This study examined 4-year-olds’ problem-solving under different social conditions. Children had to use water in order to extract a buoyant object from a narrow tube. When faced with the problem ‘cold’ without cues, nearly all children were unsuccessful. But when a solution-suggesting video was pedagogically delivered prior to the task, most children succeeded. Showing children the same video in a non-pedagogical manner did not lift their performance above baseline and was less effective than framing it pedagogically. The (...) findings support ideas central to natural pedagogy, 148–153, 2009). They also challenge the Cultural Intelligence hypothesis, according to which only humans’ social, but not their physical, cognition differs qualitatively from that of great apes. A more radical, transformative variant of the Cultural Intelligence hypothesis is suggested according to which humans’ physical cognition is shaped by their social nature and must therefore be recognized as equally distinctive as their social cognition. (shrink)

John Dewey attempted a pragmatic aufhebung of the disparate methodological aims of social science-explanation, understanding, and critique- in his 1938 Logic: the theory of Inquiry. There, in his penultimate chapter ‘Social Inquiry’, Dewey performed a trademark implementation of his deflation of absolutistic and universalistic pretensions in intellectual and theoretical discourse, in this case with respect to any one approach to social science. This deflation--as elsewhere in his analogous treatments of epistemology, ethics, and the theory of action-- involved the (...) reconstruction of the claims of the naturalist, interpretivist, and critical schools of social science into one overall pattern of social inquiry. This recasts the different and seemingly irreconcilable aims of these schools into a series of steps in a practice. That these claims, then, simultaneously stand independently but in varying degrees of tension with, and support of, each other is a hallmark of pragmatism’s embrace of pluralism in intelligent problem solving. Dewey’s discussion of interpretation needs supplementation from his broader philosophical commitments in order to see the full sense of both the compatibility and the incompatibility of his theory with philosophical hermeneutics. (shrink)

This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important or creative changes in theories and concepts. It includes revised contributions presented during the international conference on Model-Based Reasoning (MBR’015), held on June 25-27 in Sestri Levante, Italy. The book is divided into three main parts, the first of which focuses on models, reasoning and representation. It highlights key theoretical concepts from an applied perspective, addressing issues concerning (...) information visualization, experimental methods and design. The second part goes a step further, examining abduction, problem solving and reasoning. The respective contributions analyze different types of reasoning, discussing various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of historical, epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies in experimental research, this part aims at fostering new discussions and stimulating new ideas. All in all, the book provides researchers and graduate students in the field of applied philosophy, epistemology, cognitive science and artificial intelligence alike with an authoritative snapshot of current theories and applications of model-based reasoning. (shrink)

PLEASE NOTE: This is the corrected 2nd eBook edition, 2021. ●●●●● _Critique of Impure Reason_ has now also been published in a printed edition. To reduce the otherwise high price of this scholarly, technical book of nearly 900 pages and make it more widely available beyond university libraries to individual readers, the non-profit publisher and the author have agreed to issue the printed edition at cost. ●●●●● The printed edition was released on September 1, 2021 and is now available through (...) all booksellers, including Barnes & Noble, Amazon, and brick-and-mortar bookstores under ISBN 978-0-578-88646-6. ●●●●● In light of the length of this book, readers who would like to have a more detailed description of the book's objectives and method may find it helpful to read the detailed and clearly written Wikipedia entry about this work: From the Wikipedia search page, use the search phrase "Critique of Impure Reason". At least at the time of this writing (11/29/2021), the Wikipedia entry is well-researched and accurate. ●●●●● In addition, a "Primer on Bartlett's CRITIQUE OF IMPURE REASON" has been made available by the author. It is available under its title through PhilPapers and other philosophy online archives. ●●●●● COMMENDATIONS OF THIS WORK, from the back cover of the published edition: ●●●●● “I admire its range of philosophical vision.” – Nicholas Rescher, Distinguished University Professor of Philosophy, University of Pittsburgh, author of more than 100 books. ●●●●● “Bartlett’s _Critique of Impure Reason_ is an impressive, bold, and ambitious work. Careful scholarship is balanced by original analyses that lead the reader to recognize the limits of meaning, knowledge, and conceptual possibility. The work addresses a host of traditional philosophical problems, among them the nature of space, time, causality, consciousness, the self, other minds, ontology, free will and determinism, and others. The book culminates in a fascinating and profound new understanding of relativity physics and quantum theory.” – Gerhard Preyer, Professor of Philosophy, Goethe-University, Frankfurt am Main, Germany, author of many books including _Concepts of Meaning_, _Beyond Semantics and Pragmatics_, _Intention and Practical Thought_, and _Contextualism in Philosophy_. ●●●●● “[This work’s] goal is of a unique and difficult species: Dr. Bartlett seeks to develop a formal logical calculus on the basis of transcendental philosophical arguments; in fact, he hopes that this calculus will be the formal expression of the transcendental foundation of knowledge.... I consider Dr. Bartlett’s work soundly conceived and executed with great skill.” – C. F. von Weizsäcker, philosopher and physicist, former Director, Max-Planck-Institute, Starnberg, Germany. ●●●●● “Bartlett has written an American “Prolegomena to All Future Metaphysics.” He aims rigorously to eliminate meaningless assertions, reach bedrock, and place philosophy on a firm foundation that will enable it, like science and mathematics, to produce lasting results that generations to come can build on. This is a great book, the fruit of a lifetime of research and reflection, and it deserves serious attention.” — Martin X. Moleski, former Professor, Canisius College, Buffalo, NY, studies of scientific method, the presuppositions of thought, and the self-referential nature of epistemology. ●●●●● “Bartlett has written a book on what might be called the underpinnings of philosophy. It has fascinating depth and breadth, and is all the more striking due to its unifying perspective based on the concepts of reference and self-reference.” – Don Perlis, Professor of Computer Science, University of Maryland, author of numerous publications on self-adjusting autonomous systems and philosophical issues concerning self-reference, mind, and consciousness. ●●●●● ●●●●● The _Critique of Impure Reason: Horizons of Possibility and Meaning_ comprises a major and important contribution to philosophy. Thanks to the generosity of its publisher, this massive 885-page volume has been published as a free open access eBook (3.75MB) as well as an open access printed edition. It inaugurates a revolutionary paradigm shift in philosophical thought by providing compelling and long-sought-for solutions to a wide range of philosophical problems. In the process, the work fundamentally transforms the way in which the concepts of reference, meaning, and possibility are understood. The book includes a Foreword by the celebrated German philosopher and physicist Carl Friedrich von Weizsäcker. ●●●●● In Kant’s _Critique of Pure Reason_ we find an analysis of the preconditions of experience and of knowledge. In contrast, but yet in parallel, the new _Critique_ focuses upon the ways—unfortunately very widespread and often unselfconsciously habitual—in which many of the concepts that we employ _conflict_ with the very preconditions of meaning and of knowledge. ●●●●● This is a book about the boundaries of frameworks and about the unrecognized conceptual confusions in which we become entangled when we attempt to transgress beyond the limits of the possible and meaningful. We tend either not to recognize or not to accept that we all-too-often attempt to trespass beyond the boundaries of the frameworks that make knowledge possible and the world meaningful. ●●●●● The _Critique of Impure Reason_ proposes a bold, ground-breaking, and startling thesis: that a great many of the major philosophical problems of the past can be solved through the recognition of a viciously deceptive form of thinking to which philosophers as well as non-philosophers commonly fall victim. For the first time, the book advances and justifies the criticism that a substantial number of the questions that have occupied philosophers fall into the category of “impure reason,” violating the very conditions of their possible meaningfulness. ●●●●● The purpose of the study is twofold: first, to enable us to recognize the boundaries of what is referentially forbidden—the limits beyond which reference becomes meaningless—and second, to avoid falling victims to a certain broad class of conceptual confusions that lie at the heart of many major philosophical problems. As a consequence, the boundaries of _possible meaning_ are determined. ●●●●● Bartlett, the author or editor of more than 20 books, is responsible for identifying this widespread and delusion-inducing variety of error, _metalogical projection_. It is a previously unrecognized and insidious form of erroneous thinking that undermines its own possibility of meaning. It comes about as a result of the pervasive human compulsion to seek to transcend the limits of possible reference and meaning. ●●●●● Based on original research and rigorous analysis combined with extensive scholarship, the _Critique of Impure Reason_ develops a self-validating method that makes it possible to recognize, correct, and eliminate this major and pervasive form of fallacious thinking. In so doing, the book provides at last provable and constructive solutions to a wide range of major philosophical problems. ●●●●● CONTENTS AT A GLANCE ▪▪▪▪▪ Preface ▪▪▪▪▪ Foreword by Carl Friedrich von Weizsäcker ▪▪▪▪▪ Acknowledgments ▪▪▪▪▪ Avant-propos: A philosopher’s rallying call ▪▪▪▪▪ Introduction ▪▪▪▪▪ A note to the reader ▪▪▪▪▪ A note on conventions ▪▪▪▪▪ ▪▪▪▪▪ ▪▪▪▪▪ PART I ▪▪▪▪▪ ▪▪▪▪▪ WHY PHILOSOPHY HAS MADE NO PROGRESS AND HOW IT CAN ▪▪▪▪▪ 1 Philosophical-psychological prelude ▪▪▪▪▪ 2 Putting belief in its place: Its psychology and a needed polemic ▪▪▪▪▪ 3 Turning away from the linguistic turn: From theory of reference to metalogic of reference ▪▪▪▪▪ 4 The stepladder to maximum theoretical generality ▪▪▪▪▪ ▪▪▪▪▪ ▪▪▪▪▪ PART II ▪▪▪▪▪ THE METALOGIC OF REFERENCE ▪▪▪▪▪ A New Approach to Deductive, Transcendental Philosophy ▪▪▪▪▪ 5 Reference, identity, and identification ▪▪▪▪▪ 6 Self-referential argument and the metalogic of reference ▪▪▪▪▪ 7 Possibility theory ▪▪▪▪▪ 8 Presupposition logic, reference, and identification ▪▪▪▪▪ 9 Transcendental argumentation and the metalogic of reference ▪▪▪▪▪ 10 Framework relativity ▪▪▪▪▪ 11 The metalogic of meaning ▪▪▪▪▪ 12 The problem of putative meaning and the logic of meaninglessness ▪▪▪▪▪ 13 Projection ▪▪▪▪▪ 14 Horizons ▪▪▪▪▪ 15 De-projection ▪▪▪▪▪ 16 Self-validation ▪▪▪▪▪ 17 Rationality: Rules of admissibility ▪▪▪▪▪ ▪▪▪▪▪ ▪▪▪▪▪ PART III ▪▪▪▪▪ PHILOSOPHICAL APPLICATIONS OF THE METALOGIC OF REFERENCE ▪▪▪▪▪ Major Problems and Questions of Philosophy and the Philosophy of Science ▪▪▪▪▪ 18 Ontology and the metalogic of reference ▪▪▪▪▪ 19 Discovery or invention in general problem-solving, mathematics, and physics ▪▪▪▪▪ 20 The conceptually unreachable: “The far side” ▪▪▪▪▪ 21 The projections of the external world, things-in-themselves, other minds, realism, and idealism ▪▪▪▪▪ 22 The projections of time, space, and space-time ▪▪▪▪▪ 23 The projections of causality, determinism, and free will ▪▪▪▪▪ 24 Projections of the self and of solipsism ▪▪▪▪▪ 25 Non-relational, agentless reference and referential fields ▪▪▪▪▪ 26 Relativity physics as seen through the lens of the metalogic of reference ▪▪▪▪▪ 27 Quantum theory as seen through the lens of the metalogic of reference ▪▪▪▪▪ 28 Epistemological lessons learned from and applicable to relativity physics and quantum theory ▪▪▪▪▪ ▪▪▪▪▪ PART IV ▪▪▪▪▪ HORIZONS ▪▪▪▪▪ 29 Beyond belief ▪▪▪▪▪ 30 _Critique of Impure Reason_: Its results in retrospect ▪▪▪▪▪ ▪▪▪▪▪ SUPPLEMENT ▪▪▪▪▪ The Formal Structure of the Metalogic of Reference ▪▪▪▪▪ APPENDIX I ▪▪▪▪▪ The Concept of Horizon in the Work of Other Philosophers ▪▪▪▪▪ APPENDIX II ▪▪▪▪▪ Epistemological Intelligence ▪▪▪▪▪ References ▪▪▪▪▪ Index ▪▪▪▪▪ About the author. 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It has been argued that our currently most satisfactory social epistemology of science can’t account for science that is based on artificial intelligence (AI) because this social epistemology requires trust between scientists that can take full responsibility for the research tools they use, and scientists can’t take full responsibility for the AI tools they use since these systems are epistemically opaque. I think this argument overlooks that much AI-based science can be done without opaque models, and that (...) agents can take full responsibility for the systems they use even if these systems are opaque. Requiring that an agent fully understand how a system works is an untenably strong condition for that agent to take full responsibility for the system and risks absolving AI developers from responsibility for their products. AI-based science need not create trust-related social epistemological problems if we keep in mind that what makes both individual scientists and their use of AI systems trustworthy isn’t full transparency of the internal processing but their adherence to social and institutional norms that ensure that scientific claims can be trusted. (shrink)

B a c k g r o u n d. This scientific investigation conducts a constructive-critical, comprehensive, and systematic analysis of the state, paradigms, and trends in the development of Ukrainian and European fundamental science. It highlights, at a synthetic level, the epistemological and social functions, historical mission, and vision of domestic science in shaping the traditions and values of European culture. The article draws attention to the significant contributions made by domestic scientists to the research of theoretical-methodological, (...) epistemological, and worldview foundations of Ukrainian fundamental science as an intellectual phenomenon. This topic is covered in extensive monographic works, articles, discourses, and scientific investigations by such scientists as B. Paton, A. Zahorodnii, A. Naumovets, D. Zerbino, O. Habovych, V. Kuznietsov, L. Kurylo, N. Semenova, S. Honcharenko, A. Pavko, Z. Partiko, A. Filipenko, and others. Valuable, little-studied, and debatable aspects of this issue have also been highlighted in the works of foreign scientists. Among them, the author of the article includes interdisciplinary publications by J. Barroso, J. Gorgon, V. Humboldt, K. Popper, and other Western thinkers and researchers. At the same time, there are practically no professional generalizing works in domestic socio-humanitarian thought that consider Ukrainian fundamental science from a comprehensive, synthetic perspective as a powerful intellectual factor in shaping the traditions and values of European culture. M e t h o d s. The article uses a set of methods (analysis, synthesis, inductive and deductive methods) as well as general scientific approaches such as dialectical, systematic, and interdisciplinary methods to study this socio-humanitarian problem. This has led to the revelation of the internal contradictory nature of trends in the development of fundamental science, the evaluation of Ukrainian science in the system of European values and culture. Meanwhile, the dialectical method provides the theoretical and methodological foundation for a deep, essential understanding of the intellectual potential of fundamental research. In contrast, the systematic approach used in the article is extrapolated to conduct a correct, comprehensive analysis of the integral components that determine the regularities and features of the development of domestic science in modern conditions. The interdisciplinary approach, creatively applied in the article, made it possible to combine achievements in the field of social humanities, which significantly determine the determinants of the methodological culture of scientific analysis, critical thinking in the process of producing and assimilating scientific knowledge. R e s u l t s. The article identifies the following trends in the development of Ukrainian fundamental science as an institutional factor in shaping the traditions and values of European culture: Powerful, systematic development of fundamental science is a necessary condition for improving the mechanisms of deepening European integration processes in Ukraine. Modern fundamental science is one of the components of the socio-cultural progress of humanity and defines such important characteristics as the differentiation and integration of science, computerization, and humanization of each of them. The historical experience of the development of European civilization shows that science, both in the past and in the present, has been and remains an important intellectual factor, a reliable rational foundation of strong states and nations. C o n c l u s i o n s. The conclusion establishes that the scientific potential of the state is an integral component of European culture and the intellect of the modern Ukrainian nation. It is important to consider that European values should have a profound and irreversible nature for domestic science rather than a temporary one. The preservation and modernization of national intellect, intellectual capital are essential conditions for its integration into the educational, scientific, and cultural space of Europe, and for the preservation and development of Ukraine as a modern, scientifically powerful democratic state with established civilizational traditions. The article emphasizes that the constant pursuit of intellectual and political freedom, patriotism, tireless industriousness, intelligence, education, creativity, intellect, healthy pragmatism, persistence, courage, ingenuity, and a sense of humor are precisely the universal, distinctly defined, characteristic traits of our compatriots that make them resilient and open up a unique intellectual space in Europe, a wonderful European world of existence. (shrink)

The paper analysis the strategies and concepts developed in the world in modern directions: innova- tive economy, digital economy, artificial intelligence, Industry 4.0 and others. The problem is to determine the initial fundamental parameters of order and their prospects in the global world, the definition and principles of artificial intel- ligence systems, its structure and important aspects and principles of future science and technology in analysis and synthesis based on synergetic approaches, innovative, information, converged technologies, taking into account (...) the design of future and viable and safe and sustainable development in the context of Industry 4.0, digital economy and Society 5.0. The general scheme of the integrated model of sustainable and socio-humanitarian development in the global system is proposed, which the authors call the noosphere model of sustainable and safe development as an important fundamental platform for the design and creation of AIS. In the XXI century education, science and innova- tive technologies becomes the «basis» of the spiritual and material reproduction of man, transforming the society of the XXI century into an educational society, ensures compliance with the law of advanced development of human quality, quality of social intelligence and quality of educational systems in society. noosphere evolution. Industry 4.0 is the next stage in the digitalization of production technologies with the accelerated introduction of new developments and cyberphysical systems, which is changing business models. Accordingly, Industry 4.0 as a separate segment of digital manufacturing technologies takes its place among other digital sectors. The analysis of the Strategies and Concepts developed in the world in modern directions: innovative economy, digital economy, artificial intelli- gence, Industry 4.0 and others is carried out in the work. The problem is to determine the initial fundamental para- meters of order and their prospects in the global world, the definition and principles of artificial intelligence systems, its structure and important aspects and principles of future science and technology in analysis and synthesis ba- sed on synergetic approaches, innovative, information, converged technologies, taking into account the design of future and viable and safe and sustainable development in the context of Industry 4.0, DE and Society 5.0. The main scientific and technological factors in the 21st century will be the development of: Artificial Intelligence, Nano Technologies, Bio-Media Technologies, Cognitive Technologies and Socio-Humanitarian Technologies. More specifically – this is the modern development of Innovative Technologies: «NBIC SH». Therefore, it is necessary to note the importance of taking into account the principle of I2K2+SH, ie: Intellectualization, Integration, Convergence, Coevolution and Socio- humanitarian technologies. It is important and necessary to take into account modern principles of designing a su- stainable and secure AIS and solving the problem of Harmonization between 2 worlds: Real and Virtual, especially in their hybridization. Our immediate future is a hybrid nonlinear world. Note that digitalization, artificial intelligence and robotics will be the main drivers of global economic growth by 2030E. An important paradigm and platform is the Integrated System: for Industry 4.0, DE and AI, taking into account the complex principle of I2K2+SH. If the transition of the Ukrainian economy to an innovative one does not take place in the next 3-5 years, Ukraine will remain in the backyard of civilization. (shrink)

The relevance of this study's topic is supported by the argument that a philosophical understanding of the fundamental concepts of epistemology as they pertain to the educational process is crucial as the educational setting becomes increasingly digitalised. This paper aims to explore the epistemological component of the philosophy of learning in light of the educational process digitalisation. The research comprised a sample of 462 university students from Kazakhstan, with 227 participants assigned to the experimental and 235 to the control groups. (...) The research employed a scientific methodology that encompassed various scientific methods, including sociological, socio-philosophical, and culturological analysis. In addition, philosophical methods such as system analysis, structural and functional analysis, and comparative and hermeneutical approaches were utilised for the philosophical and epistemological interpretation of the cognitive activity mechanism of students and its dynamics in light of digitalising education. Furthermore, psychological diagnostic methods such as SAMOAL personality self-actualisation diagnostics, personality volitional potential diagnostics, and psychodiagnostic methods were employed. This study offers a theoretical rationale for the integration of digital technology in the realm of education, grounded in philosophical and epistemological principles. In addition, it challenges the null hypothesis that the philosophical and epistemological framework of the educational process does not influence its cognitive and personal aspects. Digital learning has been found to improve both cognitive and personal aspects of the learning process. All indicators of self-realization increased statistically significantly (p 0.05). The positive impact of digital technologies on increasing the communicative competence of students can be considered as a factor in mitigating epistemic risks in the learning process.The findings can be used to avoid epistemological risks and improve learning effectiveness in a digital learning environment. There are plans to investigate the gender dimension of digital learning epistemology in the future. (shrink)

Problem solving has recently become a central topic both in the philosophy of science and in cognitive science. This paper integrates approaches to problem solving from these two disciplines and discusses the epistemological consequences of such an integration. The paper first analyzes problem solving as getting a true answer to a question. It then explores some stages of cognitive activity relevant to question answering that have been delineated by historians and philosophers of (...) science and by cognitive psychologists and artificial intelligencers. The traditional opposition between discovery and justification is challenged. It is suggested that epistemology may be conceptualized, in part, as the critical assessment of problem-solving strategies. (shrink)

Forensic science and its influence on policing and the criminal justice system have increased since the beginning of the twentieth century. While the philosophies of the forensic science pioneers remain the pillar of modern practice, rapid advances in technology and the underpinning sciences have seen an explosion in the number of disciplines and tools. Consequently, the way in which we exploit and interpret the remnant of criminal activity are adapting to this changing environment. In order to (...) best exploit the trace, an interdisciplinary approach to both research and investigation is required. In this paper, nine postdoctoral research fellows from a multidisciplinary team discuss their vision for the future of forensic science at the crime scene, in the laboratory and beyond. This paper does not pretend to be exhaustive of all fields of forensic science, but describes a portion of the postdoctoral fellows’ interests and skills. (shrink)

This paper briefly surveys several prominent modeling approaches to knowledge-based intelligent systems design and, especially, expert systems and the breakthroughs that have most broadened and improved their applications. We argue that the implementation of technology that aims to emulate rudimentary aspects of human intelligence has enhanced KBIS design, but that weaknesses remain that could be addressed with existing research in cognitive science. For example, we propose that systems based on representational plasticity, functional dynamism, domain specificity, creativity, and concept learning, (...) with their theoretical and experimental rigor, can best characterize the problem-solving capabilities of humans and can best overcome five key limitations currently exhibited by knowledge-based intelligent systems. We begin with a brief survey of the relevant work related to KBIS design and then discuss these five shortcomings with new suggestions for how to integrate results from cognitive science to resolve each of them. Our ultimate goal is to increase awareness and direct attention to areas of theoretical and experimental cognitive research that are fundamentally relevant to the goals underlying KBISes. (shrink)

Nowadays, new directions for the development of artificial intelligence (AI) have emerged, the task has been set to develop artificial general intelligence (AGI), which is able to go beyond the narrow AI, gain a high degree of autonomy, independently solve problems in different environmental conditions and thus have the ability to perform the functions of natural intelligence. In this regard, important philosophical, theoretical, and methodological questions arise concerning the definition and evaluation of the social significance of new AI achievements, especially (...) regarding the correlation of their socio-humanitarian and technological aspects. It is necessary to overcome a purely technocratic approach, which usually ignores the negative consequences of digitalization, the possible risks and threats of AI development. These difficulties are conditioned by the paradigm gap between the system of concepts used to describe technologies and the systems of concepts specific to socio-humanitarian descriptions and explanations. The so-called ontological paradox arises during digital transformation and the introduction of AI into social systems. An ontological paradox is also an epistemological paradox, since any statement of an ontological type presupposes its epistemological justification. To overcome this gap, V.E. Lepskiy proposes the conception of self-developing polysubjective environments – cybernetics of the third order. This conception allows to create a conceptual “bridge” between two systems of definitions that do not have direct logical connections between them. A productive tool for this can be the information approach, which is widely used to solve such interdisciplinary problems. This makes possible to theoretically correctly connect technological and socio-humanitarian descriptions in a single conceptual structure and to analyze main socio-humanitarian criteria for evaluating digital innovations. The article discusses in detail the main provisions of the information approach and its application for the development of AGI systems, the socio-humanitarian assessment of AI autonomous development, and the resolution of security issues. (shrink)

In a letter of 1932, Karl Popper described _Die beiden Grundprobleme der Erkenntnistheorie – The Two Fundamental Problems of the Theory of Knowledge_ – as ‘…a child of crises, above all of …the crisis of physics.’ Finally available in English, it is a major contribution to the philosophy of science, epistemology and twentieth century philosophy generally. The two fundamental problems of knowledge that lie at the centre of the book are the problem of induction, that although we are (...) able to observe only a limited number of particular events, science nevertheless advances unrestricted universal statements; and the problem of demarcation, which asks for a separating line between empirical science and non-science. Popper seeks to solve these two basic problems with his celebrated theory of falsifiability, arguing that the inferences made in science are not inductive but deductive; science does not start with observations and proceed to generalise them but with problems, which it attacks with bold conjectures. _The Two Fundamental Problems of the Theory of Knowledge_ is essential reading for anyone interested in Karl Popper, in the history and philosophy of science, and in the methods and theories of science itself. (shrink)

Cognitive science, as an interdisciplinary research endeavour, seeks to explain mental activities such as reasoning, remembering, language use, and problem solving, and the explanations it advances commonly involve descriptions of the mechanisms responsible for these activities. Cognitive mechanisms are distinguished from the mechanisms invoked in other domains of biology by involving the processing of information. Many of the philosophical issues discussed in the context of cognitive science involve the nature of information processing. For philosophy of (...) class='Hi'>science, a central question is what counts as a scientific explanation. But what is a mechanistic explanation and how does it work, how can philosophy of science use it as a solution for the problem of integration in cognitive science? By answering these questions and merging my answers with discussion of concepts of philosophy, normativity and generality, I will investigate the following claim. -/- I claim that philosophy by using strength concepts such as normativity, generality, and a mechanistic philosophy of explanations, can be a most important contributor to cognitive science. I also investigate how philosophy of science could be (can be) a bridge between psychology and neuroscience. We need a distinction between philosophy of cognitive science and philosophy in cognitive science; I am talking about the latter. -/- This claim is very important for the integration and the future of the interdisciplinary field known as cognitive science. -/- Philosophy as a true cognitive science -/- When the Cognitive Science Society was founded, in the late 1970s, philosophy, neuroscience, and anthropology were playing smaller roles. The three disciplines that formed the core group were artificial intelligence, psychology, and linguistics. The curious thing is that George Miller, a psychologist and an important founder of cognitive sciences in a hexagon diagram that he presented, put philosophy at the top of the diagram and neuroscience at the very bottom. There is enough agreement now that neuroscience is the most important contributor to cognitive science and there are fair connections between philosophy and neuroscience. In that diagram there was almost no connection between philosophy and neuroscience. -/- The developments and rise of cognitive science in the last half-century has been accompanied by considerable amount of philosophical activity. Perhaps no other area within analytic philosophy in the second half of that period has attracted more attention or produced more publications. (Bechtel and Graham, 1998. Rumelhart and Bly 1999. Bechtel, Mandik, Mundale 2001. Thagard, 2007. Bennett and Dennett et al, 2007. Bennett and Hacker, 2008. Andler, 2009. Frankish and Ramsey, 2012.) -/- Many philosophers of science offer conclusions that have a direct bearing on cognitive science and its practitioners can profit from closer engagement with the rest of cognitive science. For example, William Bechtel has discussed three projects, two in naturalistic philosophy of mind and one in naturalistic philosophy of science that have been pursued during the past 30 years, that he contends, can make theoretical and methodological contributions to cognitive science (Bechtel, 2009). Paul Thagard is another example of the mentioned emerging school of philosophers of science that define cognitive science as the interdisciplinary investigation of mind and intelligence (Thagard, 2006). Thagard by presenting some general but important philosophical questions such as, “What is the nature of the explanations and theories developed in cognitive science?”, and by providing answers to these central questions has showed how philosophy of science can help cognitive science by the advantage of its generality. Andrew Brook, however, believes that philosophical approaches have never had a settled place in cognitive science but he is listed in a group of the philosophers of science that they are contributing very closely the cognitive science (Brook, 2009). Daniel Dennett , as well as being a member the mentioned naturalistic philosophers of science, believes that there is much good work for philosophers to do in cognitive science if they adopt the constructive attitude that prevails in science. -/- What are mechanisms? Let us begin abstractly before considering an example. Mechanisms are collections of entities and activities organized together to do something (cf. Machamer, Darden, & Craver, 2000; Craver & Darden, 2001; Bechtel &Richardson, 1993; Glennan, 1996). These explanations are known as ‘mechanistic explanations’. By using and developing these mechanistic explanations of philosophy of science one can draw normative consequences for cognitive science. Paul Thagard (Thagard, 2006 and 2009), William Bechtel (Bechtel, 2008 and 2009), Andrew Brook (Brook, 2008) investigated and promoted using the ‘normativity’ in philosophy to show a better and crucial role for philosophy of science in an interdisciplinary known as cognitive science. Some philosophers have thought that, in order to pursue this normative function, philosophy must distance itself from empirical matters, but there are examples where the investigations of descriptive and normative issues go hand in hand. ( Thagard, 2009). -/- I will investigate how we can reduce a higher-level science such as psychology to neuroscience without the problems of reductionism but via mechanistic explanations. By problem I mean psychology does not lose its autonomy. -/- Conclusion -/- If cognitive science is all about understanding the human mind, or if cognitive science is the interdisciplinary investigation of mind and intelligence, since the whole life of philosophy was involving with the ways of knowing (epistemology) and conceptions of reality (metaphysics), also philosophy has considered the so-called mind-body problem ( identity theory, functionalism, and heuristic identity theory) , then philosophy could be the most deserved discipline to be a most contributor in cognitive science. I tried to discuss this by using the three advantages in philosophy, normativity, and generality and by introducing an emerging school of mechanistic (not mechanical) philosophers. One thing left, as cognitive science is a two-way street, philosophers need also to stop in a station of cognitive science and learn from the most important advances in brain and neuroscience. -/- . (shrink)

These essays are about education, learning, rational inquiry, philosophy, science studies, problem solving, academic inquiry, global problems, wisdom and, above all, the urgent need for an academic revolution. Despite this range and diversity of topics, there is a common underlying theme. Education ought to be devoted, much more than it is, to the exploration real-life, open problems; it ought not to be restricted to learning up solutions to already solved problems - especially if nothing is said about (...) the problems that provoked the solutions in the first place. There should be much more emphasis on learning how to engage in cooperatively rational exploration of problems: even five year olds could begin to learn how to do this. A central task of philosophy ought to be to keep alive awareness of our unsolved fundamental problems - especially our most fundamental problem of all, encompassing all others: How can our human world - and the world of sentient life more generally - imbued with the experiential, consciousness, free will, meaning and value, exist and best flourish embedded as it is in the physical universe? This is both our fundamental intellectual problem and our fundamental problem of living. As far as the latter is concerned, we are at present heading towards disaster - as our immense, unsolved global problems tell us: population growth, destruction of natural habitats and rapid extinction of species, vast inequalities of wealth and power around the world, pollution of earth, sea and air, our proclivity for war, and above all global warming. If we are to resolve our conflicts and global problems more intelligently, effectively and humanely than we have managed to do so far, then we have to learn how to do it. That, in turn, requires that our institutions of learning, our universities and schools, are rationally designed and devoted to the task. At present they are not. That is the crisis behind all the others. From the past we have inherited the idea that the basic intellectual aim of inquiry ought to be to acquire knowledge. First, knowledge is to be acquired; then, secondarily, it can be applied to help solve social problems. But this is dangerously and damagingly irrational, and it is this irrationality that is, in part, responsible for the genesis of our current global problems, and our current incapacity to solve them. As a matter of supreme urgency, we need to transform academia so that it becomes rationally devoted to helping humanity learn how to make progress towards as good and wise a world as possible. This would involve putting problems of living - including global problems - at the heart of academia, problems of knowledge and technological know-how emerging out of, and feeding back into, the central task to help people tackle problems of living in increasingly cooperatively rational ways. Almost every department and aspect of academia needs to change. We need a new kind of academic inquiry devoted, not just to knowledge, but rather to wisdom - wisdom being the capacity to realize what is of value in life for oneself and others, wisdom including knowledge and technological know-how, but much else besides. So, this is what these essays seek to provoke: a concerted effort to transform our institutions of learning so that they become rationally and effectively devoted to helping us learn how to create a wiser world. With these essays before me, I can see that there is one crucial element of learning about which they say nothing - or nothing explicit. The vital role of play in learning. All mammals - or at any rate almost all mammals - learn by means of play. Cats, tigers, foxes and other predators learn to hunt by means of endless mock fights when kittens and cubs. Deer, sheep and antelope learn to escape by means of playful leaps and bounds when young. We are mammals too. Almost certainly, we learnt how to be adult human beings by means of play during the millions of years we evolved into homo sapiens living in hunting and gathering tribes. Children today, out of school, learn by means of play. Learning by means of play is almost certainly fundamental to our makeup. Education needs to exploit it. Schools and universities need to become places of play. Successful problem solving is often likely to be playful in character. The youthful Einstein called doing physics "getting up to mischief". But our most serious problems of living are so grim, so imbued with suffering, wasted lives and unnecessary death that the idea of approaching them in a playful spirit seems sacrilegious. We need to keep alive tackling of intellectual problems so that playful capacities can be exercised - if for no other reason (and other reasons there are, of course, aplenty). There are two really worthy impulses behind all rational inquiry: delight and compassion. (shrink)

Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, yet it (...) is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research – protein structure prediction and single cell RNA-sequencing – we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems. (shrink)

Argumentation theory is a distinctly multidisciplinary field of inquiry. It draws its data, assumptions, and methods from disciplines as disparate as formal logic and discourse analysis, linguistics and forensic science, philosophy and psychology, political science and education, sociology and law, and rhetoric and artificial intelligence. This presents the growing group of interested scholars and students with a problem of access, since it is even for those active in the field not common to have acquired a familiarity (...) with relevant aspects of each discipline that enters into this multidisciplinary matrix. This book offers its readers a unique comprehensive survey of the various theoretical contributions which have been made to the study of argumentation. It discusses the historical works that provide the background to the field and all major approaches and trends in contemporary research. Argument has been the subject of systematic inquiry for twenty-five hundred years. It has been graced with theories, such as formal logic or the legal theory of evidence, that have acquired a more or less settled provenance with regard to specific issues. But there has been nothing to date that qualifies as a unified general theory of argumentation, in all its richness and complexity. This being so, the argumentation theorist must have access to materials and methods that lie beyond his or her "home" subject. It is precisely on this account that this volume is offered to all the constituent research communities and their students. Apart from the historical sections, each chapter provides an economical introduction to the problems and methods that characterize a given part of the contemporary research program. Because the chapters are self-contained, they can be consulted in the order of a reader's interests or research requirements. But there is value in reading the work in its entirety. Jointly authored by the very people whose research has done much to define the current state of argumentation theory and to point the way toward more general and unified future treatments, this book is an impressively authoritative contribution to the field. (shrink)

This article challenges stereotypical conceptions of Law and Science as cultural opposites, arguing that English criminal trial practice is fundamentally congruent with modern science’s basic epistemological assumptions, values and methods of inquiry. Although practical tensions undeniably exist, they are explicable—and may be neutralised—by paying closer attention to criminal adjudication’s normative ideals and their institutional expression in familiar aspects of common law trial procedure, including evidentiary rules of admissibility, trial by jury, adversarial fact-finding, cross-examination and the ethical duties of (...) expert witnesses. Effective partnerships between lawyers and forensic scientists are indispensable for integrating scientific evidence into criminal proceedings, and must be renegotiated between individual practitioners on an on-going basis. Fruitful interdisciplinary collaboration between scholars with a shared interest in forensic science should dispense with reductive cultural stereotypes of Science and Law. (shrink)

On a cultural level, and for Christian theology as part of a long tradition in the evolution of religion, evolutionary epistemology “sets the stage,” as it were, for understanding the deep evolutionary impact of our ancestral history on the evolution of culture, and eventually on the evolution of disciplinary and interdisciplinary reflection. In the process of the evolution of human knowledge, our interpreted experiences and expectations of the world have a central role to play. What evolutionary epistemology also shows us (...) is that we humans can indeed take on cognitive goals and ideals that cannot be explained or justified in terms of survival-promotion or reproductive advantage only. Therefore, once the capacities for rational knowledge, moral sensibility, aesthetic appreciation of beauty, and the propensity for religious belief have emerged in our biological history, they cannot be explained only in biological/evolutionary terms. Finally, in this way a door is opened for seeing problem solving as a central activity of our research traditions. As philosophers of science have argued, one of the most important shared rational resources between even widely divergent disciplines is problem solving as the most central and defining activity of all research traditions. As will become clear, the very diverse reasoning strategies of theology and the sciences clearly overlap in their shared quests for intelligible problem solving, including problem solving on an empirical, experiential, and conceptual level. (shrink)

On a cultural level, and for Christian theology as part of a long tradition in the evolution of religion, evolutionary epistemology “sets the stage,” as it were, for understanding the deep evolutionary impact of our ancestral history on the evolution of culture, and eventually on the evolution of disciplinary and interdisciplinary reflection. In the process of the evolution of human knowledge, our interpreted experiences and expectations of the world (and of the ultimate questions we humans typically pose to the world) (...) have a central role to play. What evolutionary epistemology also shows us is that we humans can indeed take on cognitive goals and ideals that cannot be explained or justified in terms of survival‐promotion or reproductive advantage only. Therefore, once the capacities for rational knowledge, moral sensibility, aesthetic appreciation of beauty, and the propensity for religious belief have emerged in our biological history, they cannot be explained only in biological/evolutionary terms. Finally, in this way a door is opened for seeing problem solving as a central activity of our research traditions. As philosophers of science have argued, one of the most important shared rational resources between even widely divergent disciplines is problem solving as the most central and defining activity of all research traditions. As will become clear, the very diverse reasoning strategies of theology and the sciences clearly overlap in their shared quests for intelligible problem solving, including problem solving on an empirical, experiential, and conceptual level. (shrink)

Every time we think ahead, we are crafting a story. Every daily plan—and every political vision, social movement, scientific hypothesis, business proposal, and technological breakthrough—starts with “what if?” Linking causes to effects, considering hypotheticals and counterfactuals, asking how other people will react: these are the essence of narrative. So why do we keep overlooking story’s importance to intelligence in favor of logic? This book explains how and why our brains think in stories. Angus Fletcher, an expert in neuroscientific approaches to (...) narrative, identifies this capacity as “storythinking.” He demonstrates that storythinking is fundamental to what makes us human. Artificial intelligence can perform symbolic logic, rational deduction, and mathematical calculation, but it is incapable of deliberating in narrative. Drawing on new research in neuroscience and narrative theory, Fletcher explores the nature of imagination, innovation, and creativity. He provides concise answers to big questions: How does storythinking work? Why did it evolve? How can it misfire? What problems can it solve? Revealing the significance of storythinking from science to business to philosophy, this book also provides ways for readers to harness its power to script better tomorrows. (shrink)

A problem is a situation in which an agent seeks to attain a given goal without knowing how to achieve it. Human problem solving is typically studied as a search in a problem space composed of states (information about the environment) and operators (to move between states). A problem such as playing a game of chess has possible states, and a traveling salesperson problem with as little as 82 cities already has more than different (...) tours (similar to chess). Biological neurons are slower than the digital switches in computers. An exhaustive search of the problem space exceeds the capacity of current computers for most interesting problems, and it is fairly clear that humans cannot in their lifetime exhaustively search even small fractions of these problem spaces. Yet, humans play chess and solve logistical problems of similar complexity on a daily basis. Even for simple problems humans do not typically engage in exploring even a small fraction of the problem space. This begs the question: How do humans solve problems on a daily basis in a fast and efficient way? Recent work suggests that humans build a problem representation and solve the represented problem—not the problem that is out there. The problem representation that is built and the process used to solve it are constrained by limits of cognitive capacity and a cost–benefit analysis discounting effort and reward. In this article, we argue that better understanding the way humans represent and solve problems using heuristics can help inform how simpler algorithms and representations can be used in artificial intelligence to lower computational complexity, reduce computation time, and facilitate real-time computation in complex problem solving. (shrink)

Sign-theoretical concepts have been used in research into the nature of living systems, not only by biologists, semioticians, and philosophers, but also by scientists who analyze organisms from the perspective of Decision Theory. Decision Theory (DT) describes both the external behavior and the internal information-processing of any kind of agent in terms of problem solving. Such “problem solving” is considered a complex process of: (1) defining a goal in an environment, (2) selecting the means to reach (...) the defined goal, and (3) controlling the effects of said selected means on the environment. The hypothesis that problem-solving agents are, first, physical entities and, second, sign-using systems is one of the most influential ideas in Decision Theory. This idea has been developed under the name of the ‘Physical Symbol System Hypothesis’ (PSSH) since the 1950s, particularly by two American scientists, Allen Newell and Herbert A. Simon, who did interdisciplinary research in cognitive science, artificial intelligence, economics, and decision theory. This paper first gives a short overview of the basic semiotic theory that underlies Newell’s and Simon’s work, and then offers some ideas on a specifically biosemiotic use of the Physical SymbolSystem Hypothesis. (shrink)

This book discusses how scientific and other types of cognition make use of models, abduction, and explanatory reasoning in order to produce important and innovative changes in theories and concepts. Gathering revised contributions presented at the international conference on Model-Based Reasoning, held on October 24–26 2018 in Seville, Spain, the book is divided into three main parts. The first focuses on models, reasoning, and representation. It highlights key theoretical concepts from an applied perspective, and addresses issues concerning information visualization, experimental (...) methods, and design. The second part goes a step further, examining abduction, problem solving, and reasoning. The respective papers assess different types of reasoning, and discuss various concepts of inference and creativity and their relationship with experimental data. In turn, the third part reports on a number of epistemological and technological issues. By analyzing possible contradictions in modern research and describing representative case studies, this part is intended to foster new discussions and stimulate new ideas. All in all, the book provides researchers and graduate students in the fields of applied philosophy, epistemology, cognitive science, and artificial intelligence alike with an authoritative snapshot of the latest theories and applications of model-based reasoning. (shrink)

Originally published as Scientific Research, this pair of volumes constitutes a fundamental treatise on the strategy of science. Mario Bunge, one of the major figures of the century in the development of a scientific epistemology, describes and analyzes scientific philosophy, as well as discloses its philosophical presuppositions. This work may be used as a map to identify the various stages in the road to scientific knowledge. Philosophy of Science is divided into two volumes, each with two parts. Part (...) 1 offers a preview of the scheme of science and the logical and semantical took that will be used throughout the work. The account of scientific research begins with part 2, where Bunge discusses formulating the problem to be solved, hypothesis, scientific law, and theory. The second volume opens with part 3, which deals with the application of theories to explanation, prediction, and action. This section is graced by an outstanding discussion of the philosophy of technology. Part 4 begins with measurement and experiment. It then examines risks in jumping to conclusions from data to hypotheses as well as the converse procedure. Bunge begins this mammoth work with a section entitled "How to Use This Book." He writes that it is intended for both independent reading and reference as well as for use in courses on scientific method and the philosophy of science. It suits a variety of purposes from introductory to advanced levels. Philosophy of Science is a versatile, informative, and useful text that will benefit professors, researchers, and students in a variety of disciplines, ranging from the behavioral and biological sciences to the physical sciences. (shrink)

The basic task of the essay is to exhibit science as a rational enterprise. I argue that in order to do this we need to change quite fundamentally our whole conception of science. Today it is rather generally taken for granted that a precondition for science to be rational is that in science we do not make substantial assumptions about the world, or about the phenomena we are investigating, which are held permanently immune from empirical appraisal. (...) According to this standard view, science is rational precisely because science does not make a priori metaphysical presuppositions about the world forever preserved from possible empirical refutation. It is of course accepted that an individual scientist, developing a new theory, may well be influenced by his own metaphysical presuppositions. In addition, it is acknowledged that a successful scientific theory, within the context of a particular research program, may be protected for a while from refutation, thus acquiring a kind of temporary metaphysical status, as long as the program continues to be empirically progressive. All such views unite, however, in maintaining that science cannot make permanent metaphysical presuppositions, held permanently immune from objective empirical evaluation. According to this standard view, the rationality of science arises, not from the way in which new theories are discovered, but rather from the way in which already formulated theories are appraised in the light of empirical considerations. And the fundamental problem of the rationality of science--the Humean problem of induction--concerns precisely the crucial issue of the rationality of accepting theories in the light of evidence. In this essay I argue that this widely accepted standard conception of science must be completely rejected if we are to see science as a rational enterprise. In order to assess the rationality of accepting a theory in the light of evidence it is essential to consider the ultimate aims of science. This is because adopting different aims for science will lead us, quite rationally, to accept different theories in the light of evidence. I argue that a basic aim of science is to explain. At the outset science simply presupposes, in a completely a priori fashion, that explanations can be found, that the world is ultimately intelligible or simple. In other words, science simply presupposes in an a priori way the metaphysical thesis that the world is intelligible, and then seeks to convert this presupposed metaphysical theory into a testable scientific theory. Scientific theories are only accepted insofar as they promise to help us realize this fundamental aim. At once a crucial problem arises. If scientific theories are only accepted insofar as they promise to lead us towards articulating a presupposed metaphysical theory, it is clearly essential that we can choose rationally, in an a priori way, between all the very different possible metaphysical theories that can be thought up, all the very different ways in which the universe might ultimately be intelligible. For holding different aims, accepting different metaphysical theories conceived of as blueprints for future scientific theories will, quite rationally, lead us to accept different scientific theories. Thus it is only if we can choose rationally between conflicting metaphysical blueprints for future scientific theories that we will be in a position to appraise rationally the acceptability of our present day scientific theories. We thus face the crucial problem: How can we choose rationally between conflicting possible aims for science, conflicting metaphysical blueprints for future scientific theories? It is only if we can solve this fundamental problem concerning the aims of science that we can be in a position to appraise rationally the acceptability of existing scientific theories. There is a further point here. If we could choose rationally between rival aims, rival metaphysical blueprints for future scientific theories, then we would in effect have a rational method for the discovery of new scientific theories! Thus we reach the result: there is only a rational method for the appraisal of existing scientific theories if there is a rational method of discovery. I shall argue that the aim-oriented theory of scientific inquiry to be advocated here succeeds in exhibiting science as a rational enterprise in that it succeeds in providing a rational procedure for choosing between rival metaphysical blueprints: it thus provides a rational, if fallible, method of discovery, and a rational method for the appraisal of existing scientific theories--thus resolving the Humean problem. In Part I of the essay I argue that the orthodox conception of science fails to exhibit science as a rational enterprise because it fails to solve the Humean problem of induction. The presuppositional view advocated here does however succeed in resolving the Humean problem. In Part II of the essay I spell out the new aim-oriented theory of scientific method that becomes inevitable once we accept the basic presuppositional viewpoint. I argue that this new aim oriented conception of scientific method is essentially a rational method of scientific discovery, and that the theory has important implications for scientific practice. (shrink)

This paper introduces an epistemological model of scientific reasoning which can be described in terms of abduction, deduction and induction. The aim is to emphasize the significance of abduction in order to illustrate the problem-solving process and to propose a unified epistemological model of scientific discovery. The model first describes the different meanings of the word abduction in order to clarify their significance for epistemology and artificial intelligence. In different theoretical changes in theoretical systems we witness different kinds (...) of discovery processes operating. Discovery methods are "data-driven," "explanation-driven", and "coherence-driven". Sometimes there is a mixture of such methods: for example, an hypothesis devoted to overcome a contradiction is found by abduction. Contradiction, far from damaging a system, help to indicate regions in which it can be changed and improved. I will also consider a kind of "weak" hypothesis that is hard to negate and the ways for making it easy. In these cases the subject can "rationally" decide to withdraw his or her hypotheses even in contexts where it is "impossible" to find "explicit" contradictions and anomalies. Here, the use of negation as failure is illuminating. (shrink)

This essay examines arguments for and against the proposition that Artificial Intelligence (AI) research makes an important contribution to the understanding of the human mind. A number of recent articles have seemed to question the value of Al ideas in specific domains (e.g., language. mental imagery, problem solving). In the present paper, it is argued that the real disagreement concerns the form of a scientific psychology. The critics of Artificial Intelligence believe that many acceptable psychological theories exist and (...) the important task of the scientist is to show that certain of these theories are true. They criticize AI research because it has not produced theories whose adequacy can be tested by empirical research. The supporters of AI research believe that no adequate psychological theories exist. They claim that the important task is to develop adequate theories and until such theories have been constructed, it is pointless to worry about empirical tests.A number of major arguments attacking and defending AI research are examined in order to illustrate that the real issue is methodological. It is concluded that the present debate is counterproductive since both sides assume that the methodological premise of the other side is wrong without attempting to refute it. A more constructive debate would address directly the more fundamental question of what methods offer the greatest promise of solving the major problems of psychology. (shrink)

Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a fine-grained analysis of several of the highest-profile sets of ethical principles for AI. We assess whether (...) these principles converge upon a set of agreed-upon principles, or diverge, with significant disagreement over what constitutes ‘ethical AI.’ Our analysis finds a high degree of overlap among the sets of principles we analyze. We then identify an overarching framework consisting of five core principles for ethical AI. Four of them are core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice. On the basis of our comparative analysis, we argue that a new principle is needed in addition: explicability, understood as incorporating both the epistemological sense of intelligibility (as an answer to the question ‘how does it work?’) and in the ethical sense of accountability (as an answer to the question: ‘who is responsible for the way it works?’). In the ensuing discussion, we note the limitations and assess the implications of this ethical framework for future efforts to create laws, rules, technical standards, and best practices for ethical AI in a wide range of contexts. (shrink)

Science is a social expression of intelligence. As such, science can be explained as a product of our natural history. This naturalistic account of science leads to a social conception of scientific rationality, according to which rationality is a structural property of science as a whole, not to be ascribed to the behavior of individual scientists. This new conception of rationality embedded in a straightforward biological epistemology solves the problem of the rationality of science.

The article addresses the problem of identifying methods to develop the ability of artificial intelligence (AI) systems to provide explanations for their findings. This issue is not new, but, nowadays, the increasing complexity of AI systems is forcing scientists to intensify research in this direction. Modern neural networks contain hundreds of layers of neurons. The number of parameters of these networks reaches trillions, genetic algorithms generate thousands of generations of solutions, and the semantics of AI models become more complicated, (...) going to the quantum and non-local levels. The world’s leading companies are investing heavily in creating explainable AI (XAI). However, the result is still unsatisfactory: a person often cannot understand the “explanations” of AI because the latter makes decisions differently than a person, and perhaps because a good explanation is impossible within the framework of the classical AI paradigm. AI faced a similar problem 40 years ago when expert systems contained only a few hundred logical production rules. The problem was then solved by complicating the logic and building added knowledge bases to explain the conclusions given by AI. At present, other approaches are needed, primarily those that consider the external environment and the subjectivity of AI systems. This work focuses on solving this problem by immersing AI models in the social and economic environment, building ontologies of this environment, taking into account a user profile and creating conditions for purposeful convergence of AI solutions and conclusions to user-friendly goals. (shrink)

We all believe an unbounded number of things about the way the world is and about the way the world works. For example, I believe that if I move this book into the other room, it will not change color -- unless there is a paint shower on the way, unless I carry an umbrella through that shower, and so on; I believe that large red trucks at high speeds can hurt me, that trucks with polka dots can hurt me, (...) and so on; that if I move this book, the room will stay in place -- unless there is a pressure switch under the book attached to a bomb, unless the switch communicates to the bomb by radio and there is shielding in the way, and so on; that the moon is not made of green cheese, that the moon is not made of caviar, that the moon is not made of gold, and so on. The problems involved in accounting for such infinite proliferations of beliefs -- and the computations and inferences that take them into account -- are collectively called the Frame Problems, and are considered by some to constitute a major discovery of a new philosophical problem. How could we possibly learn them all? How could the brain possibly hold them all? The problems appear insoluble, impossible. Yet we all learn and hold such unbounded numbers of beliefs; in particular, children do. Something must be wrong. I wish to argue that the frame problem arises from a fundamental presupposition about the nature of representation -- a false presupposition. Yet, it is a presupposition that dominates contemporary developmental psychology (and psychology more broadly, and cognitive science, artificial intelligence, philosophy of mind, and so on). In particular, I will offer an alternative model of the nature of representation within which the frame problem does not arise -- within which such unboundedness is natural. (shrink)

"Psychiatry and Philosophy of Science" explores conceptual issues in psychiatry from the perspective of analytic philosophy of science. Through an examination of those features of psychiatry that distinguish it from other sciences - for example, its contested subject matter, its particular modes of explanation, its multiple different theoretical frameworks, and its research links with big business - Rachel Cooper explores some of the many conceptual, metaphysical and epistemological issues that arise in psychiatry. She shows how these pose interesting (...) challenges for the philosopher of science while also showing how ideas from the philosophy of science can help to solve conceptual problems within psychiatry. Cooper's discussion ranges over such topics as the nature of mental illnesses, the treatment decisions and diagnostic categories of psychiatry, the case-history as a form of explanation, how psychiatry might be value-laden, the claim that psychiatry is a multi-paradigm science, the distortion of psychiatric research by pharmaceutical industries, as well as engaging with the fundamental question whether the mind is reducible to something at the physical level. "Psychiatry and Philosophy of Science" demonstrates that cross-disciplinary contact between philosophy of science and psychiatry can be immensely productive for both subjects and it will be required reading for mental health professionals and philosophers alike. (shrink)

For all of recorded history prior to the second half of the twentieth century, there has been but one realm in which the cognitive processes of reasoning and problem solving, learning and discovery, language and mathematics took place. The realm of human intellect no longer has an exclusive claim on these cognitive processes--artificial intelligence represents a parallel claim. Wagman compares the two realms, focusing on each of the major components of cognition: logic, reasoning, problem-solving, language, memory, (...) learning, and discovery. He identifies consonant and disparate modes of cognition, and develops a general theory of human and artificial intelligence. (shrink)

The fundamental problem of what is explained in science should be considered and clarified since it determines the way of solving the problem of how something is explained as well as the entire view of explanation. In the first section after the introduction, Hempel's models of explanation are criticized for their narrow concern with logical reconstruction. In the next section a broader epistemological approach to explanation is presented, and in the last section an historical example of (...) Newtonian explanation as epistemic activity is discussed. (shrink)

In this essay we critically evaluate the progress that has been made in solving the problem of meaning in artificial intelligence (AI) and robotics. We remain skeptical about solutions based on deep neural networks and cognitive robotics, which in our opinion do not fundamentally address the problem. We agree with the enactive approach to cognitive science that things appear as intrinsically meaningful for living beings because of their precarious existence as adaptive autopoietic individuals. But this approach (...) inherits the problem of failing to account for how meaning as such could make a difference for an agent’s behavior. In a nutshell, if life and mind are identified with physically deterministic phenomena, then there is no conceptual room for meaning to play a role in its own right. We argue that this impotence of meaning can be addressed by revising the concept of nature such that the macroscopic scale of the living can be characterized by physical indeterminacy. We consider the implications of this revision of the mind-body relationship for synthetic approaches. (shrink)

This paper focuses on the normative analysis—in the sense of the classic decision-theoretic formulation—of decision problems that arise in connection with forensic expert reporting. We distinguish this analytical account from other common types of decision analyses, such as descriptive approaches. While decision theory is, since several decades, an extensively discussed topic in legal literature, its use in forensic science is more recent, and with an emphasis on goals such as the analysis of the logical structure of (...) class='Hi'>forensic expert conclusions regarding, for example, propositions of common source of evidential and known materials. Typical examples are so-called identification decisions, especially categorical conclusions according to which fingermarks come from a particular a person of interest. We will present and compare ways of stating forensic identification decisions in decision-theoretic terms and explain their underlying rationale. In particular, we will emphasize the importance of viewing this analysis as normative in the sense of providing a reflective rather than a prescriptive reference point against which people in charge of forensic identification decisions may compare their otherwise intuitive and informal reasoning, before acting. Normative decision analysis in forensic science thus provides a vector through which current practice can be articulated, scrutinized and rethought. (shrink)

In this wide-ranging collection of essays on origins, mathematician Granville Sewell looks at the big bang, the fine-tuning of the laws of physics, and the evolution of life. He concludes that while there is much in the history of life that seems to suggest natural causes, there is nothing to support Charles Darwin’s idea that natural selection of random mutations can explain major evolutionary advances. Sewell explains why evolution is a fundamentally different and much more difficult problem than others (...) solved by science, and why increasing numbers of scientists are now recognizing what has long been obvious to the layman, that there is no explanation possible without design. This book summarizes many of the traditional arguments for intelligent design, but presents some powerful new arguments as well. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Evil in Modern Thought: An Alternative History of PhilosophyPaul S. MiklowitzSusan Neiman. Evil in Modern Thought: An Alternative History of Philosophy. Princeton: Princeton University Press, 2002. Pp. xii + 358. Cloth, $29.95.Contemporary philosophy in America tends to regard epistemological questions as the most fundamental of the discipline, but Susan Neiman's Evil in Modern Thought sets itself against this assumption in an attempt to sketch "an alternative history of (...) philosophy." "It's possible to begin to worry about the difference between appearance and reality because... a dream is so vivid that you want to grasp one of its objects for a moment or two of sleepy half-consciousness," Neiman writes, but then "you wake up in your bed, slap your face if you have to... " (5). The problem of evil, in stark contrast to such skeptical speculations, is far more urgent and trenchant; it is "the place where philosophy begins, and threatens to stop" (ibid.). Moreover, the problem of evil, far from being merely a theological quandary, actually forms a link between metaphysics and epistemology by starkly posing questions about the intelligibility of the world: Why are things like that? wants the answer Because it's best that way. For the early modern period, these questions centered on the "theodicy" debates focused by the catastrophic Lisbon earthquake of 1755, while for us the holocaust threatens to explode all our hopes and expectations for the possibility of a world that is as it should be. Thus, Lisbon and Auschwitz frame for Neiman the historical topoi that ground discussion of the relation between natural and moral evils, the evolving philosophical understanding of which defines "who we have become in the three centuries that separate us from the early Enlightenment" (10).The problem of theodicy is introduced through the figure of Alfonso X, king of Castille in 1252, and "the first Enlightenment hero" (14), according to Neiman. Having studied astronomy under the guidance of learned Jews from Toledo, Alfonso scandalously remarked "If I had been of God's counsel at the Creation, many things would have been ordered better" (15). Serving as a paradigm of blasphemy for half a millennium, Alfonso's boast also expresses the inevitable dismay that must accompany the delighted wonder at natural order exploited by the argument from design: while Creation is surely miraculous in its manifestation of seemingly intelligent structure, it is just as surely astonishing that so much of that structure seems so wantonly destructive. Can this really be the best of all possible worlds if innocent lives are cut short by cruel diseases or natural disasters while vicious villains prosper into comfortable old age?! Alfonso's remark serves Neiman as a leitmotif around which she orders central figures in the canon of western philosophy into two camps: those who struggled to reconcile natural evil with God's goodness or with reason (Leibniz, Pope, Rousseau, Kant, Hegel and Marx), and those who skeptically condemned reason by appealing to "brute appearances" (Bayle, Voltaire, Hume, Sade and Schopenhauer). Because the very categories of natural and moral evil tend to break down as a result of the development of psychology as a "science of nature" (to the extent that human motives are regarded as natural processes subject to empirical explanation, they cease to manifest a unique ontology), Nietzsche and Freud fall outside the philosophical canon thus construed—although Neiman's discussion of these two thinkers is perhaps the most compelling of the book.No new interpretive ground is broken in the resulting exegesis of western philosophical history, but Neiman's treatment of each of these figures is intelligent and skillful. In discussing Kant, for example, a deep paradox at the center of his moral thought is brilliantly harnessed to the book's argument. Whereas theodicies from Leibniz onward had focused on attempts to show that Providence ultimately guaranteed a connection between virtue and happiness despite obvious evidence to the contrary, Kant took exactly the opposite tack. As Neiman puts it, "Solving the problem of evil is not only impossible but immoral. For knowing the connections between moral and natural evils would undermine the possibility of morality" (69). If we could be sure that good deeds... (shrink)

This paper uses a case study of a 1970s controversy in artificial-intelligence (AI) research to explore how scientists understand the relationships between research and practical applications. It is part of a project that seeks to map such relationships in order to enable better policy recommendations to be grounded empirically through historical evidence. In 1972 the mathematician James Lighthill submitted a report, published in 1973, on the state of artificial-intelligence research under way in the United Kingdom. The criticisms made in the (...) report have been held to be a major cause behind the dramatic slowing down (subsequently called an ‘AI winter’) of such research. This paper has two aims, one narrow and one broad. The narrow aim is to inquire into the causes, motivations and content of the Lighthill report. I argue that behind James Lighthill's criticisms of a central part of artificial intelligence was a principle he held throughout his career – that the best research was tightly coupled to practical problem solving. I also show that the Science Research Council provided a preliminary steer to the direction of this apparently independent report. The broader aim of the paper is to map some of the ways that scientists (and in Lighthill's case, a mathematician) have articulated and justified relationships between research and practical, real-world problems, an issue previously identified as central to historical analysis of modern science. The paper therefore offers some deepened historical case studies of the processes identified in Agar's ‘working-worlds’ model. (shrink)

According to the standard (recent) history of connectionism (see for example the accounts offered by Hecht-Nielsen (1990: pp. 14-19) and Dreyfus and Dreyfus (1988), or Papert's (1988: pp. 3-4) somewhat whimsical description), in the early days of Classical Computational Theory of Mind (CCTM) based AI research, there was also another allegedly distinct approach, one based upon network models. The work on network models seems to fall broadly within the scope of the term 'connectionist' (see Aizawa 1992), although the term had (...) yet to be coined at the time. These two approaches were "two daughter sciences" according to Papert (1988: p. 3). The fundamental difference between these two 'daughters', lay (according to Dreyfus and Dreyfus (1988: p. 16)) in what they took to be the paradigm of intelligence. Whereas the early connectionists took learning to be fundamental, the traditional school concentrated upon problem solving. (shrink)

The frame problem was originally a problem for Artificial Intelligence, but philosophers have interpreted it as an epistemological problem for human cognition. As a result of this reinterpretation, however, specifying the frame problem has become a difficult task. To get a better idea of what the frame problem is, how it gives rise to more general problems of relevance, and how deep these problems run, I expound six guises of the frame problem. I then (...) assess some proposed heuristic solutions to the frame problem; I show that these proposals misunderstand, and fail to address, an important aspect of the frame problem. Finally, I argue that though human cognition does not solve the frame problem in its epistemological guise, human cognition avoids some of the epistemological worries. (shrink)

This book explores new findings on the long-neglected topic of theory construction and discovery, and challenges the orthodox, current division of scientific development into discrete stages: the stage of generation of new hypotheses; the stage of collection of relevant data; the stage of justification of possible theories; and the final stage of selection from among equally confirmed theories. The chapters, written by leading researchers, offer an interdisciplinary perspective on various aspects of the processes by which theories rationally should, and descriptively (...) are, built. They address issues such as the role of problem-solving and heuristic reasoning in theory-building; how inferences and models shape the pursuit of scientific knowledge; the relation between problem-solving and scientific discovery; the relative values of the syntactic, semantic, and pragmatic view of theories in understanding theory construction; and the relation between ampliative inferences, heuristic reasoning, and models as a means for building new theories and knowledge. Through detailed arguments and examinations, the volume collectively challenges the orthodox view’s main tenets by characterizing the ways in which the different “stages” are logically, temporally, and psychologically intertwined. As a group, the chapters provide several attempts to answer long-standing questions about the possibility of a unified conceptual framework for building theories and formulating hypotheses. (shrink)

The paper aims to analyze three discussions pertaining to the artificial intelligence project that took place on both sides of the “Iron Curtain” since the 1960s: 1) E.V. Ilyenkov – D.I. Dubrovsky (USSR), 2) H. Dreyfus – computer scientists (USA), 3) H. Dreyfus – H. Collins (USA – UK). The authors observe the originality of the arguments of Soviet philosophers in comparison with the discussions in the US and UK. The basis for a comparative analysis of these discussions is the (...) typology of social intercourse paradigms, which assumes the existence of three fundamentally different views of humans as social beings: the information-instrumental, the existential-phenomenological, and the social-institutional paradigms. These views correlate with the ideas about humans within different theoretical structures examining artificial intelligence. The information-instrumental paradigm poses the problem of social intercourse regarding communication, material, and informational exchange (D.I. Dubrovsky, computer scientists). The social-institutional paradigm considers social intercourse as personification, actualization, and implementation of the structural characteristics of social relations (E.V. Ilyenkov). The existential-phenomenological paradigm characterizes social intercourse as the realization of the internal potency of a self-conscious individual (H. Dreyfus). The paper exposes that the theoretical and methodological foundations of the discussions under consideration are similar to the paradigmatic foundations of the analysis of social intercourse in social philosophy and social sciences. There are “two and a half floors” in the research of problems associated with the entry of artificial intelligence technologies into the everyday life of society: 1) philosophy, computer and cognitive sciences; 2) social sciences; 3) interdisciplinary research. A particular paradigmatic structure and a unique research problem characterize each of them. In conclusion, “new old questions” are highlighted that structure the field of contemporary social analytics for studying artificial intelligence and artificial sociality. (shrink)

Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial Intelligence aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory successes. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of “opacity” from (...) philosophy of science, this framework is modeled after accounts of explanation in cognitive science. The framework distinguishes between the explanation-seeking questions that are likely to be asked by different stakeholders, and specifies the general ways in which these questions should be answered so as to allow these stakeholders to perform their roles in the Machine Learning ecosystem. By applying the normative framework to recently developed techniques such as input heatmapping, feature-detector visualization, and diagnostic classification, it is possible to determine whether and to what extent techniques from Explainable Artificial Intelligence can be used to render opaque computing systems transparent and, thus, whether they can be used to solve the Black Box Problem. (shrink)

The article presents grounds for defining the fetish of artificial intelligence (AI). We highlight the fundamental differences of AI from all earlier technological advances, as they are primarily related to its introduction into the human cognitive sphere and generating fundamentally new uncontrollable consequences for society. We provide solid evidence that the leaders of the globalist project are the main beneficiaries of the AI fetish. This is clearly manifested in the works of philosophers who are close to major technology corporations and (...) their mega-projects. We suggest considering the problem of how to use the capabilities of AI to overcome the growing international conflicts and the global crisis. The focus is on the problem of agency, which solution from the standpoint of an anthropomorphic approach to AI is fraught with serious negative consequences. Endowing AI with agency, responsibility is implicitly removed from the person who uses the technology, and the established legislative practice is also destroyed. We present AI as an agent endowed with a set of invariant generalized qualities that is similar to natural subjects. These qualities include: the ability to deliberation, reflexivity, communication and elements of sociability. Such a representation of AI as an agent (pseudo-subject) is consistent with the principle of distributed control in biology and psychology, which was called the principle of a dual subject. In combination with the systems of principles and ontologies specified in the concept of post-nonclassical cybernetics of self-developing environments, this will allow the use of AI as a means of social innovation, while maintaining control over AI technologies. This will also help to pose and solve the problem of integrating formations of artificial and natural intelligence while maintaining the basic qualities of carriers of natural intelligence. (shrink)

Diversity is often announced as a solution to ethical problems in artificial intelligence (AI), but what exactly is meant by diversity and how it can solve those problems is seldom spelled out. This lack of clarity is one hurdle to motivating diversity in AI. Another hurdle is that while the most common perceptions about what diversity is are too weak to do the work set out for them, stronger notions of diversity are often defended on normative grounds that fail to (...) connect to the values that are important to decision-makers in AI. However, there is a long history of research in feminist philosophy of science and a recent body of work in social epistemology that taken together provide the foundation for a notion of diversity that is both strong enough to do the work demanded of it, and can be defended on epistemic grounds that connect with the values that are important to decision-makers in AI. We clarify and defend that notion here by introducing emergent expertise as a network phenomenon wherein groups of workers with expertise of different types can gain knowledge not available to any individual alone, as long as they have ways of communicating across types of expertise. We illustrate the connected epistemic and ethical benefits of designing technology with diverse groups of workers using the examples of an infamous racist soap dispenser, and the millimeter wave scanners used in US airport security. (shrink)

Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven's Progressive Matrices, a widely used tool in the field of intelligence testing. The model is (...) able to generate the rules necessary to correctly solve Raven's items, as well as recreate many of the experimental effects observed in human subjects. (shrink)

According to the diversity-beats-ability theorem, groups of diverse problem solvers can outperform groups of high-ability problem solvers. We argue that the model introduced by Lu Hong and Scott Page is inadequate for exploring the trade-off between diversity and ability. This is because the model employs an impoverished implementation of the problem-solving task. We present a new version of the model which captures the role of ‘ability’ in a meaningful way, and use it to explore the trade-offs (...) between diversity and ability in scientific problem solving. (shrink)