Radical democratic thinking is becoming intrigued by the material situatedness of its political agents and by the role of nonhuman participants in political interaction. At stake here is the displacement of narrow anthropocentrism that currently guides democratic theory and practice, and its repositioning into what we call ‘the nonhuman condition’. This Critical Exchange explores the nonhuman condition. It asks: What are the implications of decentering the human subject via a new materialist reading of radical democracy? Does this reading dilute (...) political agency? Or should this be seen, on the contrary, as an invitation for new voices and demands to enter into democratic assemblages? How might engagement with the more-than-human disrupt or extend theories of radical democracy? Hans Asenbaum and Amanda Machin discuss the human democratic subject both through radical democratic and new materialist lenses. They suggest that a new materialist decentred subject does not lose agency but further gains political responsibility in radical democracy. Jean-Paul Gagnon highlights the possibility of becoming through loss. In losing our anthropocentric arrogance—our understanding of being other and better than animal—we become more connected and discover our place in human-nonhuman assemblages. Melissa Orlie agrees that something can be gained through renouncing dominant human fantasies and presents a ‘radical democratic naturalism’. By renouncing land violence and acknowledging nature’s subjectivity, we can further nonexploitative radical democratic politics. This is where the notion of ‘tidalectic’ processes introduced by James L. Smith is helpful. Focusing on water and the Indigenous knowledges around rivers, he considers how democracy and community can be reimagined as plural, cyclical and attentive to the nonhuman with which the human is inevitably entangled. Diana Leong offers a different approach. Considering the nonhuman from the perspective of Black Studies demands an awareness to how blackness has long been constituted as non- or partially human. (shrink)

In ‘Computing Machinery and Intelligence’, Turing, sceptical of the question ‘Can machines think?’, quickly replaces it with an experimentally verifiable test: the imitation game. I suggest that for such a move to be successful the test needs to be relevant, expansive, solvable by exemplars, unpredictable, and lead to actionable research. The Imitation Game is only partially successful in this regard and its reliance on language, whilst insightful for partially solving the problem, has put AI progress on the wrong foot, (...) prescribing a top-down approach for building thinking machines. I argue that to fix shortcomings with modern AI systems a nonverbal operationalisation is required. This is provided by the recent Animal-AI Testbed, which translates animal cognition tests for AI and provides a bottom-up research pathway for building thinking machines that create predictive models of their environment from sensory input. (shrink)

In Malicious Deceivers, Ioana B. Jucan traces a genealogy of post-truth intimately tied to globalizing modernity and connects the production of repeatable fakeness with capitalism and Cartesian metaphysics. Through case studies that cross times and geographies, the book unpacks the notion of fakeness through the related logics of dissimulation (deception) and simulation (performativity) as seen with software/AI, television, plastics, and the internet. Specifically, Jucan shows how these (dis)simulation machines and performative objects construct impoverished pictures of the world, ensuring a (...) repeatable sameness through processes of hollowing out embodied histories and lived experience. Through both its methodology and its subjects-objects of study, the book further seeks ways to counter the abstracting mode of thinking and the processes of voiding performed by the twinning of Cartesian metaphysics and global capitalism. Enacting a model of creative scholarship rooted in the tradition of writing as performance, Jucan, a multimedia performance-maker and theatre director, uses the embodied "I" as a framing and situating device for the book and its sites of investigation. In this way, she aims to counter the Cartesian voiding of the thinking "I" and to enact a different kind of relationship between self and world from the one posited by Descartes and replayed in much Western philosophical and - more broadly - academic writing: a relationship of separation that situates the "I" on a pedestal of abstraction that voids it of its embodied histories and fails to account for its positionality within a socio-historical context and the operations of power that define it. (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)

Leibniz dit souvent que toutes les substances individuelles et seulement les substances individuelles peuvent être agents moraux authentiques. Mais, dans un passage fascinant des "Nouveaux essais", Leibniz semble concéder à John Locke que si les machines pensantes existaient, elles pourraient être des agents moraux authentiques. Ce serait une concession très significative considérant le fait que pour Leibniz les machines ne sont pas du tout des substances authentiques, mais de simple agrégats de matière. En général, les érudits ont interprété (...) ce passage comme un moyen pour Leibniz d'affirmer que l'identité métaphysique à long terme n'est pas une condition nécessaire pour l'identité morale à long terme. Mais, dans cette étude, je vais soutenir que Leibniz ne concède réellement pas à Locke que les machines pensantes pourraient être des agents moraux. (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)

A book Review of Artificial Knowing Gender and the Thinking Machine, by Alison Adam, Routledge: Taylor and Francis, 1998.

This chapter contains sections titled: The Philosophical Tale.

Artificial Knowing challenges the masculine slant in the Artificial Intelligence (AI) view of the world. Alison Adam admirably fills the large gap in science and technology studies by showing us that gender bias is inscribed in AI-based computer systems. Her treatment of feminist epistemology, focusing on the ideas of the knowing subject, the nature of knowledge, rationality and language, are bound to make a significant and powerful contribution to AI studies. Drawing from theories by Donna Haraway and Sherry Turkle, and (...) using tools of feminist epistemology, Adam provides a sustained critique of AI which interestingly re-enforces many of the traditional criticisms of the AI project. Artificial Knowing is an esential read for those interested in gender studies, science and technology studies, and philosophical debates in AI. (shrink)

_Artificial Knowing_ challenges the masculine slant in the Artificial Intelligence view of the world. Alison Adam admirably fills the large gap in science and technology studies by showing us that gender bias is inscribed in AI-based computer systems. Her treatment of feminist epistemology, focusing on the ideas of the knowing subject, the nature of knowledge, rationality and language, are bound to make a significant and powerful contribution to AI studies. Drawing from theories by Donna Haraway and Sherry Turkle, and using (...) tools of feminist epistemology, Adam provides a sustained critique of AI which interestingly re-enforces many of the traditional criticisms of the AI project. _Artificial Knowing_ is an esential read for those interested in gender studies, science and technology studies, and philosophical debates in AI. (shrink)

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and (...) class='Hi'>thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models. (shrink)

In the opening paragraph of “Computing Machinery and Intelligence” Alan Turing famously notes that “if the meaning of the words ‘machine’ and ‘think’ are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, ‘Can machines think?’ is to be sought in a statistical survey such as a Gallup poll.” He then immediately responds, “But this is absurd.” But why is this absurd, if (...) indeed it is? We think that the suggested method is absurd because the answer to the question might not follow from the meaning of the words alone—it might critically depend on what machines can be built to do. Further, the ordinary use of the terms might display shallow biases or superstitions that we want to set aside, or overcome, in pursuing a science or philosophy of mind. However, we do not think that the method is absurd insofar as we are interested in getting clear on “the normal use of the words,” as Turing puts it. And we believe that getting clear on the normal use of words like “machine” and “think” is relevant even if we then want to move beyond the ordinary usage in our theorizing. But the best way to figure out how ordinary people use language is via empirical investigation. (shrink)

From his preliminary analysis in 1965, Hubert Dreyfus projected a future much different than those with which his contemporaries were practically concerned, tempering their optimism in realizing something like human intelligence through conventional methods. At that time, he advised that there was nothing “directly” to be done toward machines with human-like intelligence, and that practical research should aim at a symbiosis between human beings and computers with computers doing what they do best, processing discrete symbols in formally structured problem (...) domains. Fast-forward five decades, and his emphasis on the difference between two essential modes of processing, the unconscious yet purposeful mode fundamental to situated human cognition, and the “minded” sense of conscious processing characterizing symbolic reasoning that seems to lend itself to explicit programming, continues into the famous Dreyfus–McDowell debate. The present memorial reviews Dreyfus’ early projections, asking if the fears that punctuate current popular commentary on AI are warranted, and in light of these if he would deliver similar practical advice to researchers today. (shrink)

Mr. A. M. Turing was quoted in The Times about a year ago as saying it would be interesting to discover the degree of intellectual activity of which a machine was capable and to what extent it could think for itself. He has now pressed this suggestion further and given the results of his researches in an article called “Computing Machines and Intelligence,” together with a brief account of a “child-machine” which he has attempted to educate . I intend (...) to discuss this article in some detail and examine his claim that “machines can think.”. (shrink)

Turing’s much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why did Turing refer to (...) gender imitation in a test for machine intelligence? In this article, I shall address these questions by unveiling social, historical and epistemological roots of the so-called Turing test. I will draw attention to a historical fact that has been only scarcely observed in the secondary literature thus far, namely that Turing’s 1950 test emerged out of a controversy over the cognitive capabilities of digital computers, most notably out of debates with physicist and computer pioneer Douglas Hartree, chemist and philosopher Michael Polanyi, and neurosurgeon Geoffrey Jefferson. Seen in its historical context, Turing’s 1950 paper can be understood as essentially a reply to a series of challenges posed to him by these thinkers arguing against his view that machines can think. Turing did propose gender learning and imitation as one of his various imitation tests for machine intelligence, and I argue here that this was done in response to Jefferson's suggestion that gendered behavior is causally related to the physiology of sex hormones. (shrink)

Can computers think? This book is intended to demonstrate that thinking, understanding, and intelligence are more than simply the execution of algorithms--that is, that machines cannot think. Written and edited by leaders in the fields of artificial intelligence and the philosophy of computing.

The dynamic development of technology, as well as of AI’s, raises the fundamental question: can machines think? At the beginning of the 20th Century the Austrian philosopher wonders about this problem and analyses it from a logical-philosophical conception which conveys the need to recognize all the elements comprised in the thought-language relationship. An ethical-religious dimension is revealed in certain uses of language. This dimension, which shows the human’s attitude to the world, stems from the human tendency to search for (...) explanations. This attitude does not extend to machines. (shrink)

Over three decades ago, in a brief but provocative essay, Paul Ziff argued for the thesis that robots cannot have feelings because they are "mechanisms, not organisms, not living creatures. There could be a broken-down robot but not a dead one. Only living creatures can literally have feelings."[i] Since machines are not living things they cannot have feelings. In the first half of my paper I review Ziff's arguments against the idea that robots could be conscious, especially his appeal (...) to our linguistic usage. In the second half of the essay I try to provide a deeper ontological understanding of why we ought not attribute minds to nonliving artifacts. I argue that inanimate mechanisms are incapable of genuinely active and purposive behavior. They are importantly different in kind from living human beings and animals. (shrink)

Lake et al. propose three criteria which, they argue, will bring artificial intelligence (AI) systems closer to human cognitive abilities. In this paper, we explore the application of these criteria to a particular domain of human cognition: our capacity for moral reasoning. In doing so, we explore a set of considerations relevant to the development of AI moral decision-making. Our main focus is on the relation between dual-process accounts of moral reasoning and model-free/model-based forms of machine learning. We also discuss (...) how work in embodied and situated cognition could provide a valu- able perspective on future research. (shrink)

The claims that Dr. F. H. George makes on behalf of his machines are obscurely stated. Does he claim that a machine has been made and has actually produced a kind of response which is incalculable, given the specification to which it has been built and also the prescribed conditions, what is put in for the particular performance in question? “Incalculable” does not mean that nobody has bothered to calculate, but that somebody has bothered, that the calculations show that (...) the response in question is not among the calculated ones, that it is not chance or mechanical breakdown, but a specific response to something specific among the given conditions. If he could substantiate this claim, then I would reluctantly agree that some machines are cleverer and more thoughtful than I thought possible, and some men sillier and less thoughtful, for they deliberately design machines to do they know not what. (shrink)

This paper revisits the often debated question Can machines think? It is argued that the usual identification of machines with the notion of algorithm has been both counter-intuitive and counter-productive. This is based on the fact that the notion of algorithm just requires an algorithm to contain a finite but arbitrary number of rules. It is argued that intuitively people tend to think of an algorithm to have a rather limited number of rules. The paper will further propose (...) a modification of the above mentioned explication of the notion of machines by quantifying the length of an algorithm. Based on that it appears possible to reconcile the opposing views on the topic, which people have been arguing about for more than half a century. (shrink)

This question as to whether machines can, or could, be made to think, has become familiar in recent years since the renewed outburst of interest that has taken place in the development of Cybernetics. The notion of servo–mechanisms and the like has a history in remote antiquity but the form of its fundamental question has recently taken on a new and especially acute significance.

Professors A. D. Ritchie and F. H. George both miss the question: What is the use of saying that machines think? Does the extension in the use of the word “think” to cover the complicated activity of modern machines enable us to explain better the nature of such activity? Extensions in usage are common and necessary in science.

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: opacity as intentional corporate or state (...) secrecy, opacity as technical illiteracy, and an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. (shrink)

Heidegger's treatment of 'machination' in the Beiträge zur Philosophie begins the critique of technological thinking that would centrally characterize his later work. Unlike later discussions of technology, the critique of machination in Beiträge connects its arising to the predominance of 'lived-experience' ( Erlebnis ) as the concealed basis for the possibility of a pre-delineated, rule-based metaphysical understanding of the world. In this essay I explore this connection. The unity of machination and lived-experience becomes intelligible when both are traced to (...) their common root in the primordial Greek attitude of techne , originally a basic attitude of wondering knowledge of nature. But with this common root revealed, the basic connection between machination and lived-experience also emerges as an important development of one of the deepest guiding thoughts of the Western philosophical tradition: the Parmenidean assertion of the sameness of being and thinking. In the Beiträge 's analysis of machination and lived-experience, Heidegger hopes to discover a way of thinking that avoids the Western tradition's constant basic assumption of self-identity, an assumption which culminates in the modern picture of the autonomous, self-identical subject aggressively set over against a pre-delineated world of objects in a relationship of mutual confrontation. In the final section, I investigate an important and illuminating parallel to Heidegger's result: the consideration of the relationship between experience and technological ways of thinking that forms the basis of the late Wittgenstein's famous rule-following considerations. (shrink)

The article attempts to locate the role of the computer in the long-standing conflict between the humanities on one side and the hard sciences and mathematics on the other. The state-sponsored promotion of philosophy and its subsequent demotion of scientific explanations provoked a scientific counter-attack, in the course of which psychophysical research subjected the human perception apparatus to rigorous investigations that all but mechanized the faculties of human understanding that were so central to the aspirations of philosophers. The latter retaliated (...) either by creating realms such as Husserl’s phenomenological ‘life-world’ that preceded, and hence were immune to, psychophysical explanations of sensation and perception, or by universalizing the faculty of understanding in such a way as to ensure the importance and competence of philosophy (e.g., in the work of the early Heidegger). This ongoing antagonism was also present in the ways in which the parties treated media technology: Philosophy tried to constrain media by conceptualizing them as obedient instruments tools at the beck and call of their users, whereas psychophysical research modeled the human mind on the very media that were indispensable for accessing this mind in the first place. The former saw media as ‘handy’ tools, the latter proceeded to make them the very measure of man. Alan Turing’s universal machine puts an end to this struggle: Not only does it blur the distinction between instructions and data, it converts the materials of the real world, the mechanisms of the mind, and the principal bastion of philosophy, natural languages, into numbers. (shrink)