Can Machines Think?

Book. From the Publisher. An influential scientist in the field of artificial intelligence (AI) explains its fundamental concepts and how it is changing culture and society. -/- A particular form of AI is now embedded in our tech, our infrastructure, and our lives. How did it get there? Where and why should we be concerned? And what should we do now? The Shortcut: Why Intelligent Machines Do Not Think Like Us provides an accessible yet probing exposure of AI in its (...) prevalent form today, proposing a new narrative to connect and make sense of events that have happened in the recent tumultuous past, and enabling us to think soberly about the road ahead. -/- This book is divided into ten carefully crafted and easily digestible chapters. Each chapter grapples with an important question for AI. Ranging from the scientific concepts that underpin the technology to wider implications for society, it develops a unified description using tools from different disciplines and avoiding unnecessary abstractions or words that end with -ism. The book uses real examples wherever possible, introducing the reader to the people who have created some of these technologies and to ideas shaping modern society that originate from the technical side of AI. It contains important practical advice about how we should approach AI in the future without promoting exaggerated hypes or fears. -/- Entertaining and disturbing but always thoughtful, The Shortcut confronts the hidden logic of AI while preserving a space for human dignity. It is essential reading for anyone with an interest in AI, the history of technology, and the history of ideas. General readers will come away much more informed about how AI really works today and what we should do next. -/- Table of Contents -/- ABOUT THE AUTHOR. PROLOGUE. 1 The Search for Intelligence. 2 The Shortcut. 3 Finding Order in the World. 4 Lady Lovelace Was Wrong. 5 Unintended Behaviour. 6 Microtargeting and Mass Persuasion. 7 The Feedback Loop. 8 The Glitch. 9 Social Machines. 10 Regulating, Not Unplugging. EPILOGUE. BIBLIOGRAPHY. INDEX. (shrink)

Machine learning researchers distinguish between reinforcement learning and supervised learning and refer to reinforcement learning systems as “agents”. This paper vindicates the claim that systems trained by reinforcement learning are agents while those trained by supervised learning are not. Systems of both kinds satisfy Dretske’s criteria for agency, because they both learn to produce outputs selectively in response to inputs. However, reinforcement learning is sensitive to the instrumental value of outputs, giving rise to systems which exploit the effects of outputs (...) on subsequent inputs to achieve good performance over episodes of interaction with their environments. Supervised learning systems, in contrast, merely learn to produce better outputs in response to individual inputs. (shrink)

This is a review of Mind Design II: Philosophy, Psychology, and Artificial Intelligence, edited by John Haugeland and published by The MIT Press in 1997.

Should we fear a future in which the already tricky world of academic publishing is increasingly crowded out by super-intelligent artificial general intelligence (AGI) systems writing papers on phenomenology and ethics? What are the chances that AGI advances to a stage where a human philosophy instructor is similarly removed from the equation? If Jobst Landgrebe and Barry Smith are correct, we have nothing to fear.

Originally published in PhilosophyNews, July 19, 2022. -/- This new series, What’s Happening in Philosophy (WHiP)-The Philosophers aims to provide a monthly snapshot of various trends and discussions happening across the discipline. -/- In this inaugural post, we begin with a harrowing tale from David Edmonds involving the murder of the German philosopher Moritz Schlick. Schlick was a Vienna Circle guiding spirit and logical positivist thinker. Next up is Steven Nadler’s take on several biographies of the ‘father of modern philosophy’ (...) in his new paper, The Many Lives of René Descartes. Lastly, questions around AI in academia come up in an article from Scientific American. (shrink)

This paper examines the possibility of an objective evaluation of emotions occurring within the learning process and methods for embedding such an evaluation in advanced learning systems. The main conceptual understandings of emotion in learning and teaching are systematized, with an emphasis on the process philosophy approach. Different models of emotion are considered and the possible generalization of Whitehead’s approach to the role of emotion in education is examined. Special attention is given to significant developments in artificial intelligence in identifying (...) the entire spectrum of emotions and their quantitative estimation as sensor-based variables in data-driven technology. This emotional identification is also explored with respect to data acquisition, processing and classification in computer-based systems for educational purposes. The correlation between emotion and performance outcome in learning is studied to inform an interdisciplinary approach which can improve the learning process. As a result, a complex system for emotion measurement and management is proposed. This can be of interest for the further development of intelligent autonomous tutors. (shrink)

Is there a substantial difference between a portrait depicting the sitter’s face made by an artist and an image captured by a machine able to simulate the neuro-physiology of facial perception? Drawing on the later Wittgenstein, this paper answers this question by reference to the relation between seeing a visual pattern as (i) a series of shapes and colours, and (ii) a face with expressions. In the case of the artist, and not of the machine, the portrait’s creative process involves (...) the ability to see both aspects. From the perspective of the image’s viewer, the distinction is more difficult to draw. I address this difficulty by further distinguishing between two attributes of portraits: their representational accuracy, and their ability to convey the artist’s reflection on her experience of seeing the sitter face-to-face. While artificial intelligence can mimic this latter reflective ability, it cannot exactly reproduce it. (shrink)

The objective of this paper is to provide critical analysis of the Kantian notion of freedom ; its significance in the contemporary debate on free-will and determinism, and the possibility of autonomy of artificial agency in the Kantian paradigm of autonomy. Kant's resolution of the third antinomy by positing the ground in the noumenal self resolves the problem of antinomies; however, it invites an explanatory gap between phenomenality and the noumenal self; even if he has successfully established the compatibility of (...) natural causality and non-natural causality through his transcendental argument. This paper is also devoted to establishing the plausibility of the knowledge claim that Kantian reduction of phenomenality has served half of the purpose of the AI scientists on the possibility of Artificial Autonomous Agency. (shrink)

A. Newell and H. A. Simon were two of the most influential scientists in the emerging field of artificial intelligence (AI) in the late 1950s through to the early 1990s. This paper reviews their crucial contribution to this field, namely to symbolic AI. This contribution was constituted mostly by their quest for the implementation of general intelligence and (commonsense) knowledge in artificial thinking or reasoning artifacts, a project they shared with many other scientists but that in their case was theoretically (...) based on the idiosyncratic notions of symbol systems and the representational abilities they give rise to, in particular with respect to knowledge. While focusing on the period 1956-1982, this review cites both earlier and later literature and it attempts to make visible their potential relevance to today's greatest unifying AI challenge, to wit, the design of wholly autonomous artificial agents (a.k.a. robots) that are not only rational and ethical, but also self-conscious. (shrink)

In light of the pervasive developments of new technologies, such as NBIC (Nanotechnology, biotechnology, information technology, and cognitive science), it is imperative to produce a coherent and deep reflexion on the human nature, on human intelligence and on the limit of both of them, in order to successfully respond to some technical argumentations that strive to depict humanity as a purely mechanical system. For this purpose, it is interesting to refer to the epistemology and metaphysics of Thomas Aquinas as a (...) stable philosophical reference on Human Nature. Indeed, we find in the works of Aquinas some of the most productive elements that could form a base to our deeper understanding of, and possibly even solutions to some of the most perplexing questions raised in our times by the existence of AI. (shrink)

In two experiments (total N=693) we explored whether people are willing to consider paintings made by AI-driven robots as art, and robots as artists. Across the two experiments, we manipulated three factors: (i) agent type (AI-driven robot v. human agent), (ii) behavior type (intentional creation of a painting v. accidental creation), and (iii) object type (abstract v. representational painting). We found that people judge robot paintings and human painting as art to roughly the same extent. However, people are much less (...) willing to consider robots as artists than humans, which is partially explained by the fact that they are less disposed to attribute artistic intentions to robots. (shrink)

Recent research shows – somewhat astonishingly – that people are willing to ascribe moral blame to AI-driven systems when they cause harm [1]–[4]. In this paper, we explore the moral- psychological underpinnings of these findings. Our hypothesis was that the reason why people ascribe moral blame to AI systems is that they consider them capable of entertaining inculpating mental states (what is called mens rea in the law). To explore this hypothesis, we created a scenario in which an AI system (...) runs a risk of poisoning people by using a novel type of fertilizer. Manipulating the computational (or quasi-cognitive) abilities of the AI system in a between-subjects design, we tested whether people’s willingness to ascribe knowledge of a substantial risk of harm (i.e., recklessness) and blame to the AI system. Furthermore, we investigated whether the ascription of recklessness and blame to the AI system would influence the perceived blameworthiness of the system’s user (or owner). In an experiment with 347 participants, we found (i) that people are willing to ascribe blame to AI systems in contexts of recklessness, (ii) that blame ascriptions depend strongly on the willingness to attribute recklessness and (iii) that the latter, in turn, depends on the perceived “cognitive” capacities of the system. Furthermore, our results suggest (iv) that the higher the computational sophistication of the AI system, the more blame is shifted from the human user to the AI system. (shrink)

The Frame Problem is the problem of how one can design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation assumes (...) that mental processes are identical to certain kinds of computational processes, and so solving the Frame Problem is a matter of finding a computational architecture that can effectively represent relations of semantic relevance. Instead, we must take seriously the possibility that the way in which intelligent agents use information is inherently different. Whereas intelligent agents are plausibly genuinely causally sensitive to semantic properties as such (to what they perceive, desire, believe intend, etc.), computational systems can only be causally sensitive to the formal features that represent these properties. Indeed, it is this very substitution of formal generalizations for genuinely semantic ones that is responsible for the way current AI systems are brittle, inflexible, and highly specialized. What we need is a more sophisticated way of investigating the relationship between computational information processing and genuinely semantic information use, so that these two senses of using information are not conflated, but instead the question of how they are related to one another can be studied directly. I apply the generative methodology I have developed elsewhere for cognitive science and AI research (Miracchi, 2017, 2019a) to show how the Frame Problem can be appropriately updated. (shrink)

Research in artificial intelligence (AI) has led to revise the challenges of the AI initial programme as well as to keep us alert to peculiarities and limitations of human cognition. Both are linked, as a careful further reading of the Turing’s test makes it clear from Searle’s Chinese room apologue and from Dreyfus’ suggestions, and in both cases, ideal had to be turned into operating mode. In order to rise these more pragmatic challenges AI does not hesitate to link together (...) operations of various levels and functionalities, more specific or more general. The challenges are not met by an operating formal system which should have from the outset all the learning skills, but -for instance in simulation- by the dynamics of a succession of solutions open to adjustments as well as to reflexive repeats. (shrink)

The central concept of this edited volume is "blended cognition", the natural skill of human beings for combining constantly different heuristics during their several task-solving activities. Something that was sometimes observed like a problem as “bad reasoning”, is now the central key for the understanding of the richness, adaptability and creativity of human cognition. The topic of this book connects in a significant way with the disciplines of psychology, neurology, anthropology, philosophy, logics, engineering, logics, and AI. In a nutshell: understanding (...) better humans for designing better machines. It contains a Preface by the editors and 12 chapters. (shrink)

Die Entwicklungen in der Künstlichen Intelligenz (KI) sind spannend. Aber wohin geht die Reise? Ich stelle eine Analyse vor, der zufolge exponentielles Wachstum von Rechengeschwindigkeit und Daten die entscheidenden Faktoren im bisherigen Fortschritt waren. Im Folgenden erläutere ich, unter welchen Annahmen dieses Wachstum auch weiterhin Fortschritt ermöglichen wird: 1) Intelligenz ist eindimensional und messbar, 2) Kognitionswissenschaft wird für KI nicht benötigt, 3) Berechnung (computation) ist hinreichend für Kognition, 4) Gegenwärtige Techniken und Architektur sind ausreichend skalierbar, 5) Technological Readiness Levels (TRL) (...) erweisen sich als machbar. Diese Annahmen werden sich als dubios erweisen. (shrink)

This volume offers eleven philosophical investigations into our future relations with social robots--robots that are specially designed to engage and connect with human beings. The contributors present cutting edge research that examines whether, and on which terms, robots can become members of human societies. Can our relations to robots be said to be "social"? Can robots enter into normative relationships with human beings? How will human social relations change when we interact with robots at work and at home? The authors (...) of this volume explore these questions from the perspective of philosophy, cognitive science, psychology, and robotics. The first three chapters offer a taxonomy for the classification of simulated social interactions, investigate whether human social interactions with robots can be genuine, and discuss the significance of social relations for the formation of human individuality. Subsequent chapters clarify whether robots could be said to actually follow social norms, whether they could live up to the social meaning of care in caregiving professions, and how we will need to program robots so that they can negotiate the conventions of human social space and collaborate with humans. Can we perform joint actions with robots, where both sides need to honour commitments, and how will such new commitments and practices change our regional cultures? The authors connect research in social robotics and empirical studies in Human-Robot Interaction to recent debates in social ontology, social cognition, as well as ethics and philosophy of technology. -/- The book is a response to the challenge that social robotics presents for our traditional conceptions of social interaction, which presuppose such essential capacities as consciousness, intentionality, agency, and normative understanding. The authors develop insightful answers along new interdisciplinary pathways in "robophilosophy," a new research area that will help us to shape the "robot revolution," the distinctive technological change of the beginning 21st century. (shrink)

This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI safety; (...) and cutting-edge developments in techniques to achieve AI, including machine learning, neural networks, dynamical systems. The book also discusses important applications of AI, including big data analytics, expert systems, cognitive architectures, and robotics. It offers a timely, yet very comprehensive snapshot of what is going on in the field of AI, especially at the interfaces between philosophy, cognitive science, ethics and computing. (shrink)

Artificiell intelligens (AI) är ett ungt forskningsområde där många av de grundläggande problemen förefaller att vara av filosofisk art.1 Ämnet har sina filosofiska rötter dels i traditionen från Leibniz, Frege, Russell och Hilbert, som strävar efter att formalisera principerna för exakt tänkande, dels i den klassiska mekanismen: idén att människan är en maskin och att det mänskliga tänkandet är en mekanisk process. Som en första approximation kan vi säga att AI är det vetenskapliga studiet av hur man konstruerar och bygger (...) maskiner (datorer) som är intelligenta (eller uppvisar intelligent beteende). Det är lätt att se att denna forskning aktualiserar frågor av filosofiskt slag. Hit hör problem som rör tänkandets natur samt förhållandet mellan medvetandet och kroppen. Inom AI ses ofta datorns arbetssätt som en modell för människans kognitiva processer och man föreställer sig kanske att hjärnan är ett slags dator. Eftersom hjärnan liknas vid en dator och en dator tänks arbeta rent mekaniskt i enlighet med ett givet program (en given algoritm), så föreställer man sig att samma sak bör gälla för hjärnan. Analogin hjärna-dator leder till föreställningen att tänkande inte är något annat än en dators mekaniska manipulerande av symboler i enlighet med givna formella regler. Denna uppfattning om tänkandets natur förefaller samtidigt problematisk: mekanisk manipulation av symboler förutsätter ingen förståelse av symbolernas innebörd; genuint tänkande, å andra sidan, tycks förutsätta sådan förståelse. Ibland verkar också analogin hjärna-dator i den motsatta riktningen. Eftersom hjärnan liknas vid en dator och hjärnan har en mental dimension, så föreställer man sig kanske att samma sak måste gälla för vissa datorer: synen på hjärnan som en dator leder till tanken att det borde vara möjligt att konstruera datorer som bokstavligen tänker, känner och förnimmer. Man föreställer sig kanske att man skall kunna konstruera ett artificiellt medvetande. Vi är alla förtrogna med datorer som (i en viss mening) kan lösa förelagda uppgifter på ett intelligent sätt, men följer det därav att dessa datorer kan tänka? Inte tänker väl en schackspelande dator på schack? Hur intelligent dagens datorer än beter sig, så verkar det ändå inte rimligt att säga att de har tankar, känslor eller upplevelser. Men kan man utesluta att vi någon gång i framtiden kommer att kunna konstruera datorer som verkligen förstår, är medvetna, och tänker? Dessa frågor rymmer många begreppsliga oklarheter. Vad är intelligens, tänkande, förståelse, medvetande? Hur förhåller sig intelligens och tänkande till medvetandets fenomenella sida: upplevelser och känslor? Om vad slags ting är det meningsfullt att säga att de har intelligens, medvetande, mentala tillstånd, etc.? Är det meningsfullt att säga detta om en hjärna, en maskin, en dator? Vad menas med en maskin (dator)? Hur kan vi veta (avgöra) om ett ting har intelligens, tankar, känslor eller upplevelser? I denna uppsats avser vi att närmare belysa sambandet mellan filosofi och AI samt kritiskt granska några av AI-forskningens förutsättningar inom filosofi och logik. (shrink)

Much of the basic non-technical vocabulary of artificial intelligence is surprisingly ambiguous. Some key terms with unclear meanings include intelligence, embodiment, simulation, mind, consciousness, perception, value, goal, agent, knowledge, belief, optimality, friendliness, containment, machine and thinking. Much of this vocabulary is naively borrowed from the realm of conscious human experience to apply to a theoretical notion of “mind-in-general” based on computation. However, if there is indeed a threshold between mechanical tool and autonomous agent (and a tipping point for singularity), projecting (...) human conscious-level notions into the operations of computers creates confusion and makes it harder to identify the nature and location of that threshold. There is confusion, in particular, about how—and even whether—various capabilities deemed intelligent relate to human consciousness. This suggests that insufficient thought has been given to very fundamental concepts—a dangerous state of affairs, given the intrinsic power of the technology. It also suggests that research in the area of artificial general intelligence may unwittingly be (mis)guided by unconscious motivations and assumptions. While it might be inconsequential if philosophers get it wrong (or fail to agree on what is right), it could be devastating if AI developers, corporations, and governments follow suit. It therefore seems worthwhile to try to clarify some fundamental notions. (shrink)

Handbook in the making. Expected completion for 2021.

This article looks at the debate on the soul in England at the turn of the eighteenth century and at the role played within it by the question of animal soul, which had both theological and scientific ramifications. It discusses the difficulty of accounting for animal behaviour without either adopting the animal-machine hypothesis or according animals an immaterial and hence immortal soul. While those who denied the existence of an immaterial human soul and refused any fundamental distinction between humans and (...) other animals were accused of reducing humans to machines, this article shows that the issues were in fact more complex. The fundamental question was that of the nature of matter; the main danger for many theologians seemed to lie in the attribution of innate life and sensibility to matter, which opened the door to materialism and undermined Christian doctrine. (shrink)

This paper concerns “human symbolic output,” or strings of characters produced by humans in our various symbolic systems; e.g., sentences in a natural language, mathematical propositions, and so on. One can form a set that consists of all of the strings of characters that have been produced by at least one human up to any given moment in human history. We argue that at any particular moment in human history, even at moments in the distant future, this set is finite. (...) But then, given fundamental results in recursion theory, the set will also be recursive, recursively enumerable, axiomatizable, and could be the output of a Turing machine. We then argue that it is impossible to produce a string of symbols that humans could possibly produce but no Turing machine could. Moreover, we show that any given string of symbols that we could produce could also be the output of a Turing machine. Our arguments have implications for Hilbert’s sixth problem and the possibility of axiomatizing particular sciences, they undermine at least two distinct arguments against the possibility of Artificial Intelligence, and they entail that expert systems that are the equals of human experts are possible, and so at least one of the goals of Artificial Intelligence can be realized, at least in principle. (shrink)

Explores the ideas of Turing, Lucas, Scriven, Putnam, and Searle, and renders the Gödel-Church-Lucas argument in terms intelligible to beginning students. Updated and expanded to take into account important arguments and developments in the ten years since its original publication, this provocative dialogue explores the ideas of Turing, Lucas, Scriven, Putnam, and Searle, and renders the complex Gödel-Church-Lucas argument in transparent terms. It includes a new argument, based loosely on Tarski's work on truth and the liar paradox, and a new (...) section dealing with the problem of qualitative features of experience, such as color properties. (shrink)

Could a machine possess an aesthetic sense? Could it appreciate the beauty of a pebble?

There is a great deal in Man Machine and Other Writings that will delight the reader. Thomson has managed to capture much of La Mettrie’s wit and poetic use of language, which is no easy task; as La Mettrie himself comments on his “figurative style,” it “is often necessary in order to express better what is felt and to add grace to truth itself”. The central thesis of Man Machine needs little introduction. Inspired by the suggestion in Part 5 of (...) Descartes’s Discourse on Method that animals are machines, La Mettrie extends the metaphor to man. Man is like a watch whose springs and wheels, properly organized, move to perform its function. But before the reader concludes that La Mettrie is embracing Descartes’s reduction of biological phenomena to “lifeless” mechanism, La Mettrie further claims that matter itself cannot tell time. Only a body appropriately organized can have the property of self-motion, and, hence, perform such functions as telling time, or feeling, or thinking. La Mettrie’s primary task is to vitalize Cartesian mechanism to show that self-directed motion could be a property of organized matter: “I believe thought to be so little incompatible with organized matter that it seems to be one of its properties, like electricity, motive power, impenetrability, extension, etc.”. As for how inert, simple matter becomes active and composed of organs, and, in some cases, endowed with feeling and thought, La Mettrie appeals to our ignorance of causes which prevents us from knowing ultimate beginnings or ends. Such ignorance, however, does not prevent us from admitting such “incontrovertible observations” as that organized matter possesses a motive principle, whereas unorganized matter does not. Other themes in the work include his account of the faculty of imagination as the origin of thought, observations in comparative psychology, a sketch of thinking as a symbolic process, and a discussion of whether apes could speak. Man Machine, as Thomson explains in the introduction, is not a formal treatise, but, rather, a loosely structured polemic, often given to rhetorical flourishes. However, where Man Machine falls short on argumentation it will surely provoke and engage the reader. (shrink)

As important as it is to realize the potential of computer technology to improve education, it is just as important to understand how the social organization of schools and classrooms influences the use of computers, and in turn is effected by that technology in unanticipated ways. In Computers and Classroom Culture, first published in 1996, Janet Schofield observes the fascinating dynamics of the computer-age classroom. Among her many discoveries, Schofield describes how the use of an artificially-intelligent tutor in a geometry (...) class unexpectedly changes aspects like the level of peer competition and the teachers' grading practices. She also discusses why many teachers fail to make significant instructional use of computers and how gender appears to have a crucial impact on students' reactions to computer use. All educators, sociologists, and psychologists concerned with educational computing and the changing shape of the classroom will find themselves compellingly engaged. (shrink)

The purpose of this paper is to argue that, in order for the debate in Computing and philosophy to move forward with respect to its current state, the advances of Computer Science and Artificial Intelligence of the last decades must be taken into account. Computational reflection, one of these advances, is presented in detail and its philosophical implications are discussed, in contrast with old-fashioned views of computational systems such as those presented by Lucas’ papers on Minds and Machines.

This is the first of two volumes of essays in commemoration of Alan Turing, whose pioneering work in the theory of artificial intelligence and computer science continues to be widely discussed today. A group of prominent academics from a wide range of disciplines focus on three questions famously raised by Turing: What, if any, are the limits on machine `thinking'? Could a machine be genuinely intelligent? Might we ourselves be biological machines, whose thought consists essentially in nothing more than the (...) interaction of neurons according to strictly determined rules? The discussion of these fascinating issues is accessible to non-specialists and stimulating for all readers. (shrink)

Abstract: Many believe that, in addition to cognitive capacities, autonomous robots need something similar to affect. As in humans, affect, including specific emotions, would filter robot experience based on a set of goals, values, and interests. This narrows behavioral options and avoids combinatorial explosion or regress problems that challenge purely cognitive assessments in a continuously changing experiential field. Adding human-like affect to robots is not straightforward, however. Affect in organisms is an aspect of evolved biological systems, from the taxes of (...) single-cell organisms to the instincts, drives, feelings, moods, and emotions that focus human behavior through the mediation of hormones, pheromones, neurotransmitters, the autonomic nervous system, and key brain structures. We argue that human intelligence is intimately linked to biological affective systems and to the unique repertoire of potential behaviors, sometimes conflicting, they facilitate. Artificial affect is affect in name only and without genes and biological bodies, autonomous robots will lack the goals, interests, and value systems associated with human intelligence. We will take advantage of their general intelligence and expertise, but robots will not enter our intellectual world or apply for legal status in the community. -/- . (shrink)

Recent philosophy of mind has increasingly focused on the role of technology in shaping, influencing, and extending our mental faculties. Technology extends the mind in two basic ways: through the creative design of artifacts and the purposive use of instruments. If the meaningful activity of technological artifacts were exhaustively described in these mind-dependent terms, then a philosophy of technology would depend entirely on our theory of mind. In this dissertation, I argue that a mind-dependent approach to technology is mistaken. Instead, (...) some machines are best understood as independent participants in their own right, contributing to and augmenting a variety of social practices as active, though often unrecognized, community members. Beginning with Turing’s call for “fair play for machines”, I trace an argument concerning the social autonomy of nonhuman agents through the artificial intelligence debates of the 20th century. I’ll argue that undue focus on the mind has obscured the force of Turing’s proposal, leaving the debates in an unfortunate stalemate. I will then examine a network theoretic alternative to the study of multi-agent complex systems that can avoid anthropocentric, mind-dependent ways of framing human-machine interactions. I argue that this approach allows for both scientific and philosophical treatment of large and complicated sociotechnical systems, and suggests novel methods for designing, managing, and maintaining such systems. Rethinking machines in mind-independent terms will illuminate the nature, scope, and evolution of our social and technological practices, and will help clarify the relationships between minds, machines, and the environments we share. (shrink)