Machine learning, and more broadly artificial intelligence (AI), is a fascinating technology and can be considered as the closest approximation to the Cartesian “thinking thing” that humans have ever created. Just as the industrial revolution required a new ethos, the age of intelligent machines will create its own, challenging the established moral, economic, and political presuppositions. This paper discusses the relationship between AI and society; it presents several thought experiments to explore the complexity of the relationship and highlights the insufficiency (...) of the current normative paradigm in addressing technological expansion. I argue that many of the externalities, such as security risks, loss of privacy, and economic instability will result from trying to fit the emerging technologies into the existing frame of efficiency and utility, by redefining the notions of human value, identity, autonomy, purity, and truth, among others. The age of the intelligent machine is elevating alienation to new levels, treating the individual as mere patterns in data—its primary commodity. I further argue that while the possibility of unintended consequences, due to the potential misuse of AI is ever present, the intelligent machine per se is unlikely to engage in a zero-sum game for power on its own initiative. I question whether singularity is at all attainable and argue that technology will forever remain a proxy for human interests. I conclude by posing questions for charting the path forward. Through this analysis, I aim to provide a more nuanced understanding of the complex relationship between the AI and humans. (shrink)

Accelerating Turing machines have attracted much attention in the last decade or so. They have been described as “the work-horse of hypercomputation” (Potgieter and Rosinger 2010: 853). But do they really compute beyond the “Turing limit”—e.g., compute the halting function? We argue that the answer depends on what you mean by an accelerating Turing machine, on what you mean by computation, and even on what you mean by a Turing machine. We show first that in the current literature the term (...) “accelerating Turing machine” is used to refer to two very different species of accelerating machine, which we call end-stage-in and end-stage-out machines, respectively. We argue that end-stage-in accelerating machines are not Turing machines at all. We then present two differing conceptions of computation, the internal and the external, and introduce the notion of an epistemic embedding of a computation. We argue that no accelerating Turing machine computes the halting function in the internal sense. Finally, we distinguish between two very different conceptions of the Turing machine, the purist conception and the realist conception; and we argue that Turing himself was no subscriber to the purist conception. We conclude that under the realist conception, but not under the purist conception, an accelerating Turing machine is able to compute the halting function in the external sense. We adopt a relatively informal approach throughout, since we take the key issues to be philosophical rather than mathematical. (shrink)

This paper concerns Alan Turing’s ideas about machines, mathematical methods of proof, and intelligence. By the late 1930s, Kurt Gödel and other logicians, including Turing himself, had shown that no finite set of rules could be used to generate all true mathematical statements. Yet according to Turing, there was no upper bound to the number of mathematical truths provable by intelligent human beings, for they could invent new rules and methods of proof. So, the output of a human mathematician, for (...) Turing, was not a computable sequence (i.e., one that could be generated by a Turing machine). Since computers only contained a finite number of instructions (or programs), one might argue, they could not reproduce human intelligence. Turing called this the “mathematical
objection” to his view that machines can think. Logico-mathematical reasons, stemming from his own work, helped to convince Turing that it should be possible to reproduce human intelligence, and eventually compete with it, by developing the appropriate kind of digital computer. He felt it
should be possible to program a computer so that it could learn or discover new rules, overcoming the limitations imposed by the incompleteness and undecidability results in the same way that human
mathematicians presumably do. (shrink)

A critical survey of some attempts to define ‘computer’, beginning with some informal ones, then critically evaluating those of three philosophers, and concluding with an examination of whether the brain and the universe are computers.

In this paper, I raise the question whether an artificial intelligence can act morally. I first sketch and defend a general picture of what is at stake in this question. I then sketch and defend a behavioral test, known as the Moral Turing Test, as a good sufficiency test for an artificial intelligence acting morally. I end by discussing some general anticipated objections.

Alan Turing’s 1950 imitation game has been widely understood as a means for testing if an entity is intelligent. Following a series of papers by Diane Proudfoot, I offer a socio-technological interpretation of Turing’s paper and present an alternative way of understanding both the imitation game and Turing’s concept of intelligence. Turing, I claim, saw intelligence as a social concept, meaning that possession of intelligence is a property determined by society’s attitude toward the entity. He realized that as long as (...) human society held a prejudiced attitude toward machinery—seeing machines a priori as mindless objects—machines could not be said to be intelligent, by definition. He also realized, though, that if humans’ a priori, chauvinistic attitude toward machinery changed, the existence of intelligent machines would become logically possible. Turing thought that such a change would eventually occur: He believed that when scientists overcome the technological challenge of constructing sophisticated machines that could imitate human verbal behavior—i.e., do well in the imitation game—humans’ prejudiced attitude toward machinery will have altered in such a way that machines could be said to be intelligent. The imitation game, for Turing, was not an intelligence test, but a technological aspiration whose realization would likely involve a change in society’s attitude toward machines. (shrink)

This article provides a detailed analysis of the transfer of a key cluster of ideas from mathematical logic to computing. We demonstrate the impact of certain of Turing’s logico-philosophical concepts from the mid-1930s on the emergence of the modern electronic computer—and so, in consequence, Turing’s impact on the direction of modern philosophy, via the computational turn. We explain why both Turing and von Neumann saw the problem of developing the electronic computer as a problem in logic, and we describe their (...) joint journey from logic to electronic computation. While much has been written about Turing’s and von Neumann’s individual contributions to the development of the computer, this article investigates less well-known terrain: their interactions and mutual influences. Along the way we argue against ‘logic skeptics’ and ‘Turing skeptics’, who claim that neither logic nor Turing played any significant role in the creation of the modern computer. (shrink)

In this paper I argue that Turing’s responses to the mathematical objection are straightforward, despite recent claims to the contrary. I then go on to show that by understanding the importance of learning machines for Turing as related not to the mathematical objection, but to Lady Lovelace’s objection, we can better understand Turing’s response to Lady Lovelace’s objection. Finally, I argue that by understanding Turing’s responses to these objections more clearly, we discover a hitherto unrecognized, substantive thesis in his philosophical (...) thinking about the nature of mind. (shrink)

We discuss mathematical and physical arguments against continuity and in favor of discreteness, with particular emphasis on the ideas of Émile Borel.

The aim of this paper is to grasp the relevant distinctions between various ways in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. The proposed taxonomy cuts across the (...) traditional dichotomies between symbolic and embodied AI, general intelligence and symbol and intelligence and cognitive simulation and human/non-human-like AI. -/- According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programs: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). -/- In continuation of pragmatic views of the modes of modeling and simulating world affairs, this taxonomy of approaches to modeling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research—and what made that research program uniquely dependent on them. (shrink)

In the first section of the paper I present Alan Turing’s notion of effective memory, as it appears in his 1936 paper ‘On Computable Numbers, With an Application to The Entscheidungsproblem’. This notion stands in surprising contrast with the way memory is usually thought of in the context of contemporary computer science. Turing’s view (in 1936) is that for a computing machine to remember a previously scanned string of symbols is not to store an internal symbolic image of this string. (...) Rather, memory consists in the fact that the past scanning of the string affects the behavior of the computer in the face of potential future inputs. In the second, central section of the paper I begin exploring how this view of Turing’s bears upon contemporary discussions in the philosophy of mind. In particular, I argue that Turing’s approach can be used to lend support to dispositional conceptions of the propositional attitudes, like the one recently presented by Matthews (2007), and that his effective memory manifests some of the characteristics of Millikan’s (1996) pushmepullyou mental states. (shrink)

This chapter offers a high-level overview of the philosophy of cognitive science and an introduction to the Oxford Handbook of Philosophy of Cognitive Science. The philosophy of cognitive science emerged out of a set of common and overlapping interests among philosophers and scientists who study the mind. We identify five categories of issues that illustrate the best work in this broad field: (1) traditional philosophical issues about the mind that have been invigorated by research in cognitive science, (2) issues regarding (...) the practice of cognitive science and its foundational assumptions, (3) issues regarding the explication and clarification of core concepts in cognitive science, (4) first-order empirical issues where philosophers participate in the interdisciplinary investigation of particular psychological phenomena, (5) traditional philosophical issues that aren’t about the mind but that can be informed by a better understanding of how the mind works. (shrink)

Turing used the expression “emotional” in three distinct ways: to state his philosophical theory of the concept of intelligence, to classify arguments for and against the possibility of machine intelligence, and to describe the education of a “child machine”. The remarks on emotion include several of the most important philosophical claims. This paper analyses these remarks and their significance for current research in Artificial Intelligence.

If Australasian philosophers constitute the kind of group to which a collective identity or broadly shared self-image can plausibly be ascribed, the celebrated history of Australian materialism rightly lies close to its heart. Jack Smart’s chapter in this volume, along with an outstanding series of briefer essays in A Companion to Philosophy in Australia and New Zealand (Forrest 2010; Gold 2010; Koksvik 2010; Lycan 2010; Matthews 2010; Nagasawa 2010; Opie 2010; Stoljar 2010a), effectively describe the naturalistic realism of Australian philosophy (...) of mind. In occasional semi-serious psychogeographic speculation, this long-standing and strongly-felt intellectual attitude has been traced back to the influences of our light, land, or lifestyle (Devitt 1996, x; compare comments by Chalmers and O’Brien in Mitchell, 2006). Australasian work in philosophy of mind and cognition has become more diverse in the last 40 years, but is almost all still marked, in one way or another, by the history of these debates on materialism. (shrink)

An investigation of the concept of “surveyability” as traced through the thought of Hilbert, Wittgenstein, and Turing. The communicability and reproducibility of proof, with certainty, are seen as earmarked by the “surveyability” of symbols, sequences, and structures of proof in all these thinkers. Hilbert initiated the idea within his metamathematics, Wittgenstein took up a kind of game formalism in the 1920s and early 1930s in response. Turing carried Hilbert’s conception of the “surveyability” of proof in metamathematics through into his analysis (...) of what a formal system (what a step in a computation) is in “On computable numbers, with an application to the Entscheidungsproblem” (1936). Wittgenstein’s 1939 investigations of the significance of surveyability to the concept of “proof “in Principia Mathematica were influenced, both by Turing’s remarkable everyday analysis of the Hilbertian idea, and by conversations with Turing. Although Turing does not use the word “surveyability” explicitly, it is clear that the Hilbertian idea plays a recurrent role in his work, refracted through his engagement with Wittgenstein’s idea of a “language-game”. This is evinced in some of his later writings, where the “reform” of mathematical notation for the sake of human surveyability (1944/45) may be seen to draw out the Hilbertian idea. For Turing, as for Wittgenstein, the need for “surveyability” earmarks the evolving culture of humans located in an evolving social and scientific world, just as it had for Hilbert. (shrink)

En “Computing Machinery and Intelligence”, de 1950, Turing acaba afirmando la posibilidad de procedimientos automáticos computacionales que sustituyan al pensamiento humano. Son reconocidas las críticas de Searle a este artículo, que se han retomado y ampliado a propósito de la actual discusión sobre el alcance y los límites de la inteligencia artificial. Especialmente relevantes al respecto resultan los últimos trabajos de Žižek y Larson, así como la defensa de las propuestas de Turing que ha realizado Daniel Dennett, especialmente en su (...) obra final. De toda esta discusión quiero valerme para esclarecer cuál es exactamente la posición de Turing a partir de 1950. La conclusión a la que llego es que Turing no defiende que los procedimientos automáticos computacionales, tal y como él mismo los define, sustituyen al pensamiento humano, sino que es posible que lo puedan sustituir, y que se trata de una cuestión que solo se puede resolver empíricamente. (shrink)

With 500+ papers and 20+ books spanning many scientific disciplines, Mark Burgin has left an indelible mark and legacy for future explorers of human thought and information technology professionals. In this paper, I discuss his contribution to the evolution of machine intelligence using his general theory of information (GTI) based on my discussions with him and various papers I co-authored during the past eight years. His construction of a new class of digital automata to overcome the barrier posed by the (...) Church–Turing Thesis, and his contribution to super-symbolic computing with knowledge structures, cognizing oracles, and structural machines are leading to practical applications changing the future landscape of information systems. GTI provides a model for the operational knowledge of biological systems to build, operate, and manage life processes using 30+ trillion cells capable of replication and metabolism. The schema and associated operations derived from GTI are also used to model a digital genome specifying the operational knowledge of algorithms executing the software life processes with specific purposes using replication and metabolism. The result is a digital software system with a super-symbolic computing structure exhibiting autopoietic and cognitive behaviors that biological systems also exhibit. We discuss here one of these applications. (shrink)

Philosophy and Literature is an internationally renowned refereed journal founded by Denis Dutton at the University of Canterbury, Christchurch. It is now published by the Johns Hopkins University Press. Since its inception in 1976, Philosophy and Literature has been concerned with the relation between literary and philosophical studies, publishing articles on the philosophical interpretation of literature as well as the literary treatment of philosophy. Philosophy and Literature has sometimes been regarded as iconoclastic, in the sense that it repudiates academic pretensions, (...) insidious jargon and institutional vogue. Dutton, who remains the editor, still writes a regular column. A distinctive feature of Philosophy and Literature was the annual Bad Writing Contest, held from 1995–98, which sought to identify (and publish) the ‘most stylistically lamentable passages’ of academic prose, often to great amusement. (shrink)

Given the difficulties of defining “machine” and “think”, Turing proposed to replace the question “Can machines think?” with a proxy: how well can an agent engage in sustained conversation with a human? Though Turing neither described himself as a philosopher nor published much on philosophical matters, his Imitation Game has stood the test of time. Most understood at that time that success would not come easy, but few would have guessed just how difficult engaging in ordinary conversation would turn out (...) to be. Despite the proliferation of language processing tools, we have seen little progress towards doing well at the Imitation Game. Had Turing instead suggested ability at games or even translation as a proxy for intelligence, his paper might have been forgotten. We argue that these and related problems are amenable to mechanical, though sophisticated, formal techniques. Turing appears to have taken care to select sustained, productive conversation and that alone as his proxy. Even simple conversation challenges a machine to engage in the rich practice of human discourse in all its generality and variety. (shrink)

In recent decades, there has been a proliferation among the scientific community of works that focus on Alan Turing’s contributions to the design and development of the modern computer. However, there are significant discrepancies among these studies, to such a point that some of them cast serious doubts on Alan Turing’s work with respect to today’s computer, and there are others that staunchly defend his leading role, as well as other studies that set out more well-balanced opinions. Faced with this (...) situation, the aim of this paper is to analyse the evidence existing today in order to be able to draw a conclusion about whether or not Turing anticipated the trivialisation of the modern computer memory and, likewise, if his universal a-machine is the precursor of the general-purpose computer so omnipresent today. As a result of our research, the authors conclude that Turing did indeed play a leading role in the appearance of the modern computer, although he was not the only one or the first in the field of Computing Science, albeit he was the most influential, both in scope and in depth. (shrink)

Within the strong branch of artificial intelligence, which is aimed at creating thinking machines with intellectual powers like those of man, the most explored research program is symbolic aI, defined as the attempt to use electronic computers to replicate the human mind, either assuming a structural and functional similarity between them, or trying to replicate the behavior produced by the human mind through computational processes that also have an intentional structure but are only instrumentally equivalent. In this paper we show (...) that strong symbolic aI, in either of its two variants, is impossible, since formal systems, that is what all computer programs ultimately are, are not sufficient to replicate in an intentional way four intellectual faculties of man that are essential to intelligent behavior: sense of the situation, common sense, procedural skills and theory abduction. (shrink)

En diciembre de 1999, la revista Time eligió a Albert Einstein “The Person of the Century”. Fue, no cabe duda, una elección razonable, pero, como se argumenta en este artículo, existen también buenos argumentos para sostener que Turing podría haber recibido tal honor. En apoyo de semejante tesis están sus trabajos puramente científicos, que se esbozan aquí, trabajos que entroncan con la mejor tradición matemática, y cómo afectaron al desarrollo matemático, siendo finalmente instrumentales en la configuración de un nuevo mundo (...) tecnológico, tanto en lo que al cálculo y manejo de información se refiere, como en lo relativo al establecimiento de nuevas formas de relaciones sociales. Relacionadas con lo anterior, se encuentran las aportaciones que hizo durante la Segunda Guerra Mundial al desciframiento de códigos secretos, que, en cierto sentido, metafórico, se pueden considerar como una nueva herramienta para socavar la privacidad, ese derecho civil cuya negación arruinó su propia vida. (shrink)

John R. Searle’nin Çin Odası Argümanı oldukça ilgi çekici ve üzerinde çokça tartışılmış bir argümandır. Argümanın ilgi çekiciliğini günümüzün en baskın zihin kuramlarından biri olan kompütasyonalizmi hedef almasına, üzerinde çokça tartışılmasını ise felsefi implikasyonlarının hayli zengin olmasına bağlayabiliriz. Searle, argümanının beynin kelimenin tam anlamıyla bir bilgisayar, zihnin de bir bilgisayar programı olduğunu ileri süren kompütasyonalizmin kesin ve net bir reddi olarak görse de, bize ve birçoklarına göre argüman onun düşündüğü kadar ikna edici değildir. Bu çalışmada Çin Odası Argümanı Searle’nin genel (...) zihin anlayışı zemininde incelenecek ve argümana verilen yaygın cevaplar değerlendirilerek argümanın neden ikna edici olmadığı gösterilmeye çalışılacaktır. (shrink)