Our fascination with artificial intelligence (AI), robots and sentient machines has a long history, and references to such humanoids are present even in ancient myths and folklore. The advancements in digital and computational technology have turned this fascination into apprehension, with the machines often being depicted as a binary to the human. However, the recent domains of academic enquiry such as transhumanism and posthumanism have produced many a literature in the genre of science fiction (SF) that endeavours (...) to alter this antagonistic notion of AI. In his novel Klara and the Sun, Kazuo Ishiguro explores this notion of AI as a caring machine capable of nursing an ailing young girl back to health. Through this portrayal of AI as a sentient being capable of empathy and cognition, Ishiguro is attempting to usher in a new perception of AI: a posthuman perception that challenges the conventional notions of AI as a machine devoid of emotions. The novel further expands on the idea of self, soul and human consciousness and ponders on the question, what makes humans human, and if it is possible to imbibe these qualities onto an AI. Owing to the novelty of this notion, an unexplored avenue that deserves further exploration, this paper examines the plausibility of this new notion of AI through a careful exegesis of the novel. The paper also attempts to chart the impact of SF in society and culture. The study reveals a positive shift in perception towards AI, and there seems to be much scope for interdisciplinary and transdisciplinary research. (shrink)

Artificial intelligence is developing rapidly. There are technologies available that fulfil several tasks better than humans can and even behave like humans to some extent. Thus, the situation prompts the question whether AI should be granted legal person- and/or agenthood? There have been similar situations in history where the legal status of slaves or indigenous peoples was discussed. Still, in those historical questions, the subjects under study were always natural persons, i.e., they were living beings belonging to (...) the species Homo sapiens. We analyse the situation from moral-ethical and practical perspectives. The final conclusion is that the currently existing AIs are still so far removed from humans that there is simply no need to think seriously about legal person- or agenthood. Doing so would mean imposing obligations on the AI to follow. This, in turn, would mean that certain rights in relation to those obligations would have to be granted as well. By all evidence, this is something that humans are not ready to do yet and might never get that far. (shrink)

The following paper presents both a historical and personal account of the societal and ethical issues arising in the development of artificial intelligence, tracking, where I was involved, the issues from the nineteen seventies onward. My own involvement in the AI narrative begins with the early discussions around whether machines can think. These first discussions, in time, evolved secondly, with the rise of the internet in the nineties, into perceptions of AI as distributed intelligence, addressing its impact (...) on social structures including basic ethical issues arising in daily life. Thirdly, in the sweeping application of AI to all kinds of societal goals and contexts, the awareness that all natural and artificial things might be digitally connected with each other and to human agents led my further involvement in the AI narrative. Tracing this evolution from start to finish, I conclude my own narrative in the history of AI by presenting some of the future challenges for the development and use of artificial intelligences. Through the application of recent research in academia, scientific associations and political bodies, I address the possibilities for the good life, both with and without artificial intelligences. (shrink)

This paper explores and ultimately affirms the surprising claim that artificial intelligence (AI) can become part of the person, in a robust sense, and examines three ethical and legal implications. The argument is based on a rich, legally inspired conception of persons as free and independent rightholders and objects of heightened protection, but it is construed so broadly that it should also apply to mainstream philosophical conceptions of personhood. The claim is exemplified by a specific technology, devices that (...) connect human brains with computers and operate by AI-algorithms. Under philosophically reasonable and empirically realistic conditions, these devices and the AI running them become parts of the person, in the same way as arms, hearts, or mental capacities are. This transformation shall be called empersonification. It has normative and especially legal consequences because people have broader and stronger duties regarding other persons (and parts of them) than regarding things. Three consequences with practical implications are: (i) AI-devices cease to exist as independent legal entities and come to enjoy the special legal protection of persons; (ii) therefore, third parties such as manufacturers or authors of software lose (intellectual) property rights in device and software; (iii) persons become responsible for the outputs of the empersonified AI-devices to the same degree that they are for desires or intentions arising from the depths of their unconscious. More generally, empersonification marks a new step in the long history of human–machine interaction that deserves critical ethical reflection and calls for a stronger value-aligned development of these technologies. (shrink)

This paper explores the theological and philosophical implications of artificial intelligence (AI) and Neuroscience research on the Christian’s notion of the human person. The paschal mystery of Christ is the intuitive foundation of Christian anthropology. In the intellectual history of the Christianity, Platonism and Aristotelianism have been employed to articulate the Christian philosophical anthropology. The Aristotelian systematization has endured to this era. Since the modern period of the Western intellectual history, Aristotelianism has been supplanted by the (...) positive sciences as the standard scientific and philosophical interpretative framework for many. This paper argues that there is need for a new Christian anthropology based on the interpretation of the paschal mystery of Christ that is consistent with contemporary science and philosophy, especially as informed by research in AI and neuroscience. Christian anthropology today ought to be fully aware of the theological and philosophical implications of AI and neuroscience research. This paper denotes this new Christian anthropology as neuro-theological Christian anthropology. (shrink)

This paper explores and ultimately affirms the surprising claim that artificial intelligence (AI) can become part of the person, in a robust sense, and examines three ethical and legal implications. The argument is based on a rich, legally inspired conception of persons as free and independent rightholders and objects of heightened protection, but it is construed so broadly that it should also apply to mainstream philosophical conceptions of personhood. The claim is exemplified by a specific technology, devices that (...) connect human brains with computers and operate by AI-algorithms. Under philosophically reasonable and empirically realistic conditions, these devices and the AI running them become parts of the person, in the same way as arms, hearts, or mental capacities are. This transformation shall be called empersonification. It has normative and especially legal consequences because people have broader and stronger duties regarding other persons (and parts of them) than regarding things. Three consequences with practical implications are: (i) AI-devices cease to exist as independent legal entities and come to enjoy the special legal protection of persons; (ii) therefore, third parties such as manufacturers or authors of software lose (intellectual) property rights in device and software; (iii) persons become responsible for the outputs of the empersonified AI-devices to the same degree that they are for desires or intentions arising from the depths of their unconscious. More generally, empersonification marks a new step in the long history of human–machine interaction that deserves critical ethical reflection and calls for a stronger value-aligned development of these technologies. (shrink)

The future rests under the sign of technology. Given the prevalence of technological neutrality and inevitabilism, most conceptualizations of the future tend to ignore moral problems. In this paper we argue that every choice about future technologies is a moral choice and even the most technology-dominated scenarios of the future are, in fact, moral provocations we have to imagine solutions to. We begin by explaining the intricate connection between morality and the future. After a short excursion into the history (...) of Artificial Intelligence, we analyse two possible scenarios, which show that building the future with technology is, first and foremost, a moral endeavor. (shrink)

The inquiry into the moral status of artificial intelligence (AI) is leading to prolific theoretical discussions. A new entity that does not share the material substrate of human beings begins to show signs of a number of properties that are nuclear to the understanding of moral agency. It makes us wonder whether the properties we associate with moral status need to be revised or whether the new artificial entities deserve to enter within the circle of moral consideration. (...) This raises the foreboding that we are at the gates of an anthropological crisis: the properties bound to moral agency have been exclusively possessed in the past by human beings and have shaped the very definition of being human. In this article, I will argue that AI does not lead us to an anthropological crisis and that, if we adhere to the history and philosophy of technology, we will notice that the debate on the moral status of AI uncritically starts from an anthropology of properties and loses sight of the relational dimension of technology. First, I will articulate three criteria for analyzing different anthropological views in philosophy of technology. Second, I will propose six anthropological models: traditional, industrial, phenomenological, postphenomenological, symmetrical, and cyborg. Third, I will show how the emergence of AI breaks with the dynamics of increased relationality in the history and philosophy of technology. I will argue that this aspect is central to debates about the moral status of AI, since it sheds light on an aspect of moral consideration that has been obscured. Finally, I will reject entirely relational approaches to moral status and propose two hybrid possibilities for rethinking it. (shrink)

We are obsessed with self-improvement; it’s a billion-dollar industry. But apps, workshops, speakers, retreats, and life hacks have not made us happier. Obsessed with the endless task of perfecting ourselves, we have become restless, anxious, and desperate. We are improving ourselves to death. The culture of self-improvement stems from philosophical classics, perfectionist religions, and a ruthless strain of capitalism—but today, new technologies shape what it means to improve the self. The old humanist culture has given way to artificial (...) class='Hi'>intelligence, social media, and big data: powerful tools that do not only inform us but also measure, compare, and perhaps change us forever. This book shows how self-improvement culture became so toxic—and why we need both a new concept of the self and a mission of social change in order to escape it. Mark Coeckelbergh delves into the history of the ideas that shaped this culture, critically analyzes the role of technology, and explores surprising paths out of the self-improvement trap. Digital detox is no longer a viable option and advice based on ancient wisdom sounds like yet more self-help memes: The only way out is to transform our social and technological environment. Coeckelbergh advocates new “narrative technologies” that help us tell different and better stories about ourselves. However, he cautions, there is no shortcut that avoids the ancient philosophical quest to know yourself, or the obligation to cultivate the good life and the good society. (shrink)

Artificial intelligence (AI) technologies are increasingly creeping into the work sphere, thereby gradually questioning and/or disturbing the long-established moral concepts and norms communities have been using to define what makes work good. Each community, and Muslims make no exception in this regard, has to revisit their moral world to provide well-thought frameworks that can engage with the challenging ethical questions raised by the new phenomenon of AI-mediated work. For a systematic analysis of the broad topic of AI-mediated work (...) ethics from an Islamic perspective, this article focuses on presenting an accessible overview of the “moral world” of work in the Islamic tradition. Three main components of this moral world were selected due to their relevance to the AI context, namely (1) Work is inherently good for humans, (2) Practising a religiously permitted profession and (c) Maintaining good relations with involved stakeholders. Each of these three components is addressed in a distinct section, followed by a sub-section highlighting the relevance of the respective component to the particular context of AI-mediated work. The article argues that there are no unsurmountable barriers in the Islamic tradition against the adoption of AI technologies in work sphere. However, important precautions should be considered to ensure that embracing AI will not be at the cost of work-related moral values. The article also highlights how important lessons can be learnt from the positive historical experience of automata that thrived in the Islamic civilization. (shrink)

This paper considers recent experiments in art and artificial intelligence that crystallize around training algorithms to generate artworks based on datasets derived from the Western art historical canon. Over the last decade, a shift towards the rejection of canonicity has begun to take shape in art historical discourse. At the same time, algorithmically enabled practices in the US and Europe have emerged that entrench the Western canon as a locus and guarantor of aesthetic value. Operating within the epistemic (...) framework of a “view from nowhere,” this tendency in generative art inherits the coloniality of both art history and artificial intelligence. Producing “art histories from nowhere,” this tendency conflates the conceptual category of visual art with the histories of Western cultural production. It reproduces a set of aesthetic values that entrench the mythology of the artist-genius and his imputed whiteness and masculinity; the extolment of innovation and novelty as self-evident virtues; disembodied Cartesian models of knowing and sensing; and the erasure of contributions that have been occluded from canonical visibility. As we encounter systems trained on particular visions of art history and of the artist, how might we remain attentive to the specific lens through which they are taught to see? This essay addresses that question by bringing the coloniality of recent experiments into view, bridging data feminisms and decolonial studies to formulate alternative visions of encounters between art and AI. (shrink)

In this article the central philosophical issues concerning human-level artificial intelligence (AI) are presented. AI largely changed direction in the 1980s and 1990s, concentrating on building domain-specific systems and on sub-goals such as self-organization, self-repair, and reliability. Computer scientists aimed to construct intelligence amplifiers for human beings, rather than imitation humans. Turing based his test on a computer-imitates-human game, describing three versions of this game in 1948, 1950, and 1952. The famous version appears in a 1950 article (...) in Mind, ‘Computing Machinery and Intelligence’ (Turing 1950). The interpretation of Turing's test is that it provides an operational definition of intelligence (or thinking) in machines, in terms of behavior. ‘Intelligent Machinery’ sets out the thesis that whether an entity is intelligent is determined in part by our responses to the entity's behavior. Wittgenstein frequently employed the idea of a human being acting like a reliable machine. A ‘living reading-machine’ is a human being or other creature that is given written signs, for example Chinese characters, arithmetical symbols, logical symbols, or musical notation, and who produces text spoken aloud, solutions to arithmetical problems, and proofs of logical theorems. Wittgenstein mentions that an entity that manipulates symbols genuinely reads only if he or she has a particular history, involving learning and training, and participates in a social environment that includes normative constraints and further uses of the symbols. (shrink)

Many scholars make a very clear distinction between intelligence and consciousness. Let’s take one of the most famous today, Israeli history Professor, Yuval Noah Harari, the author of Sapiens and Homo Deus. In his 2018 book, 21 lessons for the twenty-first century, he writes that, “intelligence and consciousness are very different things. Intelligence is the ability to solve problems. Consciousness is the ability to feel things such as pain, joy, love, and anger.”.

Music and affects share a long history. In recent times, 4E cognitive sciences (embodied, embedded, enacted, and extended), situated affectivity, and related ecological theoretical frameworks have been conceptualizing music as a case of a tool for feeling. Drawing on this debate, I propose to further theorize the role of music in situating our affectivity by analyzing how the very affective affordances of music are technologically situated. In other words, I propose to shift the attention from music as a tool (...) for feeling to the tools for feeling music. I argue that the experience of music as a tool for feeling may be altered, enhanced, or lessened depending on the tools for feeling music. I investigate the extent to which AI might be a case of a tool for feeling music and examine the influence it could exert over musical affectivity. I conclude that AI can be considered a tool for feeling music of curatorial type and that the limitations and/or biases of AI as a method risk lessening the power of musical affective affordances. (shrink)

The moral enhancement of human beings is a constant theme in the history of humanity. Today, faced with the threats of a new, globalised world, concern over this matter is more pressing. For this reason, the use of biotechnology to make human beings more moral has been considered. However, this approach is dangerous and very controversial. The purpose of this article is to argue that the use of another new technology, AI, would be preferable to achieve this goal. Whilst (...) several proposals have been made on how to use AI for moral enhancement, we present an alternative that we argue to be superior to other proposals that have been developed. (shrink)

The tendency to idealise artificial intelligence as independent from human manipulators, combined with the growing ontological entanglement of humans and digital machines, has created an “anthrobotic” horizon, in which data analytics, statistics and probabilities throw our agential power into question. How can we avoid the consequences of a reified definition of intelligence as universal operation becoming imposed upon our destinies? It is here argued that the fantasised autonomy of automated intelligence presents a contradistinctive opportunity for philosophical (...) consciousness to understand itself anew as holistic and co-creative, beyond the recent “analytic” moment of the history of philosophy. Here we introduce the concept of “crealectic intelligence”, a meta-analytic and meta-dialectic aspect of consciousness. Intelligent behaviour may consist in distinguishing discrete familiar parts or reproducible functions in the midst of noise via an analytic process of segmentation; intelligence may also manifest itself in the constitution of larger wholes and dynamic unities through a dialectic process of association or assemblage. But, by contrast, crealectic intelligence co-creates realities in the image of an ideal or truth, taking into account the desiring agent imbued with a sense of possibility, in a relationship not only with the Real but also with the creative sublime or “Creal”. (shrink)

Artificial intelligence is not and has never been a technology. It began with Turing's famous "can machine think?", a philosophical question that too many were quick to transform into a more prosaic "can Thought be mechanized?" Only in this perspective can the history and the technological success of AI be duly explained and understood, one of the tasks this book engages in. -/- It is important for philosophers to take AI seriously, and for AI researchers to see (...) their discipline through the lenses of the adventure of the Spirit of man. The conclusion is that - paraphrasing Von Clausewitz's declaration about politics - "AI is a continuation of Philosophy by other means". Only after a proper meditaion and understanding of this fact can all the ethical, social and economical debates surrounding Artificial Intelligence begin to be discussed. (shrink)

Artificial Intelligence (AI) is a high-profile subject these days. In its brief history it has undergone several highs and lows and suffered from significant degrees of hype as well as antagonism and fear. One thing is clear: we are no closer to the goal of producing a truly sentient being than when it started. Nonetheless, the tools developed by AI researchers are here to stay and as with all technological advances it has its good and bad aspects. (...) In this article I will present a brief overview of the field of AI looking at what it is, how it developed, what are its specialism as well as some of its well-publicised successes, and failures, as well as pointing out some key Christian participants in the story. (shrink)

Hubert and Stuart Dreyfus have tried to place connectionism and artificial intelligence in a broader historical and intellectual context. This history associates connectionism with neuroscience, conceptual holism, and nonrationalism, and artificial intelligence with conceptual atomism, rationalism, and formal logic. The present paper argues that the Dreyfus account of connectionism and artificial intelligence is both historically and philosophically misleading.

Applications of artificial intelligence/machine learning in health care are dynamic and rapidly growing. One strategy for anticipating and addressing ethical challenges related to AI/ml for health care is patient and public involvement in the design of those technologies – often referred to as ‘co-design’. Co-design has a diverse intellectual and practical history, however, and has been conceptualized in many different ways. Moreover, AI/ml introduces challenges to co-design that are often underappreciated. Informed by perspectives from critical data studies (...) and critical digital health studies, we review the research literature on involvement in health care, and involvement in design, and examine the extent to which co-design as commonly conceptualized is capable of addressing the range of normative issues raised by AI/ml for health care. We suggest that AI/ml technologies have amplified and modified existing challenges related to patient and public involvement, and created entirely new challenges. We outline three pitfalls associated with co-design for ethical AI/ml for health care and conclude with suggestions for addressing these practical and conceptual challenges. (shrink)

The field of artificial intelligence (AI) can be characterized as the investigation of computational systems that exhibit intelligent behavior (including algorithms and models used in these systems). The emphasis is not so much on understanding (human) cognitive processes as on producing models, algorithms, and systems that are capable of apparently intelligent behavior by whatever means available. The idea of AI has had a long history that can be traced all the way back to, for example, Leibniz. The (...) idea was furthered through the development, early in this century, of mathematical logic, cybernetics, and information theory, all of which contributed to AI theorizing. Alan Turing's bold claim that machines can think, advanced in his famous 1950 paper, and the “Turing test” he proposed therein as a means to test the intelligence of machines predated and prompted the formation of AI. Six years later, the Dartmouth conference in 1956, the participants of which included Allen Newell, Herbert Simon, Marvin Minsky, and John McCarthy, inaugurated AI as an academic discipline. This contemporary version of AI was facilitated by the invention and rapidly widening use of digital computers. AI embodies an extremely diverse set of ideas that are often mutually conflicting (as in the case of symbolism versus connectionism), and its relationship with other fields studying cognition (such as psychology and linguistics) has been a close but uneasy one. (shrink)

In this article, I establish a theory of knowledge approach for evaluating the use of computers for educational purposes at the university. In so doing, I trace part of the history of the “enabling factor” of Artificial Intelligence in this sector, an important element that has been integrated into everyday learning environments. The result of my reflection is a dialogical structure, directly inspired by past technology assessment research, which illustrates the conceptual advancement of researchers in the field (...) of learning technologies. The notions covered have implications in future policy-related discourse with regards to education. (shrink)

The association of Wittgenstein’s name with the notion of artificial intelligence is bound to cause some surprise both to Wittgensteinians and to people interested in artificial intelligence. After all, Wittgenstein died in 1951 and the term artificial intelligence didn’t come into use until 1956 so that it seems unlikely that one could have anything to do with the other. However, establishing a connection between Wittgenstein and artificial intelligence is not as insuperable a (...) problem as it might appear at first glance. While it is true that artificial intelligence as a quasi-distinct discipline is of recent vintage, some of its concerns, especially those of a philosophical nature, have been around for quite some time. At the birth of modern philosophy we find Descartes wondering whether it would be possible to create a machine that would be phenomenologically indistinguishable from.. (shrink)

I develop a multilevel, holistic view of persons, emphasizing embodiment, emotions, consciousness, and the social self. In successive sections I draw from six sources: 1. Theology. The biblical understanding of the unitary, embodied, social self gave way in classical Christianity to a body‐soul dualism, but it has been recovered by many recent theologians. 2. Neuroscience. Research has shown the localization of mental functions in regions of the brain, the interaction of cognition and emotion, and the importance of social interaction in (...) evolutionary history and child development. 3. Artificial intelligence. Some forms of robotics use embodied systems that learn by interacting with their environment, but the possibilities for emotion, socialization, and consciousness in robots remain problematic. 4. Relations between levels. Concepts that can help us relate studies of neurons and persons include the hierarchy of levels, the communication of information, thebehavior of dynamic systems, and epistemological and ontological emergence. 5. Philosophy of mind. Two‐aspect theories of the mind‐brain relation offer an alternative between the extremes of eliminative materialism and the thesis that consciousness is irreducible. 6. Process philosophy. I suggest that process thought provides a coherent philosophical framework in which these themes can be brought together. It combines dipolar monism with organizational pluralism, and it emphasizes embodiment, emotions, a hierarchy oflevels, and the social character of selfhood. (shrink)

The central thesis of this book is that the argument that probability is insufficient to handle uncertainty in artificial intelligence (AI) is metaphysical in nature. Piscopo calls this argument against probability the non-adequacy claim and provides this summary of it [which first appeared in (Piscopo and Birattari 2008)]:Probability theory is not suitable to handle uncertainty in AI because it has been developed to deal with intrinsically stochastic phenomena, while in AI, uncertainty has an epistemic nature. (Piscopo (3))Piscopo uses (...) the term “metaphysical” in the Popperian sense: the non-adequacy claim is metaphysical (rather than scientific) since it cannot be disproved (much less established) by observation or experiment.In Chapter 2, Piscopo recounts some of the relevant history and issues in philosophy of science. There is little that is controversial here. One problem is that when addressing the question of how a particular probabilistic model is tested, Piscopo limits herself .. (shrink)

We are not today close to producing a computer that could convince us that it is intelligent. Some philosophers have argued that we are not even appreciably closer to this goal than we were ten years ago. But why should artificial intelligence even be considered possible? In this paper I shall argue that the temptation to believe in the possibility of AI stems from a misunderstanding about the nature of ideas; further, this misunderstanding can be traced back at (...) least to Plato’s presentation of the metaphor of the cave. A phenomenological view of the nature of ideas removes the temptation to believe in the possibility of AI as presently considered. (shrink)

Diversity is often announced as a solution to ethical problems in artificial intelligence (AI), but what exactly is meant by diversity and how it can solve those problems is seldom spelled out. This lack of clarity is one hurdle to motivating diversity in AI. Another hurdle is that while the most common perceptions about what diversity is are too weak to do the work set out for them, stronger notions of diversity are often defended on normative grounds that (...) fail to connect to the values that are important to decision-makers in AI. However, there is a long history of research in feminist philosophy of science and a recent body of work in social epistemology that taken together provide the foundation for a notion of diversity that is both strong enough to do the work demanded of it, and can be defended on epistemic grounds that connect with the values that are important to decision-makers in AI. We clarify and defend that notion here by introducing emergent expertise as a network phenomenon wherein groups of workers with expertise of different types can gain knowledge not available to any individual alone, as long as they have ways of communicating across types of expertise. We illustrate the connected epistemic and ethical benefits of designing technology with diverse groups of workers using the examples of an infamous racist soap dispenser, and the millimeter wave scanners used in US airport security. (shrink)

Heuristic programming was the first area in which AI methods were tested. The favourite case-studies were fairly simple toy- problems, such as cryptarithmetic, games, such as checker or chess, and formal problems, such as logic or geometry theorem-proving. These problems are well-defined, roughly speaking, at least in comparison to real-life problems, and as such have played the role of Drosophila in early AI. In this chapter I will investigate the origins of heuristic programming and the shift to more knowledge-based and (...) real-life problem solving. (shrink)

We discuss, first, TUring's role in the development of the computer; second, the early history of Artificial Intelligence (to 1956); and third, TUring's fa- mous imitation game, now universally known as the TUring test, which he proposed in cameo form in 1948 and then more fully in 1950 and 1952. Various objections have been raised to Turing's test: we describe some of the most prominent and explain why, in our view, they fail.

In this paper, we present a survey of the development of the technique of argument diagramming covering not only the fields in which it originated - informal logic, argumentation theory, evidence law and legal reasoning – but also more recent work in applying and developing it in computer science and artificial intelligence. Beginning with a simple example of an everyday argument, we present an analysis of it visualised as an argument diagram constructed using a software tool. In the (...) context of a brief history of the development of diagramming, it is then shown how argument diagrams have been used to analyze and work with argumentation in law, philosophy and artificial intelligence. (shrink)

Here are the topics. What is logical AI? The common sense informatic situation Relevant history of logic Problems with logical AI Nonmonotonic reasoning Domain dependent control of reasoning Concepts as objects Contexts as objects Partially defined objects Self-awareness Remarks and references LOGICAL AI Logical AI proposes computer systems that represent what they know about the world by sentences in a suitable mathematical logical language. It achieves goals by inferring that a certain strategy of action is appropriate to achieve the (...) goal. It then carries out that strategy, using observations also expressed as logical sentences. (shrink)

Historically, the dispute between empiricists and nativists in philosophy and cognitive science has concerned human and animal minds (Margolis and Laurence in Philos Stud: An Int J Philos Anal Tradit 165(2): 693-718, 2013, Ritchie in Synthese 199(Suppl 1): 159–176, 2021, Colombo in Synthese 195: 4817–4838, 2018). But recent progress has highlighted how empiricist and nativist concerns arise in the construction of artificial systems (Buckner in From deep learning to rational machines: What the history of philosophy can teach us (...) about the future of artificial intelligence. Oxford University Press.). This paper uses nativism and empiricism to address questions about the nature of artificial intelligence and its trajectory. It begins by defining the nativism/empiricism debate in terms of the generality of a system. Nativist systems have initial states with domain-specific features; empiricist systems have initial states with only domain-general features. With the debate framed in this way, it then explores a variety of arguments for nativism and empiricism in AI. These arguments revolve around two different questions which must be distinguished: whether nativism the only possible approach to developing human-level AI (HLAI); and whether nativism is the most practical approach to developing HLAI. On the first question, it argues that nativism is quite clearly not the only possible approach to developing HLAI, as is sometimes suggested. It argues that existing arguments for the necessity of nativism are unconvincing, because they analogize from poverty of the stimulus arguments about humans, while AIs often have access to much more data than humans. Then it argues that the case of evolution gives us a compelling argument against nativism. On the second, practical question, the paper argues that there is a tradeoff between the advantages of encoding innate machinery directly, and the advantages of evolving or learning it. However, as the past decade has shown, empiricism is a much more viable path to greater capability levels, given the ‘bitter lesson’ (Sutton in _Reinforcement Learning: An Introduction_. MIT press.) that encoding the ‘correct’ knowledge in AI systems is perennially outperformed by more empiricist methods that leverage large-scale data and computation. (shrink)

Temporal Logic: From Ancient Ideas to Artificial Intelligence deals with the history of temporal logic as well as the crucial systematic questions within the field. The book studies the rich contributions from ancient and medieval philosophy up to the downfall of temporal logic in the Renaissance. The modern rediscovery of the subject, which is especially due to the work of A. N. Prior, is described, leading into a thorough discussion of the use of temporal logic in computer (...) science and the understanding of natural language. Temporal Logic: From Ancient Ideas to Artificial Intelligence thus interweaves linguistic, philosophical and computational aspects into an informative and inspiring whole. (shrink)

Experts in medical informatics have argued for the incorporation of ever more machine-learning algorithms into medical care. As artificial intelligence research advances, such technologies raise the possibility of an “iDoctor,” a machine theoretically capable of replacing the judgment of primary care physicians. In this article, I draw on Martin Heidegger’s critique of technology to show how an algorithmic approach to medicine distorts the physician–patient relationship. Among other problems, AI cannot adapt guidelines according to the individual patient’s needs. In (...) response to the objection that AI could develop this capacity, I use Hubert Dreyfus’s analysis of AI to argue that attention to the needs of each patient requires the physician to attune his or her perception to the patient’s history and physical exam, an ability that seems uniquely human. Human physician judgment will remain better suited to the practice of primary care despite anticipated advances in AI technology. (shrink)

For all of recorded history prior to the second half of the twentieth century, there has been but one realm in which the cognitive processes of reasoning and problem solving, learning and discovery, language and mathematics took place. The realm of human intellect no longer has an exclusive claim on these cognitive processes--artificial intelligence represents a parallel claim. Wagman compares the two realms, focusing on each of the major components of cognition: logic, reasoning, problem-solving, language, memory, learning, (...) and discovery. He identifies consonant and disparate modes of cognition, and develops a general theory of human and artificial intelligence. (shrink)

The imaginative context in which artificial intelligence is embedded remains a crucial touchstone from which to understand and critique both the histories and prospective futures of an AI-driven world. A recent article from Cave and Dihal sets out a narrative schema of four hopes and four corresponding fears associated with intelligent machines and AI. This article seeks to respond to the work of Cave and Dihal by presenting a gendered reading of this schema of hopes and fears. I (...) offer a brief genealogy of narratives which feature female automata, before turning to examine how gendered technology today—particularly AI assistants like Siri and Alexa—reproduces the historical narratives associated with intelligent machines in new ways. Through a gendered reading of the hopes and fears associated with AI, two key responses arise. First, that the affective reactions to intelligent machines cannot be readily separated where such machines are gendered female. And second, that the gendering of AI technologies today can be understood as an attempt to reconcile the opposing hopes and fears AI produces, and that this reconciliation is based on the association of such technologies with traditional notions of femininity. Critically, a gendered reading enables us to problematize the narratives associated with AI and expose the power asymmetries that lie within, and the technologies which arise out of, such narratives. (shrink)

Since antiquity, philosophers and engineers have tried to take life’s measure by reproducing it. Aiming to reenact Creation, at least in part, these experimenters have hoped to understand the links between body and spirit, matter and mind, mechanism and consciousness. Genesis Redux examines moments from this centuries-long experimental tradition: efforts to simulate life in machinery, to synthesize life out of material parts, and to understand living beings by comparison with inanimate mechanisms. Jessica Riskin collects seventeen essays from distinguished scholars in (...) several fields. These studies offer an unexpected and far-reaching result: attempts to create artificial life have rarely been driven by an impulse to reduce life and mind to machinery. On the contrary, designers of synthetic creatures have generally assumed a role for something nonmechanical. The history of artificial life is thus also a history of theories of soul and intellect. Taking a historical approach to a modern quandary, Genesis Redux is essential reading for historians and philosophers of science and technology, scientists and engineers working in artificial life and intelligence, and anyone engaged in evaluating these world-changing projects. (shrink)

Experts in medical informatics have argued for the incorporation of ever more machine-learning algorithms into medical care. As artificial intelligence research advances, such technologies raise the possibility of an “iDoctor,” a machine theoretically capable of replacing the judgment of primary care physicians. In this article, I draw on Martin Heidegger’s critique of technology to show how an algorithmic approach to medicine distorts the physician–patient relationship. Among other problems, AI cannot adapt guidelines according to the individual patient’s needs. In (...) response to the objection that AI could develop this capacity, I use Hubert Dreyfus’s analysis of AI to argue that attention to the needs of each patient requires the physician to attune his or her perception to the patient’s history and physical exam, an ability that seems uniquely human. Human physician judgment will remain better suited to the practice of primary care despite anticipated advances in AI technology. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Philosophic Foundations of Mimetic Theory and Cognitive Science(Including Artificial Intelligence)Jean-Pierre Dupuy (bio)In the mid 1970s I discovered at the same time cognitive science and mimetic theory. Being a philosopher with a scientific background, I immediately brought them together and tried to reconceptualize the latter in terms of the former. In a sense, I haven't stopped doing that in the last 45 years. That is why I (...) feel fully at home with the goals pursued by the organizers of this conference.I do not believe it is feasible or even desirable to operate the rapprochement between the two disciplines at a global level. The history of cognitive science is exceedingly involved, and the part played by artificial intelligence in it compared to the part played by the neurosciences has varied greatly over time.1 Only a piecemeal approach makes sense. I present here four short case studies that cover the whole period, from the beginnings in cybernetics to today's artificial intelligence (AI).In order to better situate these studies within a broader frame, I propose the diagram in Figure 1. To comment on it would require more time than available for my whole presentation. Let me just say the following. [End Page 1]It is essential to distinguish between two kinds of AI. The first to bear this name was launched by economist Herbert Simon and computer scientist Allan Newell at a Dartmouth conference in 1956. It was based on the premise that thinking is computing, with this computation bearing on sentences of a "language of thought" implementable in a digital computer. It turns out that this AI(1) was progressively substituted from the 1980s onward by an AI(2), which, to some extent, was a return to a fundamental result obtained by cybernetics in 1943. Neurophysiologist Warren McCulloch and mathematician Walter Pitts had constructed a mathematical model of a network of elementary calculators called "neurons" and explored its properties. The capacity to "think" was one of these. It was not situated at the elementary level, as was the case in AI(1), but it emerged at the collective level from the interactions between the neurons.This model was heavily criticized by the supporters of AI(1),2 and its first implementations, in the notorious "Perceptron" in particular (1957), were pitiful failures. However, the emergence of "second-order cybernetics" under the guidance of cybernetician Heinz von Foerster and the theory of complex, self-organizing systems that it helped develop, in cooperation and rivalry with similar efforts in the physical sciences, enabled the neural network model to come to a second life under different names: formal neurons, deep learning, big data, and the like. Together, they constitute AI(2).The following network of influences wouldn't have been possible without its stem, that is, the boxes containing the names of Kurt Gödel and Alan Turing. The former revolutionized logic by showing that reasoning about arithmetic and arithmetic are one and the same, insofar as it is possible to code in a 1-to-1 fashion propositions about integers (for instance, "7 is a prime number") by means of integers. The latter demonstrated that some mechanisms can self-transcend inasmuch as they generate an ensemble of outputs that cannot be defined mechanistically. Later, John von Neumann, who is credited along with Alan Turing with having invented the digital computer, would use this logical possibility to define complexity: A mechanism is complex if it is capable of generating one more complex than itself. The gate was open for the theories of complex, self-organizing systems to thrive and, by way of consequence, for AI(2) to emerge.fascination with modelsHerbert Simon (1916–2001), winner of the Nobel Prize in economics for his work on what he called "bounded rationality," is generally considered to be one [End Page 2] Click for larger view View full resolutionFigure 1.of the founders of artificial intelligence. Some cognitive scientists still rely on the computer program that he devised with Alan Newell to simulate the creative processes of human thought—a program aptly named "General Problem Solver." The title of one of his principal works, The Sciences of the... (shrink)

The common view that the notion of a Turing machine is directly relevant to AI is criticised. It is argued that computers are the result of a convergence of two strands of development with a long history: development of machines for automating various physical processes and machines for performing abstract operations on abstract entities, e.g. doing numerical calculations. Various aspects of these developments are analysed, along with their relevance to AI, and the similarities between computers viewed in this way (...) and animal brains. This comparison depends on a number of distinctions: between energy requirements and information requirements of machines, between ballistic and online control, between internal and external operations, and between various kinds of autonomy and self-awareness. The ideas are all intuitively familiar to software engineers, though rarely made fully explicit. Most of this has nothing to do with Turing machines or most of the mathematical theory of computation. But it has everything to do with both the scientific task of understanding, modelling or replicating human or animal intelligence and the engineering applications of AI, as well as other applications of computers. (shrink)

The first part (“Is there a future?”), discusses the idea of the future in the context of Carl Schmitt’s vision for the spatial revolutions of modernity, and then the idea of Anthropocene, as a synonym for an environmental crisis endangering the very survival of humankind. From this point of view, the conquest of space and the colonization of Mars at the center of futuristic and technocratic visions appear to be an attempt to escape from human responsibilities on Earth. The second (...) part (“AI and other hyperobjects”) discusses the extent of intellectual hubris expressed in computation, AI (Garvin Minsky e Ray Kurzweil), and the philosophy of computing and information (Eric Fredkin), involved in the elaboration of new theoretical assessments on the ultimate nature of reality. Their vision is then contrasted and made to interact with that of philosopher Timothy Morton. He has taken the perspective of global warming and the possibility of ecological catastrophe seriously, avoiding all the futuristic enthusiasms and instead emphasizing the radical nature of the transformations that humans experience in the present. In this perspective, AI becomes one of the “hyperobjects,” like the Internet or climate change, in which humans are immersed. Morton’s hyperobjects delineate an uncanny view of the future; this uncanniness is not related to the supernatural but to the environment. The third section (“More-than-human-humanism”) further reflects on the “uncanniness” that human perceive in the encounters with the manifestations of hyperobjects. It also seeks to understand the human position in the face of the radical technological transformations induced by cybernetics and AI. This section discusses Anti-humanism, Transhumanism, and Posthumanism within the broader category of more-than-human thought, which seems to be a more appropriate term to clarify the possible misunderstandings induced by the word “posthuman” and “transhuman.” The central question is not to empower (Transhumanism) or disempower (Posthumanism) humans, but to see them in relation to what is not human, including other animals, the environment, and the machine. The analysis considers the works of Cary Wolfe, Jane Bennet, Bryant Levi, among others, and introduces ethical debates on cyborgs, robots, and Autonomous weapons systems (AWS). The fourth section (“Ethical Perspectives”) continues this inquiry, concentrating on the non-standard ethical theories of Luciano Floridi (Computer and Information Ethics) and David Gunkel (The question of the Machine). It examines the opportunity and feasibility of including in the discussion on the ethics of our time - characterized by the pervasiveness of AI - the notions of consciousness as theorized by Emmanuel Levinas’s Humanism of the Other and Paul Ricoeur’s Oneself as Another. Finally, the last section (“The time of the end?”) reflects on how the hyperobject, Anthropocene, re-establishes a sense of limits in human history and confirms the special responsibility of human beings, and supports the need for a more-than-human-humanism. The latter, in other words, means intertwining ourselves with a unique ecosystem which cannot be overlooked and which restores meaning to our relationship with the past, present, and future. The awareness of the current challenges of technology can and must express itself in different forms of resistance to the adverse effects of AI in our lives. The ethical approach based on the persisting role of human consciousness is essential, but it must be coupled with human decision-making and political action. (shrink)

The field of Artificial Intelligence (AI) has undergone many transformations, most recently the emergence of data-driven approaches centred on machine learning technology. The present article examines that paradigm shift by using the conceptual tools developed by Thomas Kuhn, and by analysing the contents of the longest running conference series in the field. A paradigm shift occurs when a new set of assumptions and values replaces the previous one within a given scientific community. These are often conveyed implicitly, by (...) the choice of success stories that exemplify and define what a given field of research is about, demonstrating what kind of questions and answers are appropriate. The replacement of these exemplar stories corresponds to a shift in goals, methods, and expectations. We discuss the most recent such transition in the field of Artificial Intelligence, as well as commenting on some earlier ones. (shrink)