A complete reconstruction of Lehmer’s ENIAC set-up for computing the exponents of p modulo two is given. This program served as an early test program for the ENIAC (1946). The reconstruction illustrates the difficulties of early programmers to find a way between a man operated and a machine operated computation. These difficulties concern both the content level (the algorithm) and the formal level (the logic of sequencing operations).

The Joint International Conference on Logic Programming, sponsored by the Association for Logic Programming, is a major forum for presentations of research, applications, and implementations in this important area of computer science. Logic programming is one of the most promising steps toward declarative programming and forms the theoretical basis of the programming language Prolog and its various extensions. Logic programming is also fundamental to work in artificial intelligence, where it has been used for (...) nonmonotonic and commonsense reasoning, expert systems implementation, deductive databases, and applications such as computer-aided manufacturing. Krzysztof R. Apt is project leader at the Centre for Mathematics and Computer Science in Amsterdam and part-time Professor in Computer Science at the University of Amsterdam. Topics covered: Theory Foundations. Programming Languages. Implementation. Programming Methodologies and Tools. Applications. Deductive Databases. Artificial Intelligence. Parallelism. (shrink)

After the First World War mathematics and the organisation of ballistic computations at Aberdeen Proving Ground changed considerably. This was the basis for the development of a number of computing aids that were constructed and used during the years 1920 to 1950. This article looks how the computational organisation forms and changes the instruments of calculation. After the differential analyzer relay-based machines were built by Bell Labs and, finally, the ENIAC, one of the first electronic computers, was built, to satisfy (...) the need for computational power in ballistics during the second World War. (shrink)

This article intervenes in feminist theories concerning the politics of care, reading this contested notion through its representation in an ‘artificial’ relationship between a human clone and a computer in the science fiction film Moon (dir. Duncan Jones, 2009). Drawing on Joan Tronto’s work (1993), I delineate a conventional, vernacular conception of care, which puts in place problematic, prescriptive roles in caring relationships. Then, reading Moon through Donna Haraway’s theorisation of companion species (2008) and what she terms the ‘touching’ (...) of material histories and contingencies, I show how these hierarchies may be unravelled. This figure of the caring computer represents a capacity for affective impact upon human subjects that is not normally ascribed to ‘emotionless’ technological subjects. It animates a disruption of the linear, instrumental relationships of power between humans and technologies by showing the ways in which ongoing processes of co-constitution are responsible for bringing each participant in the relationship into being. Care here is a manner of activating and making conspicuous those processes and can unravel, rather than prescribe, the roles which respective participants in such relationships are assigned. (shrink)

The First International Workshop brings together researchers from the theoretical ends of the logic programming and artificial intelligence communities to discuss their mutual interests. Logic programming deals with the use of models of mathematical logic as a way of programming computers, where theoretical AI deals with abstract issues in modeling and representing human knowledge and beliefs. One common ground is nonmonotonic reasoning, a family of logics that includes room for the kinds of variations that can be found (...) in human reasoning. Topics covered: Stable Semantics. Default Logic. AutoEpistemic Logic. Truth Maintenance Systems. Implementation Issues. Diagnosis. Applications. Inheritance Reasoning. Logics of Belief. Inconsistency and Non-Monotonicity. (shrink)

• Lisp was intended to be compiled at first. However, a universal Lisp function eval in 1959 to show that neater language for computability theory than Turing Steve Russell pointed out that the universal function taken as an interpreter for pure Lisp, and hand-compiled..

Since its conception nearly 20 years ago, Logic Programming - the idea of using logic as a programming language - has been developed to the point where it now plays an important role in areas such as database theory, artificial intelligence and software engineering. However, there are still many challenging research issues to be addressed and the UK branch of the Association for Logic Programming was set up to provide a forum where the flourishing research community could (...) discuss important issues of Logic Programming which were often by-passed at the large international conferences. This volume contains the twelve papers which were presented at the ALPUK's 3rd conference which was held in Edinburgh, 10-12 April 1991. The aim of the conference was to give a broad but detailed technical insight into the work currently being done in this field, both in the UK and by researchers as far afield as Canada and Bulgaria. The breadth of interest in this area of Computer Science is reflected in the range of the papers which cover - amongst other areas - massively parallel implementation, constraint logic programming, circuit modelling, algebraic proof of program properties, deductive databases, specialised editors and standardisation. The resulting volume gives a good overview of the current progress being made in the field and will be of interest to researchers and students of any aspects of logic programming, parallel computing or database techniques and management. (shrink)

Community detection is a major issue in network analysis. This paper combines a socio-historical approach with an experimental reconstruction of programs to investigate the early automation of clique detection algorithms, which remains one of the unsolved NP-complete problems today. The research led by the archaeologist Jean-Claude Gardin from the 1950s on non-numerical information and graph analysis is retraced to demonstrate the early contributions of social sciences and humanities. The limited recognition and reception of Gardin's innovative computer application to the (...) humanities are addressed through two factors, in addition to the effects of historiography and bibliographies on the recording, discoverability, and reuse of scientific productions: (1) funding policies, evidenced by the transfer of research effort on graph applications from temporary interdisciplinary spaces to disciplinary organizations related to the then-emerging field of computer science; and (2) the erratic careers of algorithms, in which efficiency, flaws, corrections, and authors’ status, were determining factors. (shrink)

The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s (Representation & Reality, Bradford Books, Cambridge in 1988) monograph, “Representation & Reality”, which if correct, has (...) important implications for turing machine functionalism and the prospect of ‘conscious’ machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that, “everything implements the specific machine Q on a particular input set (x)”. Then, equating Q (x) to any putative AI program, I will show that conceding the ‘strong AI’ thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g. grass, rocks etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere. (shrink)

In an attempt to determine the epistemic status of computer simulation results, philosophers of science have recently explored the similarities and differences between computer simulations and experiments. One question that arises is whether and, if so, when, simulation results constitute novel empirical data. It is often supposed that computer simulation results could never be empirical or novel because simulations never interact with their targets, and cannot go beyond their programming. This paper argues against this position by (...) examining whether, and under what conditions, the features of empiricality and novelty could be displayed by computer simulation data. I show that, to the extent that certain familiar measurement results have these features, so can some computer simulation results. (shrink)

The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s (Representation & Reality, Bradford Books, Cambridge in 1988 ) monograph, “Representation & Reality”, which if correct, (...) has important implications for turing machine functionalism and the prospect of ‘conscious’ machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that, “everything implements the specific machine Q on a particular input set ( x )”. Then, equating Q ( x ) to any putative AI program, I will show that conceding the ‘strong AI’ thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g. grass, rocks etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere. (shrink)

The history of computing usually focuses on achievements in Western universities and research centers and is mostly about what happened in the United States and Great Britain. However, in Eastern Europe, particularly in war-torn Poland, where there was very little state funding, many highly original hardware and software projects were initiated. The small number of publications available to us, especially those in English, led to the belief that technological progress was the result of research carried out in Western countries alone. (...) This article aims to fill this knowledge gap by focusing on the numerous research projects initiated in Polish universities and computer industries that unfortunately turned into dead ends as the result of socialist policies. These are references that cannot be ignored, not only for a historical reconstruction of the evolution of technology but also with regard to the social effects recorded in Poland immediately after the Second World War. The communist ideology, which pursued gender equality policies after the end of the war, encouraged women to pursue education, enabling the many female students enrolled in mathematics degree courses to specialize in “ Maszyny Matematyczne” (mathematical machines) and become, like men, experts in computer programming and design. As well as highlighting the role that Poland played in the nascent “computer science” and providing detailed information on what women contributed, this article will explain why the success of the Polish computer industry was limited due to the nonexistent coordination between the communist states (Comecon). (shrink)

Because the label "computing and philosophy" can seem like an ad hoc attempt to tie computing to philosophy, it is important to explain why it is not, what it studies (or does) and how it differs from research in, say, "computing and history," or "computing and biology". The American Association for History and Computing is "dedicated to the reasonable and productive marriage of history and computer technology for teaching, researching and representing history through scholarship and public history" (http://theaahc.org). More (...) pervasive, work in computing and biology enjoys the convenient name of "bioinformatics...the science of using information to understand biology..., a subset of the larger field of computational biology, the application of quantitative analytical techniques in modeling biological systems" (http://oreilly.com/catalog/bioskills/chapter/ ch01.html). The recent venture of the Association for Computing Machinery and the Institute of Electrical and Electronics Engineers to publish the Transactions on Computational Biology and Bioinformatics (TCBB) bears witness to the reach of computing and biology and underscores its objective. TCBB intends to report "archival research results related to the algorithmic, mathematical, statistical, and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development and optimization of biological databases; and important biological results that are obtained from the use of these methods, programs, and databases" (http://tcbb.acm.org). In the case of "computing and history" and "bioinformatics," each discipline stands in a particular relationship to computers that raises questions unique to itself. But both are devoted to the development of computational tools to aid discovery.. (shrink)

The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s monograph, “Representation & Reality”, which if correct, has important implications for turing machine functionalism and the (...) prospect of ‘conscious’ machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that, “everything implements the specific machine Q on a particular input set ”. Then, equating Q to any putative AI program, I will show that conceding the ‘strong AI’ thesis for Q opens the door to a vicious form of panpsychism whereby all open systems,, must instantiate conscious experience and hence that disembodied minds lurk everywhere. (shrink)

Philosophy and Computing explores each of the following areas of technology: the digital revolution; the computer; the Internet and the Web; CD-ROMs and Mulitmedia; databases, textbases, and hypertexts; Artificial Intelligence; the future of computing. Luciano Floridi shows us how the relationship between philosophy and computing provokes a wide range of philosophical questions: is there a philosophy of information? What can be achieved by a classic computer? How can we define complexity? What are the limits of quantam computers? Is (...) the Internet an intellectual space or a polluted environment? What is the paradox in the Strong Artificial Intelligence program? Philosophy and Computing is essential reading for anyone wishing to fully understand both the development and history of information and communication technology as well as the philosophical issues it ultimately raises. (shrink)

The term Monte Carlo method indicates any computer-aided procedure for numerical estimation that combines mathematical calculations with randomly generated numerical input values. Today it is an important tool in high energy physics while physicists and philosophers also often consider it a sort of virtual experiment. The Monte Carlo method was developed in the 1940s, in the context of U.S. American nuclear weapons research, an event often regarded as the origin of both computer simulation and “artificial reality” (Galison 1997). (...) The present paper interrogates this strong claim by focusing on the emergence of Monte Carlo event generators in particle physics in the early 1960s. This historical case study shows how, as Monte Carlo computation became part of the toolbox of particle physicists around 1960, it was neither usually referred to as a “computer simulation” nor was it regarded as a surrogate for experimentation. In revising the history of this method, this paper asks, in what context did particle physicists of the 1960s decide to create FAKE, the first high-energy-physics Monte Carlo event simulator? What was their goal? And what epistemic role did FAKE play? In answering these questions, it is argued that Monte Carlo computations were not introduced into particle physics to simulate experiments, but rather they played the role of theoretical tools. The Monte Carlo method was able to do this thanks to its random component, a property which provided a means of modeling a specific phenomenon, so-called “(particle) resonances”. Indeed, in doing so, event generators even came to mutually assimilate and reshape the notions of particle and resonance, taking up an epistemic function which had previously been confined to physical-mathematical formulae: that of a medium which could express aspects of particle theory. (shrink)

This book presents a systematic philosophical and historical analysis of operating systems (0S). The discussion starts with the evolution of OSs since before their birth. It continues with a comprehensive philosophical analysis grounded in technical aspects. Coverage looks at software and (where appropriate) hardware as well as their historical developments. The authors not only offer historical and philosophical reflections on operating systems. They also explore the programs they coordinate and trace the epsitemic and ontological consequences of their designs. Each chapter (...) investigates one or more overlapping fragments of this fascinating history. These include: the birth of the UNIX system and the development of early systems and prototypes; a conceptual analysis of the plurality of systems; an investigation into business, ethical, and aesthetics aspects related to operating systems; and logical principles of formal languages. This book will interest researchers from a diversity of backgrounds. It will appeal to historians, philosophers, as well as logicians and computer scientists who want to engage with topics relevant to the history and philosophy of programming and more specifically that of operating systems. (shrink)

Scientific discovery is often regarded as romantic and creative - and hence unanalyzable - whereas the everyday process of verifying discoveries is sober and more suited to analysis. Yet this fascinating exploration of how scientific work proceeds argues that however sudden the moment of discovery may seem, the discovery process can be described and modeled. Using the methods and concepts of contemporary information-processing psychology (or cognitive science) the authors develop a series of artificial-intelligence programs that can simulate the human thought (...) processes used to discover scientific laws. The programs - BACON, DALTON, GLAUBER, and STAHL - are all largely data-driven, that is, when presented with series of chemical or physical measurements they search for uniformities and linking elements, generating and checking hypotheses and creating new concepts as they go along. "Scientific Discovery examines the nature of scientific research and reviews the arguments for and against a normative theory of discovery; describes the evolution of the BACON programs, which discover quantitative empirical laws and invent new concepts; presents programs that discover laws in qualitative and quantitative data; and ties the results together, suggesting how a combined and extended program might find research problems, invent new instruments, and invent appropriate problem representations. Numerous prominent historical examples of discoveries from physics and chemistry are used as tests for the programs and anchor the discussion concretely in the history of science. Pat Langley is an Associate Professor in the Department of Information and Computer Science at the University of California, Irvine.Herbert Simon is a Professor in the Departments of Psychology, Computer Science, and Philosophy at Carnegie-Mellon University. Gary L. Bradshaw is an Assistant Professor in the Department of Psychology and Institute of Cognitive Science at the University of Colorado, Boulder. Jan M. Zytkow is an Associate Professor in the Computer Science Department at Wichita State University. (shrink)

As many moral problems have a very complex nature, moral reasoning towards a justified decision in such cases may be as difficult. Many considerations have to be taken into account in order to reach a judgement based on good reasons. May the computer, as a tool in data processing, be of use for ethical decision-making? In this paper I will argue that we should be very careful in developing and using ethical expert-systems. Next, I will explain how the (...) class='Hi'>computer may be a useful ‘tool’ for instruction in moral reasoning. This will be done by presenting a model for Computer Assisted Instruction in ethics. Finally, I will describe the outline of a CAI program according to this model and discuss some problems which are encountered in designing this program. (shrink)

As many moral problems have a very complex nature, moral reasoning towards a justified decision in such cases may be as difficult. Many considerations have to be taken into account in order to reach a judgement based on good reasons. May the computer, as a tool in data processing, be of use for ethical decision-making? In this paper I will argue that we should be very careful in developing and using ethical expert-systems. Next, I will explain how the (...) class='Hi'>computer may be a useful ‘tool’ for instruction in moral reasoning. This will be done by presenting a model for Computer Assisted Instruction in ethics. Finally, I will describe the outline of a CAI program according to this model and discuss some problems which are encountered in designing this program. (shrink)

In response to the so-called ‘software crisis’ of the late 1960s, many approaches were proposed to turn software engineering and programming into more systematic disciplines, to turn an art into a science. This paper studies one popular example often used in these proposals, the computation of a list of primes, to discuss some salient features of the proposed programming paradigms. It also looks at the actual implementation in the early 1970s of the prime program on a time-sharing system (...) and on a complex scientific computer. Confronting theory with practice uncovers what the programming paradigms fail to grasp: the interaction with the user and the interaction with the machine. (shrink)

Computational models of emotions have been thriving and increasingly popular since the 1990s. Such models used to be concerned with the emotions of individual agents when they interact with other agents. Out of the array of models for the emotions, we are going to devote special attention to the approach in Adamatzky’s Dynamics of Crowd-Minds. The reason it stands out, is that it considers the crowd, rather than the individual agent. It fits in computational intelligence. It works by mathematical simulation (...) on a crowd of simple artificial agents: by letting the computer program run, the agents evolve, and crowd behaviour emerges. Adamatzky’s purpose is to give an account of the emergence of allegedly “irrational” behaviour. This is not without problem, as the irrational to one person may seem entirely rational to another, and this in turn is an insight that, in the history of crowd psychology, has affected indeed the competition among theories of crowd dynamics. Quite importantly, Adamatzky’s book argues for the transition from individual agencies to a crowd’s or a mob’s coalesced mind as so, and at any rate for coalesced crowd’s agency. (shrink)

Nowadays, it has become almost a matter of course to say that the human mind is like a computer. Folks in all walks of life talk of ‘programming’ themselves, ‘multitasking’, running different ‘operating systems’, and sometimes of ‘crashing’ and being ‘rebooted’. Few who have used computers have not been touched by the appeal of the..

In philosophical logic and metaphysics there is a long-standing debate around the most appropriate structures to represent indeterministic scenarios concerning the future. We reconstruct here such a debate in a computational setting, focusing on the fundamental difference between moment-based and history-based structures. Our presentation is centered around two versions of an indeterministic scenario in which a programmer wants a machine to perform a given task at some point after a specified time. One of the two versions includes an assumption about (...) the future behaviour of the machine that cannot be encoded in any programming instruction; such version has models over history-based structures but no model over a moment-based structure. Therefore, our work adds a new stance to the debate: moment-based structures can be said to rule out certain indeterministic scenarios that are computationally unfeasible. (shrink)

Programming languages have developed significantly over the past century to provide complex models to think about and describe the world and processes of computation. Out of Alan Kay’s Smalltalk and a number of earlier languages, object-oriented programming has emerged as a preeminent mode of writing and organizing programs. Tracing the history of object-oriented programming from its origins in Simula and Sketchpad through Smalltalk, particularly its philosophical and technical developments, offers unique insights into philosophical questions about objects, language, (...) and our digital technologies. These early attempts to understand objects as basic elements of computation demonstrate the ways in which language, while firmly planted in the material reality of computation, must delimit objects from each other. This essay critically explores this history and explicates a theory of objects suggested by the development of object-oriented programming languages, which insists on the importance of language for representing and delimiting objects. It argues that the philosophies behind object-oriented programming are ultimately opposed to the claims of object-oriented ontology and find themselves more closely allied with philosophies that insist on the mediation of what exists through language. (shrink)

The purpose of this paper is to provide an account of the epistemology and metaphysics of universe creation on a computer. The paper begins with F.J.Tipler's argument that our experience is indistinguishable from the experience of someone embedded in a perfect computer simulation of our own universe, hence we cannot know whether or not we are part of such a computer program ourselves. Tipler's argument is treated as a special case of epistemological scepticism, in a similar vein (...) to `brain-in-a-vat' arguments. It is argued that the hypothesis that our universe is a program running on a digital computer in another universe generates empirical predictions, and is therefore a falsifiable hypothesis. The computer program hypothesis is also treated as a hypothesis about what exists beyond the physical world, and is compared with Kant's metaphysics of noumena. It is proposed that a theory about what exists beyond the physical world should be formulated with the precise concepts of mathematics, and should generate physical predictions. It is argued that if our universe is a program running on a digital computer, then our universe must have compact spatial topology, and the possibilities of observationally testing this prediction are considered. The possibility of testing the computer program hypothesis with the value of the density parameter Omega_0 is also analysed. The informational requirements for a computer to represent a universeexactly and completely are considered. Consequent doubt is thrown upon Tipler's claim that if a hierarchy of computer universes exists, we would not be able to know which `level of implementation' our universe exists at. It is then argued that a digital computer simulation of a universe cannot exist as a universe. However, the paper concludes with the acknowledgement that an analog computer simulation can be objectively related to the thing it represents, hence an analog computer simulation of a universe could, in principle, exist as a universe. (shrink)

Computational models will play an important role in our understanding of human higher‐order cognition. How can a model's contribution to this goal be evaluated? This article argues that three important aspects of a model of higher‐order cognition to evaluate are (a) its ability to reason, solve problems, converse, and learn as well as people do; (b) the breadth of situations in which it can do so; and (c) the parsimony of the mechanisms it posits. This article argues that fits of (...) models to quantitative experimental data, although valuable for other reasons, do not address these criteria. Further, using analogies with other sciences, the history of cognitive science, and examples from modern‐day research programs, this article identifies five activities that have been demonstrated to play an important role in our understanding of human higher‐order cognition. These include modeling within a cognitive architecture, conducting artificial intelligence research, measuring and expanding a model's ability, finding mappings between the structure of different domains, and attempting to explain multiple phenomena within a single model. (shrink)

Computer science only became established as a field in the 1950s, growing out of theoretical and practical research begun in the previous two decades. The field has exhibited immense creativity, ranging from innovative hardware such as the early mainframes to software breakthroughs such as programming languages and the Internet. Martin Gardner worried that "it would be a sad day if human beings, adjusting to the Computer Revolution, became so intellectually lazy that they lost their power of creative (...) thinking" (Gardner, 1978, p. vi-viii). On the contrary, computers and the theory of computation have provided great opportunities for creative work. This chapter examines several key aspects of creativity in computer science, beginning with the question of how problems arise in computer science. We then discuss the use of analogies in solving key problems in the history of computer science. Our discussion in these sections is based on historical examples, but the following sections discuss the nature of creativity using information from a contemporary source, a set of interviews with practicing computer scientists collected by the Association of Computing Machinery’s on-line student magazine, Crossroads. We then provide a general comparison of creativity in computer science and in the natural sciences. (shrink)

The pervasive use of computer simulations in the sciences brings novel epistemological issues discussed in the philosophy of science literature since about a decade. Evolutionary biology strongly relies on such simulations, and in relation to it there exists a research program (Artificial Life) that mainly studies simulations themselves. This paper addresses the specificity of computer simulations in evolutionary biology, in the context (described in Sect. 1) of a set of questions about their scope as explanations, the nature of (...) validation processes and the relation between simulations and true experiments or mathematical models. After making distinctions, especially between a weak use where simulations test hypotheses about the world, and a strong use where they allow one to explore sets of evolutionary dynamics not necessarily extant in our world, I argue in Sect. 2 that (weak) simulations are likely to represent in virtue of the fact that they instantiate specific features of causal processes that may be isomorphic to features of some causal processes in the world, though the latter are always intertwined with a myriad of different processes and hence unlikely to be directly manipulated and studied. I therefore argue that these simulations are merely able to provide candidate explanations for real patterns. Section 3 ends up by placing strong and weak simulations in Levins’ triangle, that conceives of simulations as devices trying to fulfil one or two among three incompatible epistemic values (precision, realism, genericity). (shrink)

his article will focus on the mechanistic origins of the computer metaphor, which forms the conceptual framework for the methodology of the cognitive sciences, some areas of artificial intelligence and the philosophy of mind. The connection between the history of computing technology, epistemology and the philosophy of mind is expressed through the metaphorical dictionaries of the philosophical discourse of a particular era. The conceptual clarification of this connection and the substantiation of the mechanistic components of the computer metaphor (...) is the main goal of this article. The statement is substantiated that the invention of mechanical computing devices, having a long history in the European engineering tradition, formed the prerequisites for the emergence of machine functionalism in the modern philosophy of mind. The idea of multiple implementation stems from the principle that a formal symbol system prescribes rules for the use of rational abstractions through the physical architecture of a computational engine. The article considers the reasons for the conceptual shift and reveals the semantic foundations for the metaphorical transfer of the properties of abstract objects from the theory of automata to the field of modern philosophy of mind. The criticism and ways of protecting the philosophical program of machine functionalism are analyzed by changing the content of the metaphor “Mind as machine”. The reasons for the stability of the information-computer approach in cognitive sciences are also disclosed and explained. (shrink)

The history of brain-computer interfaces developed from a mere idea in the days of early digital technology to today’s highly sophisticated approaches for signal detection, recording, and analysis. In the 1960s, electroencephalography was tied to the laboratory due to equipment and recording requirements. Today, amplifiers exist that are built in the electrode cap and are so resistant to movement artefacts that data collection in the field is no longer a critical issue. Within 60 years, the field has moved from (...) simple and artefact-sensitive EEG recording to making real the vision of brain-computer communication. In the last 40 years, direct brain-computer interaction went from simple communication programs to sophisticated BCI-controlled applications. In the past two decades, much research was conducted with locked-in individuals, and since the 2010s, independent home use by exemplary patients has been demonstrated. In these patients with locked-in syndrome, BCI were installed at their home and long-term usage was established, resulting in increased quality of life. Maintaining communication in disorders leading to LIS contributes significantly to the patients’ sense of being full persons. BCI as an assistive technology will likely be perceived as integral part of the self: insofar as it can prevent total loss of communication and the ensuing social isolation, it enables essential conditions for the subjective and intersubjective experience of personhood. (shrink)

The prelims comprise: Introduction: Two Semantic Projects History The Uses of Semantics Conclusions.

"The Natural System" is the name given to the underlying arrangement present in the diversity of life. Unlike a classification, which is made up of classes and members, a system or arrangement is an integrated whole made up of connected parts. In the pre-evolutionary period a variety of forms were proposed for the Natural System, including maps, circles, stars, and abstract multidimensional objects. The trees sketched by Darwin in the 1830s should probably be considered the first genuine evolutionary diagrams of (...) the Natural System—the first genuine evolutionary trees. Darwin refined his image of the Natural System in the well-known evolutionary tree published in the Origin of Species, where he also carefully distinguished between arrangements and classifications. Following the publication of the Origin, there was a great burst of evolutionary tree building, but interest in trees declined substantially after 1900, only to be revived in recent years with the development of cladistic analysis. ¶ While evolutionary trees are modern diagrams of the Natural System, they are at the same time instances of another broad class of diagrams that may be called "trees of history": branching diagrams of genealogical descent and change. During the same years that Darwin was sketching his first evolutionary trees, the earliest examples of two other trees of history also appeared: the first trees of language evolution and of manuscript genealogy. Though these were apparently independent of evolutionary trees in their origin, the similarities among all these trees of history, and among the historical processes that underlie them, were soon recognized. Darwin compared biological evolution and language evolution several times in the Origin of Species, and both Ernst Haeckel and the linguist August Schleicher made similar comparisons. Both linguists and stemmaticists (students of manuscript descent) understood the principle of apomorphy—the principle that only shared innovations provide evidence of common ancestry—more clearly than did systematists, and if there had been more cross-fertilization among these fields the cladistic revolution in systematics might well have taken place in the nineteenth century. ¶ Although historical linguists and stemmaticists have in some respects had sounder theory than have systematists, at least until recently, they have also had the practical problem of very large amounts of data, a problem not often faced by systematists until the advent of molecular sequencing. The opportunity now exists for systematists to contribute to the theory and practice of linguistics and stemmatics, their sister disciplines in historical reconstruction, through application of our commonly used computer programs for tree estimation. Preliminary results from the application of numerical cladistic analysis to a large stemmatic data set have been very encouraging, and have already generated much discussion in the stemmatics community. (shrink)

Studies of scientific practice demonstrate that the development of scientific models is an enactive and emergent process. Scientists make meaning through processes such as perspective taking, finding patterns, and following intuitions. In this paper, we focus on how a group of fourth grade learners and their teacher engaged in interpretation in ways that align with core ideas and practices in kinematics and computing. Cycles of measuring and modeling––including computer programming––helped to support classroom interactions that highlighted the interpretive nature (...) of modeling and participation in model construction as a knowledge-building process. We draw on literature from the history and philosophy of science in order to analyze the students’ interpretive actions as forms of epistemic and representational agency, constituting a construct we term disciplined interpretation. We demonstrate how students’ disciplined interpretative moves help to position them as owners of their own design decisions and their rights to interpret the phenomena they were modeling, data collected from those phenomena, and the scientific and computational models themselves. We present four extended episodes that characterize the nature of activity in the classroom and the development of students’ disciplined interpretations in terms of learning to recognize scientific patterns amid complex perceptual fields, and to represent them in ways that support sensemaking. (shrink)

The volume contains twenty essays devoted to the philosophy of mathematics and the history of logic. They have been divided into four parts: general philosophical problems of mathematics, Hilbert's program vs. the incompleteness phenomenon, philosophy of mathematics in Poland, mathematical logic in Poland. Among considered problems are: epistemology of mathematics, the meaning of the axiomatic method, existence of mathematical objects, distinction between proof and truth, undefinability of truth, Goedel's theorems and computer science, philosophy of mathematics in Polish mathematical and (...) logical schools, beginnings of mathematical logic in Poland, contribution of Polish logicians to recursion theory. (shrink)

The term 'consciousness' has not been consistently used in the history of philosophy and psychology. It has been taken to stand for the mental activity in which all of us are engaged during our waking lives, whether absorbed in the solving of a task or in calm moments of contemplation. It has also been allied with the term 'introspection' to stand for a self-monitoring activity, one in which we are not simply engaged, but in which we aware of the succession (...) of mental items that constitute our experiences. In Consciousness and the Computational Mind, Ray Jackendoff opts for this second sense and exploits it to criticize the "central processing" theory of consciousness. The background assumption for all such theories is that the human mind is to be conceived on the model of a computer whose central processor controls in accordance with programmed instructions the reception of inputs, delegation of tasks to secondary processors carrying out sub-routines, storage and retrieval of information, and finally the production of outputs. The central processing theory identifies consciousness with the activities of those areas of the brain which function as central processor as defined by this model. Jackendoff's alternative theory correlates consciousness with intermediate-level informational structures operated on by secondary processors. Of the higher-level structures and the processes operating on them, the level we refer to with such terms as 'conceptualization' and 'understanding,' we are, he contends, unconscious. (shrink)

Poreskoro, with three cat and four dog heads and a snake with a forked tongue as his tail, is responsible for epidemics of contagious diseases in Romany folklore. The Pishachas of Vedic mythology lurk in charnel houses and graveyards, waiting for humans to infect with madness. In Christian demonology, Pythius is known as the ruler of the eighth circle of the Inferno, bestowing heinous and unspeakable tortures on those who have committed fraud. Demons are the stuff of legends, and they (...) are everywhere in mythology and theology. These are just three of the more than thirty I counted that start with the letter P.How lucky we are for the advent of science. A rational tonic, it scatters our demons, demystifying the world for us—or so, at least, we think. As Canales brilliantly shows in this book, our assumption has been far off the mark. In physics, computer science, genetics, neuroscience, and economics, not to mention in the very foundations of rationality itself, an underworld of imaginary creatures has sustained and continues to inhabit modern science. Scientific demons appear in many guises: as atomic and subatomic particle manipulators, as feedback and parallel-processing experts, as messengers who can travel faster than the speed of light, as computer-code spies, gene and bit selectors in evolution and in informatics, as triggers of cascades from the nano level to the level of full-blown ecological and astrophysical theaters. By inventing them, we have learned that causality does not always hold at the level of atoms, that the speed of message transmission is limited, that living systems spend energy, that perpetual-motion machines are impossible, and that there are corners of the universe we have not imagined.We have all heard of Laplace's Demon, and Maxwell's, but who knew that, borrowing from physics, the historian Henry Adams considered the action of demons in geopolitics or that the economist Paul Samuelson analyzed their role in producing more efficient markets? Who knew that the generation that gave us the Internet and artificial intelligence were tasked by their teachers in graduate school to insert as many demons as possible into their programs? Who among us has heard of Darwin's Demon, or Maxwell-Szilárd-Brillouin's, or Monod's, or Loschmidt's? Who is aware of Norbert Weiner's use of demons in conceptualizing cybernetics, or of the great scholar of Kabbalah Gershom Scholem's comparison between Israel's superfast computers and the Golem?As Canales shows convincingly, it has been difficult for both historians and scientists to countenance demons in science, and so they have been written out, in many ways and many instances, of the histories of which they are part and parcel. Thomas Kuhn and even the more radical philosopher Paul Feyerabend both failed to acknowledge in their social critiques of science the extent to which an imaginary underworld of beings has been constitutive of the modern scientific imagination. Ironically, it was the man who provided a hard-and-fast criterion by which to distinguish scientific and nonscientific pursuits, Karl Popper, who ultimately saw the light. Revisiting his classic book The Logic of Scientific Discovery from 1934, he clarified in Postscript to the Logic of Scientific Discovery in 1982 the extent to which every historical era has been characterized by a particular demon. We moderns believe that science is about facts and numbers, but we are mistaken. As Canales beautifully summarizes the stakes of her project, “What first sets science on its path of discovery—then as now—is the continued use of the imagination.”Tracking the fate of demons in modern thought from Descartes to Lord Kelvin to H. G. Wells to David Bohm, and from Conrad Hal Waddington to Henry Compton to Stephen Hawking and Steven Pinker, Bedeviled assumes that the demons of the church or of paganism and the demons of science differ and resemble each other in ways that can be usefully elaborated. What is more crucial, however, is how such demons become real, how they morph from fears to hopes, and from thought experiments into real experiments, and ultimately to things in this world like computer programs, transistors, and nanorobots. From weather systems to messenger RNAs to black holes, demons emerge everywhere. The hard question is: How does the imagination actually drive discovery and understanding? How does it change our world?Perhaps it has been a sidetrack to consider whether we have or have never been modern. Clearly, what reason will not allow, it perennially recruits for a shadowland that helps us to better see our world. Rather than aiming to exorcise science, one day, of all of its methodological and epistemological ghosts, perhaps we might make peace with the notion that every culture struggles with distinguishing truth from falsehood in its own unique way. Such struggles are sensitive to their times and are accomplished through leaps of imagination. But fear not: the debunking of “Western rationality” casts no wet blanket on the project of Enlightenment. If anything, as Canales writes, “an account of science that considers how scientists themselves do science only risks adding new insights into our repertoire of knowledge.” Perhaps, therefore, there is solace to be gained from the dictum, Always choose the Devil you know. Ultimately, there may be no other way forward. (shrink)

The Lisp programming language is often described as the first functional programming language and also as an important early AI language. In the history of functional programming, however, it occupies a rather anomalous position, as the circumstances of its development do not fit well with the widely accepted view that functional languages have been developed through a theoretically-inspired project of deriving practical programming languages from the lambda calculus. This paper examines the origins of Lisp in the (...) early AI programming work of the mid-to-late 1950s, and in particular in the work of Allen Newell, Cliff Shaw and Herbert Simon. Their 1956 program, the Logic Theory Machine, introduced new ideas about data and program structures that were articulated in response to perceived limitations in existing programming technique. Later writers, notably John Backus, have described these features as constituting a “programming language style” distinct from the traditional style that preceded it. The paper examines the origins of the earlier style in practices of manual computation, analyses the key technical differences between it and the style first manifested in the Logic Theory Machine, and concludes that programming practice and experience play a large and underappreciated role in the development of programming styles and languages. (shrink)

As computers became multi-component systems in the 1950s, handling the speed differentials efficiently was identified as a major challenge. The desire for better understanding and control of ‘concurrency’ spread into hardware, software, and formalism. This paper examines the way in which the problem emerged and was handled across various computing cultures from 1955 to 1985. In the machinic culture of the late 1950s, system programs called ‘monitors’ were used for directly managing synchronisation. Attempts to reframe synchronisation in the subsequent algorithmic (...) culture pushed the problem to a higher level of abstraction; Dijkstra’s semaphores were a reaction to the algorithms’ complexity. Towards the end of the 1960s, the culture of ‘structured programming’ created a milieu in which Dijkstra, Hoare, and Brinch Hansen (among others) aimed for a concurrency primitive which embodied the new view of programming. Via conditional critical regions and Dijkstra’s ‘secretaries’, the co-produced ‘monitor’ appeared to provide the desired encapsulation. The construct received embodiment in a few programming languages; this paper ends by considering Modula and Concurrent Pascal. (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)

This paper describes the major components of ImpactCS, a program to develop strategies and curriculum materials for integrating social and ethical considerations into the computer science curriculum. It presents, in particular, the content recommendations of a subcommittee of ImpactCS; and it illustrates the interdisciplinary nature of the field, drawing upon concepts from computer science, sociology, philosophy, psychology, history and economics.

Turner argues that computer programs must have purposes, that implementation is not a kind of semantics, and that computers might need to understand what they do. I respectfully disagree: Computer programs need not have purposes, implementation is a kind of semantic interpretation, and neither human computers nor computing machines need to understand what they do.

Argument-mapping software abounds, and one of the reasons is that using the software has been shown to teach/promote/improve critical thinking skills. These positive results are very encouraging, but they also raise the question of whether the computer tutorial environment is producing these results, or whether learning argument mapping, even with just paper and pencil, is sufficient. Based on the results of two empirical studies, I argue that the basic skill of being able to represent an argument diagrammatically plays an (...) important role in the improvement of critical-thinking skills. While these studies do not offer a direct comparison between the two methods, it is important for anyone wishing to employ argument mapping in the classroom to know that significant results can be obtained even with the most rudimentary of tools. (shrink)

Programming computers to construct "real types," generally descriptive of a class of societies, makes explicit all steps in the thought process of such constructions because unambiguous instructions to, the computer are needed. The historian uses his judgment to choose a data field and variables that may be relevant in forming a type. He then looks for matches; he divides the data field into groups according to one variable and sees if the other variables differ significantly according to these (...) groups. In this process, he may discover new variables of importance. The selection of interesting types from among the matches can be determined only by the pragmatic interest of the historian; analysis of this selection process involves some of the most difficult problems in the study of historical technique. The computer using a "real type" program may be useful as a quick means to scan data. (shrink)