge about the behaviour of programs proceed deductively or empirically? Are computer programs on a par with mathematical objects, with mere data, or with mental processes? We conclude that distinct positions taken in regard to these questions emanate from distinct sets of received beliefs or paradigms within the discipline: – The rationalist (...) paradigm, which was common among theoretical computer scientists, defines computer science as a branch of mathematics, treats programs on a par with mathematical objects, and seeks certain, a priori knowledge about their ‘correctness’ by means of deductive reasoning. – The technocratic paradigm, promulgated mainly by software engineers and has come to dominate much of the discipline, defines computer science as an engineering discipline, treats programs as mere data, and seeks probable, a posteriori knowledge about their reliability empirically using testing suites. – The scientific paradigm, prevalent in the branches of artificial intelligence, defines computer science as a natural (empirical) science, takes programs to be entities on a par with mental processes, and seeks a priori and a posteriori knowledge about them by combining formal deduction and scientific experimentation. We demonstrate evidence corroborating the tenets of the scientific paradigm, in particular the claim that program-processes are on a par with mental processes. We conclude with a discussion in the influence that the technocratic paradigm has been having over computer science. (shrink)

This article on the epistemology of computational models stems from an analysis of the Gaia hypothesis (GH). It begins with James Kirchner’s criticisms of the central computational model of GH: Daisyworld. Among other things, the model has been criticized for being too abstract, describing fictional entities (fictive daisies on an imaginary planet) and trying to answer counterfactual (what-if) questions (how would a planet look like if life had no influence on it?). For these reasons the model has been considered not (...) testable and therefore not legitimate in science, and in any case not very interesting since it explores non actual issues. This criticism implicitly assumes that science should only be involved in the making of models that are ‘‘actual’’ (by opposition to what-if) and ‘‘specific’’ (by opposition to abstract). I challenge both of these criticisms in this article. First by showing that although the testability—understood as the comparison of model output with empirical data—is an important procedure for explanatory models, there are plenty of models that are not testable. The fact that these are not testable (in this restricted sense) has nothing to do with their being ‘‘abstract’’ or ‘‘what-if’’ but with their being predictive models. Secondly, I argue that ‘‘abstract’’ and ‘‘what-if’’ models aim at (respectable) epistemic purposes distinct from those pursued by ‘‘actual and specific models’’. Abstract models are used to propose how-possibly explanation or to pursue theorizing. What-if models are used to attribute causal or explanatory power to a variable of interest. The fact that they aim at different epistemic goals entails that it may not be accurate to consider the choice between different kinds of model as a ‘‘strategy‘‘. (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.

Although life and questions connected with its definition among traditional philosophical topics, at the beginning of the twenty-first century and after roughly two and a half thousand years of philosophical tradition no satisfactory definition of life is at one’s disposal. Though difficulties bond up with life definition don´t accompany philosophy alone, but also almost all fields touched by this topic. Causes of this state of affairs can be viewed as a combination of several complementary connected facts. Those facts are in (...) the text stated as reasons against the possibility of creating universal definition of life and apply here one by one to the origin of life a postulated boarder line between life and nonlife, evolution of life and its variedness, multiplicity of definitions of life and still going discoveries of new organisms, philosophical tradition and lack of clarity of concepts, evolution of life as a historical process, critique of definitions of life from the point of view of „sample one“ and reflection of the concept life by conceptual analysis. Particular sources are about to be analyzed from the point of philosophy of biology with care for philosophical tradition, to questions linked with life and its selected definitions, to actual findings and explorations focused on life and possible contribution of philosophy to the endeavor of finding definition of life as such. Ground of proposed text is naturalism, physicalism and reductionism. (shrink)

The aim of this chapter is to show how the technological research activity called “artificial life” is shedding new light on human creativity. Artificial life aims to understanding the fundamental behavior of life-like systems by synthesizing that behavior in artificial systems (more on artificial life below). One of the most interesting behaviors of living systems is their creativity. Biological creativity can be found in both individual living organisms and in the whole biosphere—the entire interconnected system comprised of all forms of (...) life—but I will focus in this chapter on the biological creativity exhibited by the evolutionary process. This is the creativity that enabled the earliest simple life forms to spontaneously evolve into the incredibly rich and beautiful diversity of life that now surrounds us. This diversity of life includes the most complex adaptive and intelligent systems in the known universe. This is an amazingly powerful spontaneous creation process, indeed. I will refer to it as hyper-creativity to call attention to the way in which it produces qualitatively new and more complex kinds of adaptations. There is a similar quality in human creativity. I am thinking of the aesthetic and cultural creativity of artists, but also the intellectual creativity of scientists and scholars, as well as the commercial and practical creativity of craftsmen, businessmen, and entrepreneurs. And I want to focus especially on the hyper- creative aspects of human creativity—the way in which human activity can yield qualitatively new and more complex creations. (shrink)