There are many branches of philosophy called “the philosophy of X,” where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments.

The Computational History of Philosophy of Science Dataset aims to be a comprehensive set of article and book chapter metadata for philosophy of science. The dataset covers the full run of over 40 journals and 3 major book series in the field. An automated author disambiguation script is used to construct canonical names for each author, and a combination of gender attribution methods is used to attribute the gender of each author. The full code used to generate (...) the dataset is available at [URL not allowed]. See the file data_dictionary.txt for data dictionary and additional information. (shrink)

Many researchers insist that computational methods will transform the historical profession, while an equally large number reject these claims as unwarranted hype. This study attempts to place the debate in historical and social context. The essay is divided into three parts. The first part offers a brief review of computational history. It asks whether the “computational turn” bears any resemblance to quantitative history and how it fits within the digital humanities. The second part describes the authors’ recent attempts (...) to apply computational methods to an existing project in the history of science using a standardized workflow. It demonstrates that each step of the workflow adds another layer of subjectivity. The third part reflects on what computational methods mean for the historical profession. It systematically reviews the positive aspects of computational history (open access, interdisciplinary collaboration, and new perspectives) as well as the negative aspects (inequality, fragility, and the threat of automation) and offers prescriptions based on the authors’ experiences. (shrink)

The birth, growth, stabilization and subsequent understanding of a new field of practical and theoretical enquiry is always a conceptual process including several typologies of events, phenomena an...

Review of: Margaret A. Boden, Mind as Machine: A History of Cognitive Science, 2 vols, Oxford: Oxford University Press, 2006, xlvii+1631, cloth $225, ISBN 0-19-924144-9. - Mind as Machine is Margaret Boden’s opus magnum. For one thing, it comes in two massive volumes of nearly 1700 pages, ... But it is not just the opus magnum in simple terms of size, but also a truly crowning achievement of half a century’s career in cognitive science.

Over the last few decades there have been several calls for a “big picture” of the history of science. There is a general need for a concise overview of the rise of modern science, with a clear structure allowing for a rough division into periods. This essay proposes such a scheme, one that is both elementary and comprehensive. It focuses on four machines, which can be seen to have mediated between science and society during successive periods (...) of time: the clock, the balance, the steam engine, and the computer. Following an extended developmental phase, each of these machines came to play a highly visible role in Western societies, both socially and economically. Each of these machines, moreover, was used as a powerful resource for the understanding of both inorganic and organic nature. More specifically, their metaphorical use helped to construe and refine some key concepts that would play a prominent role in such understanding. In each case the key concept would at some point be considered to represent the ultimate building block of reality. Finally, in a refined form, each of these machines would eventually make its entry in scientific research, thereby strengthening the ties between these machines and nature. (shrink)

Computational methods and perspectives can transform the history of science by enabling the pursuit of novel types of questions, dramatically expanding the scale of analysis , and offering novel forms of publication that greatly enhance access and transparency. This essay presents a brief summary of a computational research system for the history of science, discussing its implications for research, education, and publication practices and its connections to the open-access movement and similar transformations in the natural and (...) social sciences that emphasize big data. It also argues that computational approaches help to reconnect the history of science to individual scientific disciplines. (shrink)

The History of Ideas is presently enjoying a certain renaissance after a long period of disrepute. Increasing quantities of digitally available historical texts and the availability of computational tools for the exploration of such masses of sources, it is suggested, can be of invaluable help to historians of ideas. The question is: how exactly? In this paper, we argue that a computational history of ideas is possible if the following two conditions are satisfied: (i) Sound Method . A (...) computational history of ideas must be built upon a sound theoretical foundation for its methodology, and the only such foundation is given by the use of models , i.e., fully explicit and revisable interpretive frameworks or networks of concepts developed by the historians of ideas themselves. (ii) Data Organisation. Interpretive models in our sense must be seen as topic-specific knowledge organisation systems (KOS) implementable (i.e. formalisable) as e.g. computer science ontologies. We thus require historians of ideas to provide explicitly structured semantic framing of domain knowledge before investigating texts computationally, and to constantly re-input findings from the interpretive point of view. In this way, a computational history of ideas maximally profits from computer methods while also keeping humanities experts in the loop. We elucidate our proposal with reference to a model of the notion of axiomatic science in 18th -19th century Europe. (shrink)

The aim of this paper is to investigate the ascription of probabilities in a causal model of an episode in the history of science. The aim of such a quantitative approach is to allow the implementation of the causal model in a computer, to run simulations. As an example, we look at the beginning of the science of magnetism, “explaining” — in a probabilistic way, in terms of a single causal model — why the field advanced (...) in China but not in Europe (the difference is due to different prior probabilities of certain cultural manifestations). Given the number of years between the occurrences of two causally connected advances X and Y, one proposes a criterion for stipulating the value pY/X of the conditional probability of an advance Y occurring, given X. Next, one must assume a specific form for the cumulative probability function pY/X(t), which we take to be the time integral of an exponential distribution function, as is done in physics of radioactive decay. Rules for calculating the cumulative functions for more than two events are mentioned, involving composition, disjunction and conjunction of causes. We also consider the problems involved in supposing that the appearance of events in time follows an exponential distribution, which are a consequence of the fact that a composition of causes does not follow an exponential distribution, but a “hypoexponential” one. We suggest that a gamma distribution function might more adequately represent the appearance of advances. (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)

As part of an ongoing study of causal models in the history of science, a counterfactual scenario in the history of modern astronomy is explored with the aid of computer simulations. After the definition of “linking advance”, a possible world involving technological antecedence is described, branching out in 1510, in which the telescope is invented 70 years before its actual construction, at the time in which Fracastoro actually built the first prototelescope. By using the principle of (...) the closest possible world, we estimate that in this scenario the discovery of the elliptical orbit of Mars would by anticipated by only 28 years. The second part of the paper involves an estimate of the probability of the previous scenario, guided by the principle that the actual world is the mean and using computer simulations to create possible worlds in which the time spans between advances is varied according to a gamma distribution function. Taking into account the importance of the use of the diaphragm for the invention of the telescope, the probability that the telescope were built by 1538 for a branching time at 1510 is found to be smaller than 1%. The work shows that one of the important features of computational simulations in philosophy of science is to serve as a consistency check for the intuitions and speculations of the philosopher. (shrink)

In the popular imagination, the relevance of Turing's theoretical ideas to people producing actual machines was significant and appreciated by everybody involved in computing from the moment he published his 1936 paper ‘On Computable Numbers’. Careful historians are aware that this popular conception is deeply misleading. We know from previous work by Campbell-Kelly, Aspray, Akera, Olley, Priestley, Daylight, Mounier-Kuhn, Haigh, and others that several computing pioneers, including Aiken, Eckert, Mauchly, and Zuse, did not depend on Turing's 1936 universal-machine concept. Furthermore, (...) it is not clear whether any substance in von Neumann's celebrated 1945 ‘First Draft Report on the EDVAC’ is influenced in any identifiable way by Turing's work. This raises the questions: When does Turing enter the field? Why did the Association for Computing Machinery honor Turing by associating his name to ACM's most prestigious award, the Turing Award? Previous authors have.. (shrink)

Less than twenty years after computer science was able to establish itself as an academic discipline, a group of US computer scientists organized a conference on the history of programming languages. The conference is distinguished from other self-historicization projects by the organizer's claim to present an “accurate” account of their own discipline's history. However, the actors encountered challenges in terms of how to present their own history “objectively.” How to deal with incomplete memories? How (...) to avoid putting others in a bad light? There was far from agreement on these points and heated discussions ensued. Strategies had to be developed and compromises made to make the project a reality. The conference was intended to contribute to the history of computer science and the organizers sought exchange with professional historians. However, the project met with little interest among them. Rather, the discussion between the computer scientists and the historians shows how the history of science also struggled with questions of how to deal suitably with the past. In this respect, the Computer Scientists’ Conference not only illuminates the history of computer science, but also offers a new perspective on the history of the history of science. (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.

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)

This paper presents folk impressions of the University of Manchester’s difficulties in becoming a great university, but by means of a fiction imitating a distinguished writer from the Indian subcontinent. The impressions concern past efforts and the difficulties they faced.

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)

The investigation of a method for postulating counterfactual histories of science has led to the development of a theory of science based on general units of knowledge, which are called “advances”. Advances are passed on from scientist to scientist, and may be seen as “causing” the appearance of other advances. This results in networks which may be analyzed in terms of probabilistic causal models, which are readily encodable in computer language. The probability for a set of advances (...) to give rise to another advance is taken to be invariant through history, but depends on a typical time span which is an inverse function of the degree of development of science. Examples are given from the early science of magnetism and from the 19th century physics. (shrink)

Bernard Stiegler’s theoretical and practical Internation Project called for a refoundation of theoretical computer science that would also put the fact of exchange back at the centre of the conceptualization and organization of the economy. This can be interpreted as a call to critique a form of capitalism that has arisen over the past 70 years through an ideology via which ‘information’ conjoins computation and economics into what becomes an absolute market. But another history of exchange unfolds (...) contemporaneously with this ideology: that pursued in responses to Marcel Mauss’s anthropology of the gift, by Claude Lévi-Strauss, Georges Bataille and Maurice Godelier, among others. Stiegler’s ‘neganthropology’ equally responds to and is informed by this other history of the fact of exchange but, by looking at these works more closely, it may be possible to pursue Stiegler’s project in fields such as kinship and sexuality, which were not his direct concern. (shrink)

We present the history of the conference Applications of Algebra in Logic and Computer Science, whose twenty-third edition will be held in March, 2019. At the end we outline some plans for the future.

This paper examines several issues related to information, speed of computation, and simulation of a physical process. It is argued that mental processes proceed at a rate close to the optimal based on thermodynamic considerations. Problems related to the simulation of a quantum mechanical system on a computer are reviewed. Parallels are drawn between biological and adaptive quantum systems.

How can we best reconstruct the origin of a notion, its development, and possible spread to multiple fields? We present a pilot study on the spread of the notion of conceptual scheme. Though the notion is philosophically important, its origin, development, and spread are unclear. Several purely qualitative and competing historical hypotheses have been offered, which rely on disconnected disciplinary traditions, and have never been tested all at once in a single comprehensive investigation fitting the scope of its subject matter. (...) As a step toward such an investigation, we trace the use of the bigram “conceptual scheme” in about 42,000 US journal articles in social sciences from 1888-1959 by using a novel method combining a quantitative procedure aided by basic computational techniques with qualitative elements informed by Betti and van den Berg (2014)’s ‘model approach to the history of ideas’. (shrink)

In this essay, I examine the notion of a nondeterministic algorithm from both a conceptual and historical point of view. I argue that the intuitions underwriting nondeterminism in the context of contemporary theoretical computer science cannot be reconciled with the intuitions that originally motivated nondeterminism. I identify four different intuitions about nondeterminism: nondeterminism as evidence for the Church Turing thesis; nondeterminism as a natural reflection of the mathematician's behavior; nondeterminism as a formal, mathematical generalization; and nondeterminism as a (...) physical process. I shall argue that there are irreconcilable tensions among these intuitions and, moreover, that these tensions have not been acknowledged in the conceptual development of theoretical computer science. In fact, a careful review of the received history reveals a number of gaps and discontinuities. For instance, I examine the seminal writings of Turing and argue that the formal introduction of the nondeterminism is to be found there and not, as is often supposed, in the research of the late 1950s and early 1960s. I also examine the period after 1960 and argue that even the more recent developments concerning nondeterminism are hard to reconstruct. ;Although I firmly believe that a rigorous philosophical account of nondeterminism is needed, I am not sure that such an account will bring us any closer to a solution to any of the notoriously difficult theoretical problems that turn on the notion of a nondeterministic algorithm. I do believe, however, that a clear conceptual account of nondeterminism will shed light on many long-standing philosophical problems. I conclude by pointing to a number of philosophical questions that resonate with issues raised by the foregoing investigation of nondeterministic algorithms. (shrink)

Science is studied in very different ways by historians, philosophers, psychologists, and sociologists. Not only do researchers from different fields apply markedly different methods, they also tend to focus on apparently disparate aspects of science. At the farthest extremes, we find on one side some philosophers attempting logical analyses of scientific knowledge, and on the other some sociologists maintaining that all knowledge is socially constructed. This paper is an attempt to view history, philosophy, psychology, and sociology of (...) science from a unified perspective. (shrink)

This book constitutes the refereed proceedings of the 9th Conference on Computability in Europe, CiE 2013, held in Milan, Italy, in July 2013. The 48 revised papers presented together with 1 invited lecture and 2 tutorials were carefully reviewed and selected with an acceptance rate of under 31,7%. Both the conference series and the association promote the development of computability-related science, ranging over mathematics, computer science and applications in various natural and engineering sciences such as physics and (...) biology, and also including the promotion of related non-scientific fields such as philosophy and history of computing. (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)

Since the birth of computing as an academic discipline, the disciplinary identity of computing has been debated fiercely. The most heated question has concerned the scientific status of computing. Some consider computing to be a natural science and some consider it to be an experimental science. Others argue that computing is bad science, whereas some say that computing is not a science at all. This survey article presents viewpoints for and against computing as a science. (...) Those viewpoints are analyzed against basic positions in the philosophy of science. The article aims at giving the reader an overview, background, and a historical and theoretical frame of reference for understanding and interpreting some central questions in the debates about the disciplinary identity of computer science. The article argues that much of the discussion about the scientific nature of computing is misguided due to a deep conceptual uncertainty about science in general as well as computing in particular. (shrink)

Millions of scientific articles are published each year, making it difficult to stay abreast of advances within even the smallest subdisciplines. Traditional approaches to the study of science, such as the history and philosophy of science, involve closely reading a relatively small set of journal articles. And yet many questions benefit from casting a wider net: Is most scientific change gradual or revolutionary? What are the key sources of scientific novelty? Over the past several decades, a massive (...) effort to digitize the academic literature and equip computers with algorithms that can distantly read and analyze a digital database has taken us one step closer to answering these questions. The Dynamics of Science brings together a diverse array of contributors to examine the largely unexplored computational frontiers of history and philosophy of science. Together, they reveal how tools and data from automated textual analysis, or machine "reading," combined with methods and models from game theory and cultural evolutionary theory, can begin to answer fundamental questions about the nature and history of science. (shrink)

The project “Artificial and Artistic Vision. Computer Vision and Art History in Practical-Methodical Cooperation” is interdisciplinary by definition and also in its personnel composition and combines the humanities, engineering, and natural sciences. Together, prototypes and methodological approaches to an automatic vision that assists art history are being developed in the form of basic research.

In 1839 the working hours of the computers employed on the lunar and planetary reductions of the Royal Observatory, Greenwich were reduced from eleven hours to eight hours. Previous historians have explained this decrease by reference to the generally benevolent nature of the manager of the reductions, George Biddell Airy. By contrast, this article uses the letters and notes exchanged between Airy and the computers to demonstrate that the change in the working hours originated from the computers as a reaction (...) to their poor working conditions. Through the exploration of these archival materials, the article shifts the focus of the analysis to the working experience of the computers, rather than to the administrative history of the project that inevitably tends to highlight Airy's actions. By doing so, the article shows how the computers were treated as a disposable low-skilled workforce, as opposed to aspiring astronomers with considerable mathematical talent. Through this reframing, the article takes a step towards a working history of the observatory. (shrink)

This book uses computer modeling to investigate trends in what is published in leading philosophy journals over the last century and a half. The notable trends include the rise of realism from a fringe view to the mainstream metaphysical outlook, the increase in specialization, and the increasing depth of integration between philosophy and physical sciences. It also contains a guide to how to do similar investigations, and discussions of the strengths and weaknesses of the approach.

Politics and Modernity provides a critical review of the key interface of contemporary political theory and social theory about the questions of modernity and postmodernity. Review essays offer a broad-ranging assessment of the issues at stake in current debates. Among the works reviewed are those of William Connolly, Anthony Giddens, J[um]urgen Habermas, Alasdair MacIntyre, Richard Rorty, Charles Taylor and Roy Bhaskar. As well as reviewing the contemporary literature, the contributors assess the historical roots of current problems in the works of (...) Nietzsche and Max Weber. (shrink)

This is a handbook, written by a number of authors from different research communities. Even though the subject matter of the book is reasonably unified, the divergence between their communities – firstly category theory and type theory, secondly universal algebra, and thirdly classical logic and complexity theory -- makes it difficult to get an overview; these communities are notorious for their poor relations with each other. Thus, even though it would be tempting to envisage a world in which the various (...) chapters would be written so as to illuminate each other, and in which prospective readers would have a broad enough background to be able to appreciate all of this book, it is not the world that we live in. (shrink)

Natural philosophy encompassed all natural phenomena of the physical world. It sought to discover the physical causes of all natural effects and was little concerned with mathematics. By contrast, the exact mathematical sciences were narrowly confined to various computations that did not involve physical causes, functioning totally independently of natural philosophy. Although this began slowly to change in the late Middle Ages, a much more thoroughgoing union of natural philosophy and mathematics occurred in the seventeenth century and thereby made the (...) Scientific Revolution possible. The title of Isaac Newton's great work, The Mathematical Principles of Natural Philosophy, perfectly reflects the new relationship. Natural philosophy became the 'Great Mother of the Sciences', which by the nineteenth century had nourished the manifold chemical, physical, and biological sciences to maturity, thus enabling them to leave the 'Great Mother' and emerge as the multiplicity of independent sciences we know today. (shrink)

Computationalism says that brains are computing mechanisms, that is, mechanisms that perform computations. At present, there is no consensus on how to formulate computationalism precisely or adjudicate the dispute between computationalism and its foes, or between different versions of computationalism. An important reason for the current impasse is the lack of a satisfactory philosophical account of computing mechanisms. The main goal of this dissertation is to offer such an account.
I also believe that the history of computationalism sheds light on (...) the current debate. By tracing different versions of computationalism to their common historical origin, we can see how the current divisions originated and understand their motivation. Reconstructing debates over computationalism in the context of their own intellectual history can contribute to philosophical progress on the relation between brains and computing mechanisms and help determine how brains and computing mechanisms are alike, and how they differ. Accordingly, my dissertation is divided into a historical part, which traces the early history of computationalism up to 1946, and a philosophical part, which offers an account of computing mechanisms.
The two main ideas developed in this dissertation are that (1) computational states are to be identified functionally not semantically, and (2) computing mechanisms are to be studied by functional analysis. The resulting account of computing mechanism, which I call the functional account of computing mechanisms, can be used to identify computing mechanisms and the functions they compute. I use the functional account of computing mechanisms to taxonomize computing mechanisms based on their different computing power, and I use this taxonomy of computing mechanisms to taxonomize different versions of computationalism based on the functional properties that they ascribe to brains. By doing so, I begin to tease out empirically testable statements about the functional organization of the brain that different versions of computationalism are committed to. I submit that when computationalism is reformulated in the more explicit and precise way I propose, the disputes about computationalism can be adjudicated on the grounds of empirical evidence from neuroscience. (shrink)

The history of scientific illustrations is a story that correspond the cultural, economic, political and scientific history of the world. A look into the history of sciences displays that pictures and illustrations had a decisive role for the sciences progressive success and rising societal status from the sixteenth century. The illustrations visualized the unknown to graspable facts. Without the pictures the new discovered continents, the blood circulatory system and the body’s muscles had remained theoretical proclamations. The scientific (...) discoveries became visible and communicated, to a wider audience by its illustrations. The scientific illustrations and maps were intertwined with an epistemic ambition to unveil the true natural order. During the seventeenth century the concept of objectivity was interpreted as a quest for revealing nature’s ideal order, a task only feasible for the brilliant artist to accomplish. The epistemic ambition concurred with the belief that only one true ontological order existed that the scientific knowledge had to uncover. This concept of objectivity was succeeded by the modern concept of objectivity which equated objectivity with impartiality and elimination of the scientist’s subjective bias. The view from “nowhere” is still a valid, ruling definition of objectivity. However, the presence and huge expansion of computational pictures in the sciences as well in everyday life raises the question if a new sense of objectivity is framed. In physics and chemistry the produced pictures intend to be contributions to an ongoing theoretical discussion, about a nature in constant flux, let it be molecules or artificially processed materials in the nanoscale. The traveller designs her own map in advance, mixing the Google earth features with the personal arrangements. For both the scientists and the laymen the modernist objective virtues of detachment, impartiality and disinterestedness have been supplemented by a return of subjective involvements. (shrink)

Theories related to cognitive sciences, Human-in-the-loop Cyber-physical systems, data analysis for decision-making, and computational ethics make clear the need to create transdisciplinary learning, research, and application strategies to bring coherence to the paradigm of a truly human-oriented technology. Autonomous objects assume more responsibilities for individual and collective phenomena, they have gradually filtered into routines and require the incorporation of ethical practice into the professions related to the development, modeling, and design of algorithms. To make this possible, it is pertinent and (...) urgent to bring them closer to the problems and approaches of the humanities. Increasingly transdisciplinary research must be part of the construction of systems that provide developers and scientists with the necessary bases to understand the ethical debate and therefore their commitment to society. This article considers two theories as articulating axes: Blumenberg’s, coming from the field of philosophy, for whom the process of technification and especially the implementation of mathematical models in their algorithmic form leads to an emptying of meaning and therefore makes programmers who implement their functions to be alien to the concerns that gave them origin; Daston’s, belonging to the field of the history of science and according to which the division of labor in the processes of technification of the calculation implies a kind of subordination in which those who implement the inventions of a small group of privileged mathematicians ignore the procedures that put them into operation. Given these two theories, the black box models prevalent in AI development, and the urgency of establishing explanatory frameworks for the development of computational ethics, this article exposes the need to give a voice to the cultural history of consciousness for promoting the discussions around the implementation of mathematical algorithms. The paper takes as a reference the different points of view that have emerged around the study of technological ethics, its applicability, management, and design. It criticizes the current state of studies from a humanistic perspective and explains how the historical perspective allows promoting the training of software engineers, developers and creators so that they assume intuitions and moral values in the development of their work. Specifically, it aims to expose how cultural history, applied to the study of consciousness and its phenomena, makes those involved in this technological revolution aware of the effect that they, through their algorithms, have on society in general and on human beings in particular. (shrink)