Results for 'intelligence cycle'

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  1.  40
    Intelligence Cycle.Nicolae Sfetcu - manuscript
    The intelligence cycle is a set of processes used to provide useful information for decision-making. The cycle consists of several processes. The related counter-intelligence area is tasked with preventing information efforts from others. A basic model of the process of collecting and analyzing information is called the "intelligence cycle". This model can be applied, and, like all the basic models, it does not reflect the fullness of real-world operations. Through intelligence cycle activities, (...)
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  2. An Ontological Approach to Representing the Product Life Cycle.J. Neil Otte, Dimitris Kiritsi, Munira Mohd Ali, Ruoyu Yang, Binbin Zhang, Ron Rudnicki, Rahul Rai & Barry Smith - 2019 - Applied Ontology 14 (2):1-19.
    The ability to access and share data is key to optimizing and streamlining any industrial production process. Unfortunately, the manufacturing industry is stymied by a lack of interoperability among the systems by which data are produced and managed, and this is true both within and across organizations. In this paper, we describe our work to address this problem through the creation of a suite of modular ontologies representing the product life cycle and its successive phases, from design to end (...)
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  3.  57
    Epistemology of Intelligence Agencies.Nicolae Sfetcu - manuscript
    About the analogy between the epistemological and methodological aspects of the activity of intelligence agencies and some scientific disciplines, advocating for a more scientific approach to the process of collecting and analyzing information within the intelligence cycle. I assert that the theoretical, ontological and epistemological aspects of the activity of many intelligence agencies are underestimated, leading to incomplete understanding of current phenomena and confusion in inter-institutional collaboration. After a brief Introduction, which includes a history of the (...)
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  4.  12
    Intelligence Methodologies.Nicolae Sfetcu - manuscript
    Methodology, in intelligence, consists of the methods used to make decisions about threats, especially in the intelligence analysis discipline. The enormous amount of information collected by intelligence agencies often puts them in the inability to analyze them all. The US intelligence community collects over one billion daily information. The nature and characteristics of the information gathered as well as their credibility also have an impact on the intelligence analysis. Clark proposed a methodology for analyzing information (...)
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  5. Future Progress in Artificial Intelligence: A Survey of Expert Opinion.Vincent C. Müller & Nick Bostrom - 2016 - In Vincent Müller (ed.), Fundamental Issues of Artificial Intelligence. Springer. pp. 553-571.
    There is, in some quarters, concern about high–level machine intelligence and superintelligent AI coming up in a few decades, bringing with it significant risks for humanity. In other quarters, these issues are ignored or considered science fiction. We wanted to clarify what the distribution of opinions actually is, what probability the best experts currently assign to high–level machine intelligence coming up within a particular time–frame, which risks they see with that development, and how fast they see these developing. (...)
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  6. Social Machinery and Intelligence.Nello Cristianini, James Ladyman & Teresa Scantamburlo - manuscript
    Social machines are systems formed by technical and human elements interacting in a structured manner. The use of digital platforms as mediators allows large numbers of human participants to join such mechanisms, creating systems where interconnected digital and human components operate as a single machine capable of highly sophisticated behaviour. Under certain conditions, such systems can be described as autonomous and goal-driven agents. Many examples of modern Artificial Intelligence (AI) can be regarded as instances of this class of mechanisms. (...)
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  7. AAAI: An Argument Against Artificial Intelligence.Sander Beckers - 2017 - In Vincent Müller (ed.), Philosophy and theory of artificial intelligence 2017. Berlin: Springer. pp. 235-247.
    The ethical concerns regarding the successful development of an Artificial Intelligence have received a lot of attention lately. The idea is that even if we have good reason to believe that it is very unlikely, the mere possibility of an AI causing extreme human suffering is important enough to warrant serious consideration. Others look at this problem from the opposite perspective, namely that of the AI itself. Here the idea is that even if we have good reason to believe (...)
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  8. Classification of Global Catastrophic Risks Connected with Artificial Intelligence.Alexey Turchin & David Denkenberger - 2018 - AI and Society:1-17.
    A classification of the global catastrophic risks of AI is presented, along with a comprehensive list of previously identified risks. This classification allows the identification of several new risks. We show that at each level of AI’s intelligence power, separate types of possible catastrophes dominate. Our classification demonstrates that the field of AI risks is diverse, and includes many scenarios beyond the commonly discussed cases of a paperclip maximizer or robot-caused unemployment. Global catastrophic failure could happen at various levels (...)
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  9. Science is Not Always “Self-Correcting” : Fact–Value Conflation and the Study of Intelligence.Nathan Cofnas - 2016 - Foundations of Science 21 (3):477-492.
    Some prominent scientists and philosophers have stated openly that moral and political considerations should influence whether we accept or promulgate scientific theories. This widespread view has significantly influenced the development, and public perception, of intelligence research. Theories related to group differences in intelligence are often rejected a priori on explicitly moral grounds. Thus the idea, frequently expressed by commentators on science, that science is “self-correcting”—that hypotheses are simply abandoned when they are undermined by empirical evidence—may not be correct (...)
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  10. First Steps Towards an Ethics of Robots and Artificial Intelligence.John Tasioulas - 2019 - Journal of Practical Ethics 7 (1):61-95.
    This article offers an overview of the main first-order ethical questions raised by robots and Artificial Intelligence (RAIs) under five broad rubrics: functionality, inherent significance, rights and responsibilities, side-effects, and threats. The first letter of each rubric taken together conveniently generates the acronym FIRST. Special attention is given to the rubrics of functionality and inherent significance given the centrality of the former and the tendency to neglect the latter in virtue of its somewhat nebulous and contested character. In addition (...)
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  11. Editorial: Risks of General Artificial Intelligence.Vincent C. Müller - 2014 - Journal of Experimental and Theoretical Artificial Intelligence 26 (3):297-301.
    This is the editorial for a special volume of JETAI, featuring papers by Omohundro, Armstrong/Sotala/O’Heigeartaigh, T Goertzel, Brundage, Yampolskiy, B. Goertzel, Potapov/Rodinov, Kornai and Sandberg. - If the general intelligence of artificial systems were to surpass that of humans significantly, this would constitute a significant risk for humanity – so even if we estimate the probability of this event to be fairly low, it is necessary to think about it now. We need to estimate what progress we can expect, (...)
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  12. One Decade of Universal Artificial Intelligence.Marcus Hutter - 2012 - In Pei Wang & Ben Goertzel (eds.), Theoretical Foundations of Artificial General Intelligence. Springer. pp. 67--88.
    The first decade of this century has seen the nascency of the first mathematical theory of general artificial intelligence. This theory of Universal Artificial Intelligence (UAI) has made significant contributions to many theoretical, philosophical, and practical AI questions. In a series of papers culminating in book (Hutter, 2005), an exciting sound and complete mathematical model for a super intelligent agent (AIXI) has been developed and rigorously analyzed. While nowadays most AI researchers avoid discussing intelligence, the award-winning PhD (...)
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  13. A Multi-INT Semantic Reasoning Framework for Intelligence Analysis Support.Janssen Terry, Basik Herbert, Dean Mike & Barry Smith - 2010 - In L. Obrst, Terry Janssen & W. Ceusters (eds.), Ontologies and Semantic Technologies for the Intelligence Community. Amsterdam, The Netherlands: IOS Press. pp. 57-69.
    Lockheed Martin Corp. has funded research to generate a framework and methodology for developing semantic reasoning applications to support the discipline oflntelligence Analysis. This chapter outlines that framework, discusses how it may be used to advance the information sharing and integrated analytic needs of the Intelligence Community, and suggests a system I software architecture for such applications.
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  14. Social Intelligence: How to Integrate Research? A Mechanistic Perspective.Marcin Miłkowski - 2014 - Proceedings of the European Conference on Social Intelligence (ECSI-2014).
    Is there a field of social intelligence? Many various disciplines ap-proach the subject and it may only seem natural to suppose that different fields of study aim at explaining different phenomena; in other words, there is no spe-cial field of study of social intelligence. In this paper, I argue for an opposite claim. Namely, there is a way to integrate research on social intelligence, as long as one accepts the mechanistic account to explanation. Mechanistic inte-gration of different (...)
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  15.  56
    IAO-Intel: An Ontology of Information Artifacts in the Intelligence Domain.Barry Smith, Tatiana Malyuta, Ron Rudnicki, William Mandrick, David Salmen, Peter Morosoff, Danielle K. Duff, James Schoening & Kesny Parent - 2013 - In Proceedings of the Eighth International Conference on Semantic Technologies for Intelligence, Defense, and Security (STIDS), CEUR, vol. 1097. pp. 33-40.
    We describe on-going work on IAO-Intel, an information artifact ontology developed as part of a suite of ontologies designed to support the needs of the US Army intelligence community within the framework of the Distributed Common Ground System (DCGS-A). IAO-Intel provides a controlled, structured vocabulary for the consistent formulation of metadata about documents, images, emails and other carriers of information. It will provide a resource for uniform explication of the terms used in multiple existing military dictionaries, thesauri and metadata (...)
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  16. The Pragmatic Turn in Explainable Artificial Intelligence (XAI).Andrés Páez - 2019 - Minds and Machines:1-19.
    In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI. Intuitively, the purpose of providing an explanation of a model or a decision is to make it understandable to its stakeholders. But without a previous grasp of what it means to say that an agent understands a model or a decision, the explanatory strategies will (...)
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  17. Beneficial Artificial Intelligence Coordination by Means of a Value Sensitive Design Approach.Steven Umbrello - 2019 - Big Data and Cognitive Computing 3 (1):5.
    This paper argues that the Value Sensitive Design (VSD) methodology provides a principled approach to embedding common values in to AI systems both early and throughout the design process. To do so, it draws on an important case study: the evidence and final report of the UK Select Committee on Artificial Intelligence. This empirical investigation shows that the different and often disparate stakeholder groups that are implicated in AI design and use share some common values that can be used (...)
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  18.  61
    Nonconscious Cognitive Suffering: Considering Suffering Risks of Embodied Artificial Intelligence.Steven Umbrello & Stefan Lorenz Sorgner - 2019 - Philosophies 4 (2):24.
    Strong arguments have been formulated that the computational limits of disembodied artificial intelligence (AI) will, sooner or later, be a problem that needs to be addressed. Similarly, convincing cases for how embodied forms of AI can exceed these limits makes for worthwhile research avenues. This paper discusses how embodied cognition brings with it other forms of information integration and decision-making consequences that typically involve discussions of machine cognition and similarly, machine consciousness. N. Katherine Hayles’s novel conception of nonconscious cognition (...)
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  19. Artificial Intelligence as a Means to Moral Enhancement.Michał Klincewicz - 2016 - Studies in Logic, Grammar and Rhetoric 48 (1):171-187.
    This paper critically assesses the possibility of moral enhancement with ambient intelligence technologies and artificial intelligence presented in Savulescu and Maslen (2015). The main problem with their proposal is that it is not robust enough to play a normative role in users’ behavior. A more promising approach, and the one presented in the paper, relies on an artifi-cial moral reasoning engine, which is designed to present its users with moral arguments grounded in first-order normative theories, such as Kantianism (...)
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  20. The Case for Government by Artificial Intelligence.Steven James Bartlett - 2016 - Willamette University Faculty Research Website: Http://Www.Willamette.Edu/~Sbartlet/Documents/Bartlett_The%20Case%20for%20Government%20by%20Artifici al%20Intelligence.Pdf.
    THE CASE FOR GOVERNMENT BY ARTIFICIAL INTELLIGENCE. Tired of election madness? The rhetoric of politicians? Their unreliable promises? And less than good government? -/- Until recently, it hasn’t been hard for people to give up control to computers. Not very many people miss the effort and time required to do calculations by hand, to keep track of their finances, or to complete their tax returns manually. But relinquishing direct human control to self-driving cars is expected to be more of (...)
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  21. Epistemological Intelligence.Steven James Bartlett - 2017 - Willamette University Faculty Research Website.
    The monograph’s twofold purpose is to recognize epistemological intelligence as a distinguishable variety of human intelligence, one that is especially important to philosophers, and to understand the challenges posed by the psychological profile of philosophers that can impede the development and cultivation of the skills associated with epistemological intelligence.
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  22. Philosophy and Theory of Artificial Intelligence 2017.Vincent Müller (ed.) - 2017 - Berlin: Springer.
    This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI (...)
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  23. Aspects of Sex Differences: Social Intelligence Vs. Creative Intelligence.Ferdinand Fellmann & Esther Redolfi Widmann - 2017 - Advances in Anthropology 7:298-317.
    In this article, we argue that there is an essential difference between social intelligence and creative intelligence, and that they have their foundation in human sexuality. For sex differences, we refer to the vast psychological, neurological, and cognitive science research where problem-solving, verbal skills, logical reasoning, and other topics are dealt with. Intelligence tests suggest that, on average, neither sex has more general intelligence than the other. Though people are equals in general intelligence, they are (...)
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  24. Introduction: Philosophy and Theory of Artificial Intelligence.Vincent C. Müller - 2012 - Minds and Machines 22 (2):67-69.
    The theory and philosophy of artificial intelligence has come to a crucial point where the agenda for the forthcoming years is in the air. This special volume of Minds and Machines presents leading invited papers from a conference on the “Philosophy and Theory of Artificial Intelligence” that was held in October 2011 in Thessaloniki. Artificial Intelligence is perhaps unique among engineering subjects in that it has raised very basic questions about the nature of computing, perception, reasoning, learning, (...)
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  25. ELEMENTS OF COGNITIVE SCIENCES AND ARTIFICIAL INTELLIGENCE IN GAYATRI MANTRA.Varanasi Ramabrahmam - 2006 - In Proceedings of National seminar on Bharatiya Heritage in Engineering and Technology, May 11-13, 2006, at Department of Metallurgy and Inorganic Chemistry, I.I.Sc., Bangalore, India. pp. 249-254.
    The syllables and series of sounds composing Gayatri Mantra, and the sense and meaning attached to them are analyzed using Upanishadic Wisdom, Advaitha Philosophy and Sabdabrahma Siddhanta. The physical structure of mind as revealed by this analysis is presented. An insight of various phases of mind, their rise and set, their significance and implications to cognitive sciences and natural language comprehension branch of artificial intelligence are discussed. The possible applications of such an insight in the fields of cognitive sciences, (...)
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  26.  86
    Where the Standard Approach in Comparative Neuroscience Fails and Where It Works: General Intelligence and Brain Asymmetries.Davide Serpico & Elisa Frasnelli - 2018 - Comparative Cognition and Behavior Reviews 13:95-98.
    Although brain size and the concept of intelligence have been extensively used in comparative neuroscience to study cognition and its evolution, such coarse-grained traits may not be informative enough about important aspects of neurocognitive systems. By taking into account the different evolutionary trajectories and the selection pressures on neurophysiology across species, Logan and colleagues suggest that the cognitive abilities of an organism should be investigated by considering the fine-grained and species-specific phenotypic traits that characterize it. In such a way, (...)
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  27.  50
    AISC 17 Talk: The Explanatory Problems of Deep Learning in Artificial Intelligence and Computational Cognitive Science: Two Possible Research Agendas.Antonio Lieto - 2018 - In Proceedings of AISC 2017.
    Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical (...)
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  28. Mandevillian Intelligence: From Individual Vice to Collective Virtue.Paul Smart - forthcoming - In Joseph Adam Carter, Andy Clark, Jesper Kallestrup, Spyridon Orestis Palermos & Duncan Pritchard (eds.), Socially-Extended Epistemology. Oxford, UK: Oxford University Press.
    Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive shortcomings, limitations and biases play a positive functional role in yielding various forms of collective cognitive success. When this idea is transposed to the epistemological domain, mandevillian intelligence emerges as the idea that individual forms of intellectual vice may, on occasion, support the epistemic performance of some form of multi-agent ensemble, such as a socio-epistemic system, a collective doxastic agent, or an epistemic group agent. (...)
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  29. Theory of Intelligence and BIAS of the Classic IQ Method.Miro Brada - manuscript
    The classic IQ method resides in solving one right solution for a given verbal or non-verbal tasks. However the same solution can have various justifications, or even there can be more solutions based on very original or bizarre justification. Therefore the more objective intelligence test should detect justifications / logic rather than solution. I present set of tests assessing justifications that detect intelligence, flexibility and originality at the same time. On the sample of 600 people I confirmed the (...)
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  30. Ontology for the Intelligence Analyst.Barry Smith - 2012 - CrossTalk 14 (Nov/Dec):18-25.
    As available intelligence data and information expand in both quantity and variety, new techniques must be deployed for search and analytics. One technique involves the semantic enhancement of data through the creation of what are called ‘ontologies’ or ‘controlled vocabularies.’ When multiple different bodies of heterogeneous data are tagged by means of terms from common ontologies, then these data become linked together in ways which allow more effective retrieval and integration. We describe a simple case study to show how (...)
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  31. Risks of Artificial General Intelligence.Vincent C. Müller (ed.) - 2014 - Taylor & Francis (JETAI).
    Special Issue “Risks of artificial general intelligence”, Journal of Experimental and Theoretical Artificial Intelligence, 26/3 (2014), ed. Vincent C. Müller. http://www.tandfonline.com/toc/teta20/26/3# - Risks of general artificial intelligence, Vincent C. Müller, pages 297-301 - Autonomous technology and the greater human good - Steve Omohundro - pages 303-315 - - - The errors, insights and lessons of famous AI predictions – and what they mean for the future - Stuart Armstrong, Kaj Sotala & Seán S. Ó hÉigeartaigh - pages (...)
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  32. Experience, Memory and Intelligence.John T. Sanders - 1985 - The Monist 68 (4):507-521.
    What characterizes most technical or theoretical accounts of memory is their reliance upon an internal storage model. Psychologists and neurophysiologists have suggested neural traces (either dynamic or static) as the mechanism for this storage, and designers of artificial intelligence have relied upon the same general model, instantiated magnetically or electronically instead of neurally, to do the same job. Both psychology and artificial intelligence design have heretofore relied, without much question, upon the idea that memory is to be understood (...)
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  33. Philosophy and Theory of Artificial Intelligence, 3–4 October (Report on PT-AI 2011).Vincent C. Müller - 2011 - The Reasoner 5 (11):192-193.
    Report for "The Reasoner" on the conference "Philosophy and Theory of Artificial Intelligence", 3 & 4 October 2011, Thessaloniki, Anatolia College/ACT, http://www.pt-ai.org. --- Organization: Vincent C. Müller, Professor of Philosophy at ACT & James Martin Fellow, Oxford http://www.sophia.de --- Sponsors: EUCogII, Oxford-FutureTech, AAAI, ACM-SIGART, IACAP, ECCAI.
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  34. Implications for Virtue Epistemology From Psychological Science: Intelligence as an Interactionist Virtue.Mark Alfano & Gus Skorburg - forthcoming - In Heather Battaly (ed.), Handbook of Virtue Epistemology. Routledge.
    This chapter aims to expand the body of empirical literature considered relevant to virtue theory beyond the burned-over districts that are the situationist challenges to virtue ethics and epistemology. We thus raise a rather simple-sounding question: why doesn’t virtue epistemology have an account of intelligence? In the first section, we sketch the history and present state of the person-situation debate to argue for the importance of an interactionist framework in bringing psychological research in general, and intelligence research in (...)
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  35. Genes, Affect, and Reason: Why Autonomous Robot Intelligence Will Be Nothing Like Human Intelligence.Henry Moss - 2016 - Techné: Research in Philosophy and Technology 20 (1):1-15.
    Abstract: Many believe that, in addition to cognitive capacities, autonomous robots need something similar to affect. As in humans, affect, including specific emotions, would filter robot experience based on a set of goals, values, and interests. This narrows behavioral options and avoids combinatorial explosion or regress problems that challenge purely cognitive assessments in a continuously changing experiential field. Adding human-like affect to robots is not straightforward, however. Affect in organisms is an aspect of evolved biological systems, from the taxes of (...)
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  36. Turing on the Integration of Human and Machine Intelligence.S. G. Sterrett - manuscript
    Abstract Philosophical discussion of Alan Turing’s writings on intelligence has mostly revolved around a single point made in a paper published in the journal Mind in 1950. This is unfortunate, for Turing’s reflections on machine (artificial) intelligence, human intelligence, and the relation between them were more extensive and sophisticated. They are seen to be extremely well-considered and sound in retrospect. Recently, IBM developed a question-answering computer (Watson) that could compete against humans on the game show Jeopardy! There (...)
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  37. Review of A History of Intelligence and 'Intellectual Disability': The Shaping of Psychology in Early Modern Europe by C. F. Goodey. [REVIEW]María G. Navarro - 2013 - Seventeenth-Century News 71 (1 & 2).
    A History of Intelligence and “Intellectual Disability” examines how the concepts of intellectual ability and disability became part of psychology, medicine and biology. Focusing on the period between the Protestant Reform and 1700, this book shows that in many cases it has been accepted without scientific and psychological foundations that intelligence and disability describe natural or trans-historical realities.
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  38. Philosophy and Theory of Artificial Intelligence.Vincent C. Müller (ed.) - 2013 - Springer.
    [Müller, Vincent C. (ed.), (2013), Philosophy and theory of artificial intelligence (SAPERE, 5; Berlin: Springer). 429 pp. ] --- Can we make machines that think and act like humans or other natural intelligent agents? The answer to this question depends on how we see ourselves and how we see the machines in question. Classical AI and cognitive science had claimed that cognition is computation, and can thus be reproduced on other computing machines, possibly surpassing the abilities of human (...). This consensus has now come under threat and the agenda for the philosophy and theory of AI must be set anew, re-defining the relation between AI and Cognitive Science. We can re-claim the original vision of general AI from the technical AI disciplines; we can reject classical cognitive science and replace it with a new theory (e.g. embodied); or we can try to find new ways to approach AI, for example from neuroscience or from systems theory. To do this, we must go back to the basic questions on computing, cognition and ethics for AI. The 30 papers in this volume provide cutting-edge work from leading researchers that define where we stand and where we should go from here. (shrink)
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  39.  38
    Social Intelligence: How to Integrate Research? A Mechanistic Perspective.Marcin Miłkowski - forthcoming - AI and Society:1-10.
    Is there a field of social intelligence? Many various disciplines approach the subject and it may only seem natural to suppose that different fields of study aim at explaining different phenomena; in other words, there is no special field of study of social intelligence. In this paper, I argue for an opposite claim. Namely, there is a way to integrate research on social intelligence, as long as one accepts the mechanistic account to explanation. Mechanistic integration of different (...)
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  40. Embodied Intelligence: Epistemological Remarks on an Emerging Paradigm in the Artificial Intelligence Debate.Nicola Di Stefano & Giampaolo Ghilardi - 2013 - Epistemologia 36 (1):100-111.
    In this paper we want to analyze some philosophical and epistemological connections between a new kind of technology recently developed within robotics, and the previous mechanical approach. A new paradigm about machine-design in robotics, currently defined as ‘Embodied Intelligence’, has recently been developed. Here we consider the debate on the relationship between the hand and the intellect, from the perspective of the history of philosophy, aiming at providing a more suitable understanding of this paradigm. The new bottom-up approach to (...)
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  41.  42
    On the Verisimilitude of Artificial Intelligence.Roger Vergauwen & Rodrigo González - 2005 - Logique Et Analyse- 190 (189):323-350.
    This paper investigates how the simulation of intelligence, an activity that has been considered the notional task of Artificial Intelligence, does not comprise its duplication. Briefly touching on the distinction between conceivability and possibility, and commenting on Ryan’s approach to fiction in terms of the interplay between possible worlds and her principle of minimal departure, we specify verisimilitude in Artificial Intelligence as the accurate resemblance of intelligence by its simulation and, from this characterization, claim the metaphysical (...)
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  42.  30
    Improving Algebraic Thinking Skill, Beliefs And Attitude For Mathematics Throught Learning Cycle Based On Beliefs.Widodo Winarso & Toheri - 2017 - Munich University Library.
    In the recent years, problem-solving become a central topic that discussed by educators or researchers in mathematics education. it’s not only as the ability or as a method of teaching. but also, it is a little in reviewing about the components of the support to succeed in problem-solving, such as student's belief and attitude towards mathematics, algebraic thinking skills, resources and teaching materials. In this paper, examines the algebraic thinking skills as a foundation for problem-solving, and learning cycle as (...)
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  43.  50
    The Idea of a Social Cycle.Gene Callahan & Andreas Hoffman - manuscript
    The paper aims to explore what it means for something to be a social cycle, for a theory to be a social cycle theory, and to offer a suggestion for a simple, yet, we believe, fundamentally grounded schema for categorizing them. We show that a broad range of cycle theories can be described within the concept of disruption and adjustments. Further, many important cycle theories are true endogenous social cycle theories in which the theory provides (...)
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  44. Fundamental Issues of Artificial Intelligence.Vincent Müller (ed.) - 2016 - Springer.
    [Müller, Vincent C. (ed.), (2016), Fundamental issues of artificial intelligence (Synthese Library, 377; Berlin: Springer). 570 pp.] -- This volume offers a look at the fundamental issues of present and future AI, especially from cognitive science, computer science, neuroscience and philosophy. This work examines the conditions for artificial intelligence, how these relate to the conditions for intelligence in humans and other natural agents, as well as ethical and societal problems that artificial intelligence raises or will raise. (...)
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  45. Editorial: Risks of Artificial Intelligence.Vincent C. Müller - 2016 - In Risks of artificial intelligence. CRC Press - Chapman & Hall. pp. 1-8.
    If the intelligence of artificial systems were to surpass that of humans significantly, this would constitute a significant risk for humanity. Time has come to consider these issues, and this consideration must include progress in AI as much as insights from the theory of AI. The papers in this volume try to make cautious headway in setting the problem, evaluating predictions on the future of AI, proposing ways to ensure that AI systems will be beneficial to humans – and (...)
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  46.  70
    Tacit Representations and Artificial Intelligence: Hidden Lessons From an Embodied Perspective on Cognition.Elena Spitzer - 2016 - In Vincent Müller (ed.), Fundamental Issues of Artificial Intelligence. Springer. pp. 425-441.
    In this paper, I explore how an embodied perspective on cognition might inform research on artificial intelligence. Many embodied cognition theorists object to the central role that representations play on the traditional view of cognition. Based on these objections, it may seem that the lesson from embodied cognition is that AI should abandon representation as a central component of intelligence. However, I argue that the lesson from embodied cognition is actually that AI research should shift its focus from (...)
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  47. Artificial Intelligence and Personhood.Robert K. Garcia - 2002 - In John Kilner, Diane Uustal & Christopher Hook (eds.), Cutting Edge Bioethics: A Christian Exploration of Technology and Trends.
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  48. Integration of Intelligence Data Through Semantic Enhancement.David Salmen, Tatiana Malyuta, Alan Hansen, Shaun Cronen & Barry Smith - 2011 - In Proceedings of the Conference on Semantic Technology in Intelligence, Defense and Security (STIDS). CEUR, Vol. 808.
    We describe a strategy for integration of data that is based on the idea of semantic enhancement. The strategy promises a number of benefits: it can be applied incrementally; it creates minimal barriers to the incorporation of new data into the semantically enhanced system; it preserves the existing data (including any existing data-semantics) in their original form (thus all provenance information is retained, and no heavy preprocessing is required); and it embraces the full spectrum of data sources, types, models, and (...)
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  49. Understanding Unconscious Intelligence and Intuition: "Blink" and Beyond.Lois Isenman - 2013 - Perspectives in Biology and Medicine 56 (1):148-166.
    The importance of unconscious cognition is seeping into popular consciousness. A number of recent books bridging the academic world and the reading public stress that at least a portion of decision-making depends not on conscious reasoning, but instead on cognition that occurs below awareness. However, these books provide a limited perspective on how the unconscious mind works and the potential power of intuition. This essay is an effort to expand the picture. It is structured around the book that has garnered (...)
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  50. A Product Life Cycle Ontology for Additive Manufacturing.Munira Mohd Ali, Rahul Rai, J. Neil Otte & Barry Smith - 2019 - Computers in Industry 105:191-203.
    The manufacturing industry is evolving rapidly, becoming more complex, more interconnected, and more geographically distributed. Competitive pressure and diversity of consumer demand are driving manufacturing companies to rely more and more on improved knowledge management practices. As a result, multiple software systems are being created to support the integration of data across the product life cycle. Unfortunately, these systems manifest a low degree of interoperability, and this creates problems, for instance when different enterprises or different branches of an enterprise (...)
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