According to the conventional wisdom, Turing said that computing machines can be intelligent. I don't believe it. I think that what Turing really said was that computing machines –- computers limited to computing –- can only fake intelligence. If we want computers to become genuinelyintelligent, we will have to give them enough “initiative” to do more than compute. In this paper, I want to try to develop this idea. I want to explain how giving computers more ``initiative'' can (...) allow them to do more than compute. And I want to say why I believe that they will have to go beyond computation before they can become genuinely intelligent. (shrink)

According to the conventional wisdom, Turing (1950) said that computing machines can be intelligent. I don't believe it. I think that what Turing really said was that computing machines –- computers limited to computing –- can only fake intelligence. If we want computers to become genuinelyintelligent, we will have to give them enough “initiative” (Turing, 1948, p. 21) to do more than compute. In this paper, I want to try to develop this idea. I want to explain how (...) giving computers more ``initiative'' can allow them to do more than compute. And I want to say why I believe (and believe that Turing believed) that they will have to go beyond computation before they can become genuinely intelligent. (shrink)

Is ethics a computable function? Can machines learn ethics like humans do? If teaching consists in no more than programming, training, indoctrinating… and if ethics is merely following a code of conduct, then yes, we can teach ethics to algorithmic machines. But if ethics is not merely about following a code of conduct or about imitating the behavior of others, then an approach based on computing outcomes, and on the reduction of ethics to the compilation and application of (...) a set of rules, either a priori or learned, misses the point. Our intention is not to solve the technical problem of machine ethics, but to learn something about human ethics, and its rationality, by reflecting on the ethics that can and should be implemented in machines. Any machine ethics implementation will have to face a number of fundamental or conceptual problems, which in the end refer to philosophical questions, such as: what is a human being (or more generally, what is a worthy being); what is human intentional acting; and how are intentional actions and their consequences morally evaluated. We are convinced that a proper understanding of ethical issues in AI can teach us something valuable about ourselves, and what it means to lead a free and responsible ethical life, that is, being good people beyond merely “following a moral code”. In the end we believe that rationality must be seen to involve more than just computing, and that value rationality is beyond numbers. Such an understanding is a required step to recovering a renewed rationality of ethics, one that is urgently needed in our highly technified society. (shrink)

Autonomous systems are machines that can alter their behavior without direct human oversight or control. How ought we to program them to behave? A plausible starting point is given by the Reduction to Acts Thesis, according to which we ought to program autonomous systems to do whatever a human agent ought to do in the same circumstances. Although the Reduction to Acts Thesis is initially appealing, we argue that it is false: it is sometimes permissible to program a machine (...) to do something that it would be wrong for a human to do. We advance two main arguments for this claim. First, the way an autonomous system will behave can be known in advance. This knowledge can indirectly affect the behavior of other agents, while the same may not be true at the time the system actually executes its programming. Second, a lack of knowledge of the identities of the victims and beneficiaries can provide a justification during the programming phase that would be unavailable to an agent at the time the autonomous system executes its programming. (shrink)

Democracy can be a means to independently valuable ends and/or it can be intrinsically (or non-instrumentally) valuable. One powerful non-instrumental defence of democracy is based on the idea that only it can publicly justify political authority. I contend that this is an argument about the reasonable acceptability of political authority and about the requirements of publicity and that satisfying these requirements has nothing to do with whether a society is democratic or not. Democracy, then, plays no role in publicly justifying (...) political authority. I also show that any non-instrumental defence of democracy must make claims about what justice requires and make several further claims that require substantial justification. (shrink)

This article argues against the claim that democracy is a necessary condition of political legitimacy. Instead, I propose a weaker set of conditions. First, I explain the case for the necessity of democracy. This is that only democracy can address the ‘egalitarian challenge’, i.e. ‘if we are all equal, why should only some of us wield political power?’. I show that if democracy really is a necessary condition of political legitimacy, then (what I label) the problems of domestic justice and (...) of international legitimacy become intractable. I then argue that the egalitarian challenge is addressed where the requirements of (1) horizontal equality, (2) acceptable vertical inequality, and (3) publicity, are met and where (4) citizens have some institutionalized opportunity for a voice in decisions. I show that these conditions can be realized in non-democratic form and conclude by explaining how the four conditions can be employed to make the problems of domestic justice and of international legitimacy more tractable. Overall, my ambitions are limited. I do not offer an all-things-considered case against democracy but I do show that (some) forms of non-democratic government are permissible. (shrink)

This is a comment on Tihamér Margitay’s “From Epistemology to Ontology,” where he criticizes Polanyi’s claim that there is a systematic correspondence between the levels of ontology and the levels of tacit knowing. Margitay contends that Polanyi supports this correspondence by appealing to a “purely ontological argument,” one which concludes that it is impossible to reduce machines to a singular, chemical-physical type, and criticizes this claim by pointing to industrial standards (machines that do reduce to singular physical-chemical type). (...) I respond to Margitay’s claim by distinguishing two different “purely ontological arguments” in Polanyi’s thought (one relying on the multi-realizability of a machine in different physical-chemical types, the other pointing to the inability of a purely physical-chemical ontology to account for the artificial shaping and functioning of machines). With these two arguments clarified, Margitay’s criticism by appealing to industrial standards loses much of its initial force. (shrink)

This is a comment on Tihamér Margitay’s “From Epistemology to Ontology,” where he criticizes Polanyi’s claim that there is a systematic correspondence between the levels of ontology and the levels of tacit knowing. Margitay contends that Polanyi supports this correspondence by appealing to a “purely ontological argument,” one which concludes that it is impossible to reduce machines to a singular, chemical-physical type, and criticizes this claim by pointing to industrial standards (machines that do reduce to singular physical-chemical type). (...) I respond to Margitay’s claim by distinguishing two different “purely ontological arguments” in Polanyi’s thought (one relying on the multi-realizability of a machine in different physical-chemical types, the other pointing to the inability of a purely physical-chemical ontology to account for the artificial shaping and functioning of machines). With these two arguments clarified, Margitay’s criticism by appealing to industrial standards loses much of its initial force. (shrink)

This paper argues that machine learning and epidemiology are on collision course over causation. The discipline of epidemiology lays great emphasis on causation, while ML research does not. Some epidemiologists have proposed imposing what amounts to a causal constraint on ML in epidemiology, requiring it either to engage in causal inference or restrict itself to mere projection. We whittle down the issues to the question of whether causal knowledge is necessary for underwriting predictions about the outcomes of public health interventions. (...) While there is great plausibility to the idea that it is, conviction that something is impossible does not by itself motivate a constraint to forbid trying. We disambiguate the possible motivations for such a constraint into definitional, metaphysical, epistemological, and pragmatic considerations and argue that “Proceed with caution” is the outcome of each. We then argue that there are positive reasons to proceed, albeit cautiously. Causal inference enforces existing classification schema prior to the testing of associational claims, but associations and classification schema are more plausibly discovered in a back-and-forth process of gaining reflective equilibrium. ML instantiates this kind of process, we argue, and thus offers the welcome prospect of uncovering meaningful new concepts in epidemiology and public health—provided it is not causally constrained. (shrink)

As machines take over more tasks previously done by humans, artistic creation is also considered as a candidate to be automated. But, can machines create art? This paper offers a conceptual framework for a philosophical discussion of this question regarding the status of machine art and machine creativity. It breaks the main question down in three sub-questions, and then analyses each question in order to arrive at more precise problems with regard to machine art and machine creativity: What (...) is art creation? What do we mean by art? And, what do we mean by machines create art? This then provides criteria we can use to discuss the main question in relation to particular cases. In the course of the analysis, the paper engages with theory in aesthetics, refers to literature on computational creativity, and contributes to the philosophy of technology and philosophical anthropology by reflecting on the role of technology in art creation. It is shown that the distinctions between process versus outcome criteria and subjective versus objective criteria of creativity are unstable. It is also argued that we should consider non-human forms of creativity, and not only cases where either humans or machines create art but also collaborations between humans and machines, which makes us reflect on human-technology relations. Finally, the paper questions the very approach that seeks criteria and suggests that the artistic status of machines may be shown and revealed in the human/non-human encounter before any theorizing or agreement takes place; an experience which then is presupposed when we theorize. This hints at a more general model of what happens in artistic perception and engagement as a hybrid human-technological and emergent or even poetic process, a model which leaves more room for letting ourselves be surprised by creativity—human and perhaps non-human. (shrink)

Much more than their machinic reality, current iterations of AI rely on imagined divisions of human and non-human properties and skills that have genealogical ties to colonization. For this reason, research efforts have recently been made to historicize these imaginaries, connecting them to colonial ideals that delegate black and brown colonized people into the realm of the non-human. Atanasoski and Vora (Surrogate humanity. Race, robots and the politics of technological futures, Duke, Durham and London, 2019) have called this a “surrogate (...) humanity”, where narratives of autonomous technologies function to disappear precisely the formerly colonized peoples that are enveloped in its production process. At the same time, the gendered and racialized roots of this maternal figure represents an opportunity to uncover and critique the invisibilization of embodied resources necessary to produce AI, precarious bodies labouring to produce algorithmic infrastructures in a manner that can be considered in a genealogy of carework and reproduction. These genealogies complicate the detachment suggested by the surrogate figure and go beyond it to proclaim a more generative function of the relationship between the black maternal figure and AI. The article analyses Tabita Rezaire’s multi-media artwork Sugar Walls Teardom to think through decolonial and queer renderings of the black female bodies upon which technological imaginaries rest, to extend beyond AI surrogacy and towards notions of kinship, care and world-making by producing an AI aesthetics that is relational, embodied, and celebratory of other ways of liveness. (shrink)

There is a widely held view that visual representations (images) do not depict negation, for example, as expressed by the sentence, “the train is not coming.” The present study focuses on the real-world visual representations of photographs and comic (manga) illustrations and empirically challenges the question of whether humans and machines, that is, modern deep neural networks, can recognize visual representations as expressing negation. By collecting data on the captions humans gave to images and analyzing the occurrences of negation (...) phrases, we show some evidence that humans recognize certain images as expressing negation. Furthermore, based on this finding, we examined whether or not humans and machines can classify novel images as expressing negation. The humans were able to correctly classify images to some extent, as expected from the analysis of the image captions. On the other hand, the machine learning model of image processing was only able to perform this classification at about the chance level, not at the same level of performance as the human. Based on these results, we discuss what makes humans capable of recognizing negation in visual representations, highlighting the role of the background commonsense knowledge that humans can exploit. Comparing human and machine learning performances suggests new ways to understand human cognitive abilities and to build artificial intelligence systems with more human-like abilities to understand logical concepts. (shrink)

Robert Nozick famously asks us whether we would plug in to an experience machine, or whether we would insist upon ‘living in contact with reality’. Felipe De Brigard, after conducting a series of empirical ‘inverted’ experience machine studies, suggests that this is a false dilemma. Rather, he says, ’…the fact is that people tend to prefer the state of affairs they are in currently,’ or the status quo. In this paper, I argue that these studies are a test case for (...) ‘experimental philosophy’ as such. Specifically, I argue that De Brigard offers a series of faulty studies, and so, reaches unfounded conclusions. More generally, I argue that certain philosophical thought experiments cannot be tested empirically at all, and this limits what experimental philosophy can do. (shrink)

Some defenders of so-called `artificial intelligence' believe that machines can understand language. In particular, Søgaard has argued in his "Understanding models understanding language" (2022) for a thesis of this sort. His idea is that (1) where there is semantics there is also understanding and (2) machines are not only capable of what he calls `inferential semantics', but even that they can (with the help of inputs from sensors) `learn' referential semantics. We show that he goes wrong because he (...) pays insufficient attention to the difference between language as used by humans and the sequences of inert symbols which arise when language is stored on hard drives or in books in libraries. (shrink)

Machine learning (ML) has been praised as a tool that can advance science and knowledge in radical ways. However, it is not clear exactly how radical are the novelties that ML generates. In this article, I argue that this question can only be answered contextually, because outputs generated by ML have to be evaluated on the basis of the theory of the science to which ML is applied. In particular, I analyze the problem of novelty of ML outputs in the (...) context of molecular biology. In order to do this, I first clarify the nature of the models generated by ML. Next, I distinguish three ways in which a model can be novel (from the weakest to the strongest). Third, I dissect the way ML algorithms work and generate models in molecular biology and genomics. On these bases, I argue that ML is either a tool to identify instances of knowledge already present and codified, or to generate models that are novel in a weak sense. The notable contribution of ML to scientific discovery in the context of biology is that it can aid humans in overcoming potential bias by exploring more systematically the space of possible hypotheses implied by a theory. (shrink)

This article will rework the classical question ‘Can a machine think?’ into a more specific problem: ‘Can a machine think anything new?’ It will consider traditional computational tasks such as prediction and decision-making, so as to investigate whether the instrumentality of these operations can be understood in terms of the creation of novel thought. By addressing philosophical and technoscientific attempts to mechanise thought on the one hand, and the philosophical and cultural critique of these attempts on the other, I will (...) argue that computation’s epistemic productions should be assessed vis-à-vis the logico-mathematical specificity of formal axiomatic systems. Such an assessment requires us to conceive automated modes of thought in such a way as to supersede the hope that machines might replicate human cognitive faculties, and to thereby acknowledge a form of onto-epistemological autonomy in automated ‘thinking’ processes. This involves moving beyond the view that machines might merely simulate humans. Machine thought should be seen as dramatically alien to human thought, and to the dimension of lived experience upon which the latter is predicated. Having stepped outside the simulative paradigm, the question ‘Can a machine think anything new?’ can then be reformulated. One should ask whether novel behaviour in computing might come not from the breaking of mechanical rules, but from following them: from doing what computers do already, and not what we might think they should be doing if we wanted them to imitate us. (shrink)

This article will rework the classical question ‘Can a machine think?’ into a more specific problem: ‘Can a machine think anything new?’ It will consider traditional computational tasks such as prediction and decision-making, so as to investigate whether the instrumentality of these operations can be understood in terms of the creation of novel thought. By addressing philosophical and technoscientific attempts to mechanise thought on the one hand, and the philosophical and cultural critique of these attempts on the other, I will (...) argue that computation’s epistemic productions should be assessed vis-à-vis the logico-mathematical specificity of formal axiomatic systems. Such an assessment requires us to conceive automated modes of thought in such a way as to supersede the hope that machines might replicate human cognitive faculties, and to thereby acknowledge a form of onto-epistemological autonomy in automated ‘thinking’ processes. This involves moving beyond the view that machines might merely simulate humans. Machine thought should be seen as dramatically alien to human thought, and to the dimension of lived experience upon which the latter is predicated. Having stepped outside the simulative paradigm, the question ‘Can a machine think anything new?’ can then be reformulated. One should ask whether novel behaviour in computing might come not from the breaking of mechanical rules, but from following them: from doing what computers do already, and not what we might think they should be doing if we wanted them to imitate us. (shrink)

In, “The Turing Triage Test”, published in Ethics and Information Technology, I described a hypothetical scenario, modelled on the famous Turing Test for machine intelligence, which might serve as means of testing whether or not machines had achieved the moral standing of people. In this paper, I: (1) explain why the Turing Triage Test is of vital interest in the context of contemporary debates about the ethics of AI; (2) address some issues that complexify the application of this test; (...) and, (3) in doing so, defend a way of thinking about the question of the moral standing of intelligent machines, which takes the idea of “seriousness” seriously. This last objective is, in fact, my primary one and is motivated by the sense that, to date, much of the “philosophy” of AI has suffered from a profound failure to properly distinguish between things that we can say and things that we can really mean. (shrink)

Through hypothetical scenarios, this paper analyses whether machine learning (ML) could resolve one of the main shortcomings present in Christopher Boorse’s Biostatistical Theory of health (BST). In doing so, it foregrounds the boundaries and challenges of employing ML in formulating a naturalist (i.e., prima facie value-free) definition of health. The paper argues that a sweeping dataist approach cannot fully make the BST truly naturalistic, as prior theories and values persist. It also points out that supervised learning introduces circularity, rendering it (...) incompatible with a naturalistic perspective. Additionally, it underscores the need for pre-existing auxiliary theories to assess results from unsupervised learning. It emphasizes the importance of understanding the epistemological entanglements between data and data processing methods to manage expectations about what data patterns can predict. In conclusion, the paper argues against delegating the final authority for defining complex concepts like health to AI systems, as it necessitates ethical judgment and capacities for deliberation that AI currently lacks. It also warns against granting creators and deployers of AI systems the discretionary authority to determine these definitions outside the wider social discussion, advocating for ongoing public engagement on normative notions. Failure to do so risks limiting individuals and collectives’ ability to shape a just digital future and diminishes their fundamental epistemic agency. (shrink)

Scientists and decision makers rely on climate models for predictions concerning future climate change. Traditionally, physical processes that are key to predicting extreme events are either directly represented or indirectly represented. Scientists are now replacing physically based parameterizations with neural networks that do not represent physical processes directly or indirectly. I analyze the epistemic implications of this method and argue that it undermines the reliability of model predictions. I attribute the widespread failure in neural network generalizability to the lack of (...) process representation. The representation of climate processes adds significant and irreducible value to the reliability of climate model predictions. (shrink)

The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for (...) unconceived alternatives providing modal knowledge of what is possible therein. As a result, the epistemic warrant of synthesised realist theories should emerge bolstered as the underdetermination by available evidence gets reduced. While shifting the realist commitment away from theoretical artefacts towards modalities of the possibility spaces, the synthesis comes out as a kind of perspectival modelling. (shrink)

I Would like to draw attention to the basic defect in the argument used by Mr J. R. Lucas.Mr Lucas there states that Gödel's theorem shows that any consistent formal system strong enough to produce arithmetic fails to prove, within its own structure, theorems that we, as humans, can nevertheless see to be true. From this he argues that ‘minds’ can do more than machines, since machines are essentially formal systems of this same type, and subject to the (...) limitation implied by Godel's theorem. (shrink)

The body conceived of as a machinic assemblage becomes a body that is multiple. Its function or meaning no longer depends on an interior truth or identity, but on the particular assemblages it forms with other bodies. In this paper I draw on the work of Deleuze and Guattari to explore what happens to the drug using body when it is rethought as a machinic assemblage. Following an exploration of how the body of the drug user is put together and (...) stratified as a subject, and a careful manoeuvre through the bleak conception of the ‘drugged body’ provided by Deleuze and Guattari, I begin to map out some ethical alternatives. I argue that a body should, ultimately, be valued for what it can do (rather than what is essentially ‘is’), and that assemblages should be assessed in relation to their enabling, or blocking, of a body’s potential to become other. (shrink)

This question might seem odd, but it is, nevertheless, directly relevant to our life today. My intention is to bring ancient Daoist philosophy into a conversation about the challenges that technology poses. Today, cutting-edge technologies do not exist just in research labs but have already easily penetrated all aspects of our lives. It is difficult to argue that we do not yet inhabit a world with Artificial Intelligence, for it has become a pervasive and effective technology woven into the fabric (...) of everyday living. (shrink)

This paper explores the role and resolution of disagreements between physicians and their diagnostic AI-based decision support systems. With an ever-growing number of applications for these independently operating diagnostic tools, it becomes less and less clear what a physician ought to do in case their diagnosis is in faultless conflict with the results of the DSS. The consequences of such uncertainty can ultimately lead to effects detrimental to the intended purpose of such machines, e.g. by shifting the burden of (...) proof towards a physician. Thus, we require normative clarity for integrating these machines without affecting established, trusted, and relied upon workflows. In reconstructing different causes of conflicts between physicians and their AI-based tools—inspired by the approach of “meaningful human control” over autonomous systems and the challenges to resolve them—we will delineate normative conditions for “meaningful disagreements”. These incorporate the potential of DSS to take on more tasks and outline how the moral responsibility of a physician can be preserved in an increasingly automated clinical work environment. (shrink)

This paper explores Church's Thesis and related claims madeby Turing. Church's Thesis concerns computable numerical functions, whileTuring's claims concern both procedures for manipulating uninterpreted marksand machines that generate the results that these procedures would yield. Itis argued that Turing's claims are true, and that they support (the truth of)Church's Thesis. It is further argued that the truth of Turing's and Church'sTheses has no interesting consequences for human cognition or cognitiveabilities. The Theses don't even mean that computers can do as (...) much as peoplecan when it comes to carrying out effective procedures. For carrying out aprocedure is a purposive, intentional activity. No actual machine does, orcan do, as much. (shrink)

In the past few years, machine learning (ML) tools have been implemented with success in the medical context. However, several practitioners have raised concerns about the lack of transparency—at the algorithmic level—of many of these tools; and solutions from the field of explainable AI (XAI) have been seen as a way to open the ‘black box’ and make the tools more trustworthy. Recently, Alex London has argued that in the medical context we do not need machine learning tools to be (...) interpretable at the algorithmic level to make them trustworthy, as long as they meet some strict empirical desiderata. In this paper, we analyse and develop London’s position. In particular, we make two claims. First, we claim that London’s solution to the problem of trust can potentially address another problem, which is how to evaluate the reliability of ML tools in medicine for regulatory purposes. Second, we claim that to deal with this problem, we need to develop London’s views by shifting the focus from the opacity of algorithmic details to the opacity of the way in which ML tools are trained and built. We claim that to regulate AI tools and evaluate their reliability, agencies need an explanation of how ML tools have been built, which requires documenting and justifying the technical choices that practitioners have made in designing such tools. This is because different algorithmic designs may lead to different outcomes, and to the realization of different purposes. However, given that technical choices underlying algorithmic design are shaped by value-laden considerations, opening the black box of the design process means also making transparent and motivating (technical and ethical) values and preferences behind such choices. Using tools from philosophy of technology and philosophy of science, we elaborate a framework showing how an explanation of the training processes of ML tools in medicine should look like. (shrink)

This essay addresses the question whether artificial speakers can perform speech acts in the technical sense of that term common in the philosophy of language. We here argue that under certain conditions artificial speakers can perform speech acts so understood. After explaining some of the issues at stake in these questions, we elucidate a relatively uncontroversial way in which machines can communicate, namely through what we call verbal signaling. But verbal signaling is not sufficient for the performance of a (...) speech act. To explain the difference, we elucidate the notion of a speech act developed by Austin in the mid-twentieth century and then discuss Strawson’s influential proposal for how that notion may be related to Grice’s conception of speaker meaning. We then refine Strawson’s synthesis in light of Armstrong’s reconceptualization of speaker meaning in terms of objectives rather than intentions. We next extend this conception of speech acts to the cases of recorded, proxy, and conditional speech acts. On this basis, we propose that a characteristic role for artificial speakers is as proxies in the performance of speech acts on behalf of their human creators. We also consider two objections to our position, and compare our approach with others: while other authors appeal to notions such as “quasi-assertion,” we offer a sharp characterization of what artificial speakers can do that does not impute intentions or similarly controversial powers to them. We conclude by raising doubts that our strategy can be applied to speech acts generally. (shrink)

There is an existing debate regarding the view that technological instruments, devices, or machines can assert ‎or testify. A standard view in epistemology is that only humans can testify. However, the notion of quasi-‎testimony acknowledges that technological devices can assert or testify under some conditions, without ‎denying that humans and machines are not the same. Indeed, there are four relevant differences between ‎humans and instruments. First, unlike humans, machine assertion is not imaginative or playful. Second, ‎machine assertion is (...) prescripted and context restricted. As such, computers currently cannot easily switch ‎contexts or make meaningful relevant assertions in contexts for which they were not programmed. Third, ‎while both humans and computers make errors, they do so in different ways. Computers are very sensitive to ‎small errors in input, which may cause them to make big errors in output. Moreover, automatic error control ‎is based on finding irregularities in data without trying to establish whether they make sense. Fourth, ‎testimony is produced by a human with moral worth, while quasi-testimony is not. Ultimately, the notion of ‎quasi-testimony can serve as a bridge between different philosophical fields that deal with instruments and ‎testimony as sources of knowledge, allowing them to converse and agree on a shared description of reality, ‎while maintaining their distinct conceptions and ontological commitments about knowledge, humans, and ‎nonhumans.‎. (shrink)

This paper offers practical advice about how to interact with machines that we have reason to believe could have minds. I argue that we should approach these interactions by assigning credences to judgements about whether the machines in question can think. We should treat the premises of philosophical arguments about whether these machines can think as offering evidence that may increase or reduce these credences. I describe two cases in which you should refrain from doing as your (...) favored philosophical view about thinking machines suggests. Even if you believe that machines are mindless, you should acknowledge that treating them as if they are mindless risks wronging them. Suppose your considered philosophical view that a machine has a mind leads you to consider dating it. You may have reason to regret that decision should these dates lead on to a life-long relationship with a mindless machine. In the paper’s final section, I suggest that building a machine that is capable of performing all intelligent human behavior should produce a general increase in confidence that machines can think. Any reasonable judge should count this feat as evidence in favor of machines having minds. This rational nudge could lead to broad acceptance of the idea that machines can think. (shrink)

I Would like to draw attention to the basic defect in the argument used by Mr J. R. Lucas.Mr Lucas there states that Gödel's theorem shows that any consistent formal system strong enough to produce arithmetic fails to prove, within its own structure, theorems that we, as humans, can nevertheless see to be true. From this he argues that ‘minds’ can do more than machines, since machines are essentially formal systems of this same type, and subject to the (...) limitation implied by Godel's theorem. (shrink)

What is a machine? What distinguishes a machine from a tool or a simple instrument—for example, a knife, a hammer, an ax, or a pencil? Tools are technical objects that can be seen as extending or continuing a bodily action. They augment its efficiency. To push, hit, tear, pierce, crush, grasp, or throw: tools and simple instruments allow us to do better what, to some extent, we can already do without them. They enhance our performance, make the action easier, more (...) precise, they push back the limits of what we can do. What is done with difficulty and imperfectly with our bare hands, teeth, or nails can be done better and more easily using a tool, or with the help of a stone, a leaf, a stick, or of any natural object... (shrink)

Abstract Of the many ways in which identity is constructed and performed online, few are as strongly ‘anchored’ to existing offline relationships as in online social networks like Facebook and Myspace. These networks utilise profiles that extend our practical, psychological and even corporeal identity in ways that give them considerable phenomenal presence in the lives of spatially distant people. This raises interesting questions about the persistence of identity when these online profiles survive the deaths of the users behind them, via (...) the practice of ‘memorialising’ social network profile pages. I situate these practices within a phenomenology of grief that accounts for the ways in which the dead can persist as moral patients, and show how online survival in this case illuminates an important difference between persons and selves within contemporary philosophy of personal identity. Ultimately, the online persistence of the dead helps bring into view a deep ontological contradiction implicit in our dealings with death: the dead both live on as objects of duty and yet completely cease to exist. Content Type Journal Article Category Special Issue Pages 1-17 DOI 10.1007/s13347-011-0050-7 Authors Patrick Stokes, Philosophy Group, School of Humanities, University of Hertfordshire de Havilland Campus, Hatfield, Hertfordshire AL10 9AB, UK Journal Philosophy & Technology Online ISSN 2210-5441 Print ISSN 2210-5433. (shrink)

Can machines simulate, express or even have emotions? Is it a good to build such machines? How do humans react emotionally to them and how should such devices be treated from a moral point of view? This volume addresses these and related questions by bringing together perspectives from affective computing and emotional human-machine interaction, combining technological approaches with those from the humanities and social sciences. It thus relates disciplines such as philosophy, computer science, technology, psychology, sociology, design, and (...) art. The volume offers readers interested in the phenomenon of emotional machines new perspectives from a variety of disciplines and addresses fundamental questions that will become pressing in the foreseeable future as emotional machines increasingly populate our everyday lives. (shrink)

Wendell Wallach and Colin Allen’s Moral Machines: Teaching Robots Right From Wrong (Oxford University Press, 2009) explores efforts to develop machines that, not only can be employed for good or bad ends, but which themselves can be held morally accountable for what they do— artificial moral agents (AMAs). This essay is a critical response to Wallach and Allen’s conjectures. Although Wallach and Allen do not suggest that we are close to being able to create full-fledged AMAs, they do (...) talk seriously about making incremental progress in the direction of creating them (even if we never fully succeed). However, there are important questions about the moral development of AMAs that Moral Machines does not address. Given the responsibilities entrusted to human moral agents, we take questions about their moral development very seriously. In the case of children, the hope is that eventually they will develop into full-fledged moral agents. How might we expect this to go with less than fully formed AMAs? Will there be a comparable story of moral development and moral education that we can tell? (shrink)

A key distinction in ethics is between members and nonmembers of the moral community. Over time, our notion of this community has expanded as we have moved from a rationality criterion to a sentience criterion for membership. I argue that a sentience criterion is insufficient to accommodate all members of the moral community; the true underlying criterion can be understood in terms of whether a being has interests. This may be extended to conscious, self-aware machines, as well as to (...) any autonomous intelligent machines. Such machines exhibit an ability to formulate desires for the course of their own existence; this gives them basic moral standing. While not all machines display autonomy, those which do must be treated as moral patients; to ignore their claims to moral recognition is to repeat past errors. I thus urge moral generosity with respect to the ethical claims of intelligent machines. (shrink)

Infinite machines (IMs) can do supertasks. A supertask is an infinite series of operations done in some finite time. Whether or not our universe contains any IMs, they are worthy of study as upper bounds on finite machines. We introduce IMs and describe some of their physical and psychological aspects. An accelerating Turing machine (an ATM) is a Turing machine that performs every next operation twice as fast. It can carry out infinitely many operations in finite time. Many (...) ATMs can be connected together to form networks of infinitely powerful agents. A network of ATMs can also be thought of as the control system for an infinitely complex robot. We describe a robot with a dense network of ATMs for its retinas, its brain, and its motor controllers. Such a robot can perform psychological supertasks - it can perceive infinitely detailed objects in all their detail; it can formulate infinite plans; it can make infinitely precise movements. An endless hierarchy of IMs might realize a deep notion of intelligent computing everywhere. (shrink)

This paper explores Church's Thesis and related claims madeby Turing. Church's Thesis concerns computable numerical functions, whileTuring's claims concern both procedures for manipulating uninterpreted marksand machines that generate the results that these procedures would yield. Itis argued that Turing's claims are true, and that they support (the truth of)Church's Thesis. It is further argued that the truth of Turing's and Church'sTheses has no interesting consequences for human cognition or cognitiveabilities. The Theses don't even mean that computers can do as (...) much as peoplecan when it comes to carrying out effective procedures. For carrying out aprocedure is a purposive, intentional activity. No actual machine does, orcan do, as much. (shrink)

Decisions based on algorithmic, machine learning models can be unfair, reproducing biases in historical data used to train them. While computational techniques are emerging to address aspects of these concerns through communities such as discrimination-aware data mining and fairness, accountability and transparency machine learning, their practical implementation faces real-world challenges. For legal, institutional or commercial reasons, organisations might not hold the data on sensitive attributes such as gender, ethnicity, sexuality or disability needed to diagnose and mitigate emergent indirect discrimination-by-proxy, such (...) as redlining. Such organisations might also lack the knowledge and capacity to identify and manage fairness issues that are emergent properties of complex sociotechnical systems. This paper presents and discusses three potential approaches to deal with such knowledge and information deficits in the context of fairer machine learning. Trusted third parties could selectively store data necessary for performing discrimination discovery and incorporating fairness constraints into model-building in a privacy-preserving manner. Collaborative online platforms would allow diverse organisations to record, share and access contextual and experiential knowledge to promote fairness in machine learning systems. Finally, unsupervised learning and pedagogically interpretable algorithms might allow fairness hypotheses to be built for further selective testing and exploration. Real-world fairness challenges in machine learning are not abstract, constrained optimisation problems, but are institutionally and contextually grounded. Computational fairness tools are useful, but must be researched and developed in and with the messy contexts that will shape their deployment, rather than just for imagined situations. Not doing so risks real, near-term algorithmic harm. (shrink)

What I refer to is how our thought in inventing, designing, modifying, and using machines carries over into acts we do not consciously associate with them—like writing or reading poetry. An airplane in flight may be “pure poetry,” or a Ferrari “a poem in steel”; it intrigues me to consider that beneath such object comparisons an object-of-thought connection may be made. Or in other words, there may be really something to a hackneyed compliment like “poem in steel.” My preference (...) for thought form over object form makes me less interested in machines that we can see than in those that we can’t, and it makes me direct my inquiry along two lines, concerning two questions about invisibility. The first involves the history of technology: How is it that machines “disappear”—become less visible, impinge less and less upon popular consciousness? The effect cannot be imputed entirely to familiarity. The second involves the history of literature: If machines have disappeared, are there “disappeared”—or “invisible”—machines in literature? It is reasonably clear that we go to considerable lengths to hide the machines that surround us and that we choose, or our artists choose, insofar as such choice can be located, to restrict the appearance of machines in art.3 Commonly their restricted appearance in art is understood as a kind of resistance to some form of machine takeover, while their concealment has only uncommonly received analysis and is not generally discussed in context with the matter of artistic representation. But we do accept, at least in theory, the idea that style of living and style in literature are connected. If the machine penetrates our style of living, then—and this is the end of my inquiry—these invisibilities are of interest to literary criticism, for they have something to do with the way literature is written, with whether or not, that is, writers choose to describe machines, use them as characters, or give them any role at all to play in surface structure. Since the artist often works to reveal what his society works to conceal and since the postmodern period has so far been one of crisis in the relations between society and technology, we may expect to find in postmodern fiction, or in writing to come, some greater revelation or simple exposure of the workings, than we could have seen before. 3. I use “restrict” in a statistical sense, for since the beginning of the Industrial Revolution there have been artists to celebrate the machine; today’s hyperrealist movement offers contemporary examples. These artists—Tom Blackwell, Ron Kleemann, Ralph Goings, and others—work a transformation like that of Andy Warhol with the pop object. They present not so much a celebration as an effort to turn looking into seeing, here directed at automobiles in particular, so much a part of everyday life as to have become indistinguishable from one another and as a species from the other parts of the semiurban landscape—the mailbox, the front lawn, the visiting relatives staring into the camera. Strother B. Purdy is the author of The Hole in the Fabric: Science, Contemporary Literature, and Henry James. His previous contributions to Critical Inquiry are “Stalingrad and My Lai: A Literary-Political Speculation” and “Reply to Lawrence W. Hyman. (shrink)

In this article, Lucas maintains the falseness of Mechanism - the attempt to explain minds as machines - by means of Incompleteness Theorem of Gödel. Gödel’s theorem shows that in any system consistent and adequate for simple arithmetic there are formulae which cannot be proved in the system but that human minds can recognize as true; Lucas points out in his turn that Gödel’s theorem applies to machines because a machine is the concrete instantiation of a formal system: (...) therefore, for every machine consistent and able of doing simple arithmetic, there is a formula that it can’t produce as true but that we can see to be true, and so human minds and machines have to be different. Lucas considers as well in this article some possible objections to his argument: for any Gödelian formula we could, for instance, construct a machine able to produce it or we could put the Gödelian formulae that we had proved as axioms of a further machine. However - as Lucas underlines - for every of such machines we could again formulate another Gödelian formula, the Gödelian formula of these machines, that they are not able to proof but that we can recognize as true. More general arguments, such as the possibility to escape Gödelian argument by suggesting that Gödel’s theorem applies to consistent systems while we could be inconsistent ones, are moreover refuted by Lucas by maintaining that our inconsistency corresponds to occasional malfunctioning of a machine and not to his normal inconsistency; indeed, a inconsistent machine is characterized by producing any statement, on the contrary human being are selective and not disposed to assert anything. (shrink)

In this paper, we look at the possibility of a machine having a sense of humour. In particular, we focus on actual machine utterances in Turing test discourses. In doing so, we do not consider the Turing test in depth and what this might mean for humanity, rather we merely look at cases in conversations when the output from a machine can be considered to be humorous. We link such outpourings with Turing’s “arguments from various disabilities” used against the concept (...) of a machine being able to think, taken from his seminal work of 1950. Finally we consider the role that humour might play in adding to the deception, integral to the Turing test, that a machine in practice appears to be a human. (shrink)

The aim of this chapter is to explore various ways of thinking about the concept of manipulation in order to capture both current and potentially future instances of machine manipulation, manipulation on the part of everything from the Facebook advertising algorithm to super-intelligent AGI. Three views are considered: a conservative one, which slightly tweaks extant influence-based theories of manipulation; a dismissive view according to which it doesn't matter much if machines are literally manipulative, provided we can classify them as (...) so doing to make sense of our interactions with them; and an ameliorative analysis, according to which we should change our concept of manipulation better to make sense of machine manipulation. We tentatively favor the latter. (shrink)

A wide range of expressions are predicable literally or primarily only of human beings and of creatures that behave like them. The English word 'mind' is connected primarily with the intellect and the will. To have a mind to do something is to be inclined or tempted to do it, and to have half a mind to do something is to be sorely tempted, perhaps against one's better judgement. Artificial‐intelligence scientists insist that they are already building machines that can (...) think and see, recognize and identify, make choices and decisions. Chess‐playing machines can 'beat' chess‐masters, and computers can 'calculate' far more efficiently and quickly than mathematicians. Machines, unlike living creatures, do not have a body, although they are bodies. Machines and their parts have purposes and functions: viz. those which they were designed to fulfil. (shrink)

Can we test philosophical thought experiments, such as whether people would enter an experience machine or would leave one once they are inside? Dan Weijers argues that since 'rational' subjects (e.g. students taking surveys in college classes) are believable, we can do so. By contrast, I argue that because such subjects will probably have the wrong affect (i.e. emotional states) when they are tested, such tests are almost worthless. Moreover, understood as a general policy, such pretend testing would ruin the (...) results of most psychological tests, such as those of helping behavior, attitudes to authority, moral transgressions, etc. However, I also argue that certain philosophical thought experiments do not require us to have as much (or any) affect to understand them, or to elicit intuitions, and so can be tested. Generally, experimental philosophy must adhere to this limit, on pain of offering vacuous results. (shrink)

Despite there being little consensus on what intelligence is or how to measure it, the media and the public have become increasingly preoccupied with the concept owing to recent accomplishments in machine learning and research on artificial intelligence (AI). Governments and corporations are investing billions of dollars to fund researchers who are keen to produce an ever‐expanding range of artificial intelligent systems. More than 30 countries have announced such research initiatives over the past 3 years 1. For example, the EU (...) Commission pledged to increase the investment in AI research to €1.5 billion by 2020 (from €500 million in 2017), while China has committed $2.1 billion towards an AI technology park in Beijing alone 1. This global investment in AI is astonishing and prompts several questions: What are the true possibilities and limitations of AI? What do AI researchers and developers mean by “intelligence”? How does this compare to the everyday concept of intelligence and how the term is other branches of cognitive science? And can machine learning produce anything that is truly “intelligent”? (shrink)

Rule-based ethical theories like Kant's appear to be promising for machine ethics because of the computational structure of their judgments. On one formalist interpretation of Kant's categorical imperative, for instance, a machine could place prospective actions into the traditional deontic categories (forbidden, permissible, obligatory) by a simple consistency test on the maxim of action. We might enhance this test by adding a declarative set of subsidiary maxims and other "buttressing" rules. The ethical judgment is then an outcome of the consistency (...) test. While this kind of test can generate results, it may be vacuous in the sense that it would do no more than forbid obviously contradictory maxims of action. It is also possible that the kind of inference in such a rule-based system may be non-monotonic. I discuss these challenges to a rule-based machine ethics, starting from the framework of Kantian ethics. (shrink)