Search results for 'Cooperative intelligence' (try it on Scholar)

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  1. Of Intelligence (2002). Unraveling Trie Enigma of Human Intelligence: Evolutionary Psychology and the Multimodular Mind. In Robert J. Sternberg & J. Kaufman (eds.), The Evolution of Intelligence. Lawrence Erlbaum 145.
     
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  2.  1
    James Campbell (1996). [Book Review] Understanding John Dewey, Nature and Cooperative Intelligence. [REVIEW] Ethics 107 (1):166-168.
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  3.  8
    Donald F. Koch (1996). Book Review:Understanding John Dewey: Nature and Cooperative Intelligence. James Campbell. [REVIEW] Ethics 107 (1):166-.
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    Michael Paetau (1991). 'Adaptive' and 'Cooperative' Computer Systems — A Challenge for Sociological Research. AI and Society 5 (1):61-70.
    The vision of the new generation of office systems is based on the hypothesis that an automatic support system is all the more useful and acceptable, the more systems behaviour and performance are in accordance with features ofhuman behaviour. Consequently recent development activities are influenced by the paradigm of the computer as man's “cooperative assistant”. The metaphors ofassistance andcooperation illustrate some major requirements to be met by new office systems. Cooperative office systems will raise a set of new (...)
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  5.  9
    Satinder P. Gill & Jan Borchers (2004). Knowledge in Co-Action: Social Intelligence in Collaborative Design Activity. [REVIEW] AI and Society 18 (1):86-86.
    Skilled cooperative action means being able to understand the communicative situation and know how and when to respond appropriately for the purpose at hand. This skill is of the performance of knowledge in co-action and is a form of social intelligence for sustainable interaction. Social intelligence, here, denotes the ability of actors and agents to manage their relationships with each other. Within an environment we have people, tools, artefacts and technologies that we engage with. Let us consider (...)
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  6.  71
    Krist Vaesen (2012). Cooperative Feeding and Breeding, and the Evolution of Executive Control. Biology and Philosophy 27 (1):115-124.
    Dubreuil (Biol Phil 25:53–73, 2010b , this journal) argues that modern-like cognitive abilities for inhibitory control and goal maintenance most likely evolved in Homo heidelbergensis , much before the evolution of oft-cited modern traits, such as symbolism and art. Dubreuil’s argument proceeds in two steps. First, he identifies two behavioral traits that are supposed to be indicative of the presence of a capacity for inhibition and goal maintenance: cooperative feeding and cooperative breeding. Next, he tries to show that (...)
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  7.  33
    Raimo Tuomela (1996). Philosophy and Distributed Artificial Intelligence: The Case of Joint Intention. In N. Jennings & G. O'Hare (eds.), Foundations of Distributed Artificial Intelligence. Wiley
    In current philosophical research the term 'philosophy of social action' can be used - and has been used - in a broad sense to encompass the following central research topics: 1) action occurring in a social context; this includes multi-agent action; 2) joint attitudes (or "we-attitudes" such as joint intention, mutual belief) and other social attitudes needed for the explication and explanation of social action; 3) social macro-notions, such as actions performed by social groups and properties of social groups such (...)
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  8.  21
    H. G. Callaway (1997). Review of James Campbell, Understanding John Dewey. [REVIEW] Philosophical Quarterly 47 (187):272-275.
    James Campbell's Understanding John Dewey represents the latest of his series of recent books, focused on the classical pragmatist tradition. In The Community Reconstructs. Campbell capably explored the meaning and relevance of pragmatic social thought, urging that the social pragmatists combined 'the inquiring and critical spirit of Peirce' with 'issues of general and direct human concern that interested James. Dewey is 'the most important figure of this movement' and the "primary figure' for the earlier book. Campbell now engages Dewey more (...)
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  9.  9
    Patrice Caire (2009). Designing Convivial Digital Cities: A Social Intelligence Design Approach. [REVIEW] AI and Society 24 (1):97-114.
    Conviviality has been identified as a key concept necessary to web communities, such as digital cities, and while it has been simultaneously defined in literature as individual freedom realized in personal interdependence, rational and cooperative behavior and normative instrument, no model for conviviality has yet been proposed for computer science. In this article, we raised the question whether social intelligence design could be used to designing convivial digital cities. We first looked at digital cities and identified, from a (...)
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  10.  4
    Robert C. Muller (1993). Enhancing Creativity, Innovation and Cooperation. AI and Society 7 (1):4-39.
    The paper explores the creative thinking process and throws light on creativity enhancement. From the perspective of possible creativity enhancement both the characteristics of creativity and the creative thinking process are discussed, together with an analysis of the process and its common factors. Constraints on innovation (as a special type of creativity), innovation management and the acceptance of change are discussed; creativity between cooperating individuals is also examined. Some possible computer-based tools to enhance creativity, including innovation, are discussed. A framework (...)
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  11. Naoyuki Nide, Shiro Takata & Megumi Fujita (2011). Modeling Cooperative Actions Using an Extended BDI Logic TOMATOes. Transactions of the Japanese Society for Artificial Intelligence 26:13-24.
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  12. Satomi Baba, Atsushi Iwasaki, Makoto Yokoo, Marius C. Silaghi, Katsutoshi Hirayama & Toshihiro Matsui (2011). Cooperative Problem Solving Against Adversary: Quantified Distributed Constraint Satisfaction Problem. Transactions of the Japanese Society for Artificial Intelligence 26:136-146.
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  13. Lifeng He, Yuyan Chao, Tsuyoshi Nakamura & Hidenori Itoh (2002). Multi-Agent Cooperative Reasoning Using E-Knowledge and I-Knowledge. Transactions of the Japanese Society for Artificial Intelligence 17:224-229.
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  14. Vincent C. Müller & Nick Bostrom (2016). Future Progress in Artificial Intelligence: A Survey of Expert Opinion. In Fundamental Issues of Artificial Intelligence. Springer 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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  15. Vincent C. Müller (2014). Editorial: Risks of General Artificial Intelligence. 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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  16. Marcus Hutter (2012). One Decade of Universal Artificial Intelligence. In Pei Wang & Ben Goertzel (eds.), Theoretical Foundations of Artificial General Intelligence. Springer 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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  17.  52
    Marcin Miłkowski, Social Intelligence: How to Integrate Research? A Mechanistic Perspective. 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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  18. James H. Fetzer (1990). Artificial Intelligence: Its Scope and Limits. Kluwer.
    1. WHAT IS ARTIFICIAL INTELLIGENCE? One of the fascinating aspects of the field of artificial intelligence (AI) is that the precise nature of its subject ..
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  19.  39
    Christopher Mole (2012). Three Philosophical Lessons for the Analysis of Criminal and Military Intelligence. Intelligence and National Security 27 (4):441-58.
    It has recently been suggested that philosophy – in particular epistemology – has a contribution to make to the analysis of criminal and military intelligence. The present article pursues this suggestion, taking three phenomena that have recently been studied by philosophers, and showing that they have important implications for the gathering and sharing of intelligence, and for the use of intelligence in the determining of military strategy. The phenomena discussed are: (1) Simpson's Paradox, (2) the distinction between (...)
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  20.  59
    Hans F. M. Crombag (1993). On the Artificiality of Artificial Intelligence. Artificial Intelligence and Law 2 (1):39-49.
    In this article the question is raised whether artificial intelligence has any psychological relevance, i.e. contributes to our knowledge of how the mind/brain works. It is argued that the psychological relevance of artificial intelligence of the symbolic kind is questionable as yet, since there is no indication that the brain structurally resembles or operates like a digital computer. However, artificial intelligence of the connectionist kind may have psychological relevance, not because the brain is a neural network, but (...)
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  21.  9
    Thomas Li-Ping Tang & Toto Sutarso (2013). Falling or Not Falling Into Temptation? Multiple Faces of Temptation, Monetary Intelligence, and Unethical Intentions Across Gender. Journal of Business Ethics 116 (3):529-552.
    We develop a theoretical model, explore the relationship between temptation (both reflective and formative) and unethical intentions by treating monetary intelligence (MI) as a mediator, and examine the direct (temptation to unethical intentions) and indirect (temptation to MI to unethical intentions) paths simultaneously based on multiple-wave panel data collected from 340 part-time employees and university (business) students. The positive indirect path suggested that yielding to temptation (e.g., high cognitive impairment and lack of self-control) led to poor MI (low stewardship (...)
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  22.  29
    Steven James Bartlett, The Case for Government by Artificial Intelligence. 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 (...)
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    Soňa Lemrová, Eva Reiterová, Renáta Fatěnová, Karel Lemr & Thomas Li-Ping Tang (2013). Money is Power: Monetary Intelligence—Love of Money and Temptation of Materialism Among Czech University Students. [REVIEW] Journal of Business Ethics 125 (2):1-20.
    In this study, we develop a theoretical model of monetary intelligence (MI), explore the extent to which individuals’ meaning of money is related to the pursuit of materialistic purposes, and test our model using the whole sample and across college major and gender. We select the 15-item love of money (LOM) construct—Factors Good, Evil (Affective), Budget (Behavioral), Achievement, and Power (Cognitive)—from the Money Ethic Scale and Factors Success and Centrality and two indicators—from the Materialism Scale. Based on our data (...)
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    Jingqiu Chen, Thomas Li-Ping Tang & Ningyu Tang (2013). Temptation, Monetary Intelligence (Love of Money), and Environmental Context on Unethical Intentions and Cheating. Journal of Business Ethics 123 (2):1-23.
    In Study 1, we test a theoretical model involving temptation, monetary intelligence (MI), a mediator, and unethical intentions and investigate the direct and indirect paths simultaneously based on multiple-wave panel data collected in open classrooms from 492 American and 256 Chinese students. For the whole sample, temptation is related to low unethical intentions indirectly. Multi-group analyses reveal that temptation predicts unethical intentions both indirectly and directly for male American students only; but not for female American students. For Chinese students, (...)
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  25. Hong Liu, Bin Hu & Philip Moore (2015). HCI Model with Learning Mechanism for Cooperative Design in Pervasive Computing Environment. Journal of Internet Technology 16.
    This paper presents a human-computer interaction model with a three layers learning mechanism in a pervasive environment. We begin with a discussion around a number of important issues related to human-computer interaction followed by a description of the architecture for a multi-agent cooperative design system for pervasive computing environment. We present our proposed three- layer HCI model and introduce the group formation algorithm, which is predicated on a dynamic sharing niche technology. Finally, we explore the cooperative reinforcement learning (...)
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  26.  52
    Thomas Li-Ping Tang & Yuh-Jia Chen (2008). Intelligence Vs. Wisdom: The Love of Money, Machiavellianism, and Unethical Behavior Across College Major and Gender. [REVIEW] Journal of Business Ethics 82 (1):1 - 26.
    This research investigates the efficacy of business ethics intervention, tests a theoretical model that the love of money is directly or indirectly related to propensity to engage in unethical behavior (PUB), and treats college major (business vs. psychology) and gender (male vs. female) as moderators in multi-group analyses. Results suggested that business students who received business ethics intervention significantly changed their conceptions of unethical behavior and reduced their propensity to engage in theft; while psychology students without intervention had no such (...)
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  27. Roman Yampolskiy & Joshua Fox (2013). Safety Engineering for Artificial General Intelligence. Topoi 32 (2):217-226.
    Machine ethics and robot rights are quickly becoming hot topics in artificial intelligence and robotics communities. We will argue that attempts to attribute moral agency and assign rights to all intelligent machines are misguided, whether applied to infrahuman or superhuman AIs, as are proposals to limit the negative effects of AIs by constraining their behavior. As an alternative, we propose a new science of safety engineering for intelligent artificial agents based on maximizing for what humans value. In particular, we (...)
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  28.  25
    Douglas N. Walton (2008). Witness Testimony Evidence: Argumentation, Artificial Intelligence, and Law. Cambridge University Press.
    Recent work in artificial intelligence has increasingly turned to argumentation as a rich, interdisciplinary area of research that can provide new methods related to evidence and reasoning in the area of law. Douglas Walton provides an introduction to basic concepts, tools and methods in argumentation theory and artificial intelligence as applied to the analysis and evaluation of witness testimony. He shows how witness testimony is by its nature inherently fallible and sometimes subject to disastrous failures. At the same (...)
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  29.  87
    Vincent C. Müller (2011). Philosophy and Theory of Artificial Intelligence, 3–4 October (Report on PT-AI 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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  30.  92
    Satish P. Deshpande & Jacob Joseph (2009). Impact of Emotional Intelligence, Ethical Climate, and Behavior of Peers on Ethical Behavior of Nurses. Journal of Business Ethics 85 (3):403 - 410.
    This study examines factors impacting ethical behavior of 103 hospital nurses. The level of emotional intelligence and ethical behavior of peers had a significant impact on ethical behavior of nurses. Independence climate had a significant impact on ethical behavior of nurses. Other ethical climate types such as professional, caring, rules, instrumental, and efficiency did not impact ethical behavior of respondents. Implications of this study for researchers and practitioners are discussed.
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  31.  54
    Olle Blomberg (1st ed. 2015). An Account of Boeschian Cooperative Behaviour. In Catrin Misselhorn (ed.), Collective Agency and Cooperation in Natural and Artificial Systems. Springer International Publishing
    Philosophical accounts of joint action are often prefaced by the observation that there are two different senses in which several agents can intentionally perform an action Φ, such as go for a walk or capture the prey. The agents might intentionally Φ together, as a collective, or they might intentionally Φ in parallel, where Φ is distributively assigned to the agents, considered as a set of individuals. The accounts are supposed to characterise what is distinctive about activities in which several (...)
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  32. Margaret A. Boden (ed.) (1990). The Philosophy of Artificial Intelligence. Oxford University Press.
    This interdisciplinary collection of classical and contemporary readings provides a clear and comprehensive guide to the many hotly-debated philosophical issues at the heart of artificial intelligence.
     
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  33.  18
    John Angelidis & Nabil A. Ibrahim (2011). The Impact of Emotional Intelligence on the Ethical Judgment of Managers. Journal of Business Ethics 99 (S1):111-119.
    In recent years there has been a substantial amount of research on emotional intelligence (EI) across a wide range of disciplines. Also, this term has been receiving increasing attention in the popular business press. This article extends previous research by seeking to determine whether there is a relationship between emotional intelligence and ethical judgment among practicing managers with respect to questions of ethical nature that can arise in their professional activity. It analyzes the results of a survey of (...)
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  34.  31
    James M. Bloodgood, William H. Turnley & Peter Mudrack (2008). The Influence of Ethics Instruction, Religiosity, and Intelligence on Cheating Behavior. Journal of Business Ethics 82 (3):557 - 571.
    This study examines the influence of ethics instruction, religiosity, and intelligence on cheating behavior. A sample of 230 upper level, undergraduate business students had the opportunity to increase their chances of winning money in an experimental situation by falsely reporting their task performance. In general, the results indicate that students who attended worship services more frequently were less likely to cheat than those who attended worship services less frequently, but that students who had taken a course in business ethics (...)
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  35.  63
    Philip Brey (2005). Freedom and Privacy in Ambient Intelligence. Ethics and Information Technology 7 (3):157-166.
    This paper analyzes ethical aspects of the new paradigm of Ambient Intelligence, which is a combination of Ubiquitous Computing and Intelligent User Interfaces (IUI’s). After an introduction to the approach, two key ethical dimensions will be analyzed: freedom and privacy. It is argued that Ambient Intelligence, though often designed to enhance freedom and control, has the potential to limit freedom and autonomy as well. Ambient Intelligence also harbors great privacy risks, and these are explored as well.
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  36. Viola Schiaffonati (2003). A Framework for the Foundation of the Philosophy of Artificial Intelligence. Minds and Machines 13 (4):537-552.
    The peculiarity of the relationship between philosophy and Artificial Intelligence (AI) has been evidenced since the advent of AI. This paper aims to put the basis of an extended and well founded philosophy of AI: it delineates a multi-layered general framework to which different contributions in the field may be traced back. The core point is to underline how in the same scenario both the role of philosophy on AI and role of AI on philosophy must be considered. Moreover, (...)
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  37. John M. Preston & John Mark Bishop (eds.) (2002). Views Into the Chinese Room: New Essays on Searle and Artificial Intelligence. Oxford University Press.
    The most famous challenge to computational cognitive science and artificial intelligence is the philosopher John Searle's "Chinese Room" argument.
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  38.  27
    Nancy Matchett (2009). Cooperative Learning, Critical Thinking and Character. Techniques to Cultivate Ethical Deliberation. Public Integrity 12 (1).
    Effective ethics teaching and training must cultivate both the critical thinking skills and the character traits needed to deliberate effectively about ethical issues in personal and professional life. After highlighting some cognitive and motivational obstacles that stand in the way of this task, the article draws on educational research and the author's experience to demonstrate how cooperative learning techniques can be used to overcome them.
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  39.  14
    Henry Moss (2016). Genes, Affect, and Reason: Why Autonomous Robot Intelligence Will Be Nothing Like Human Intelligence. 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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  40.  63
    Douglas Schuler (2010). Community Networks and the Evolution of Civic Intelligence. AI and Society 25 (3):291-307.
    Although the intrinsic physicality of human beings has not changed in millennia, the species has managed to profoundly reconstitute the physical and social world it inhabits. Although the word “profound” is insufficient to describe the vast changes our world has undergone, it is sufficiently neutral to encompass both the opportunities—and the challenges—that our age provides. It is a premise of my work that technology, particularly information and communication technology (ICT), offers spectacular opportunities for humankind to address its collective problems. The (...)
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  41.  74
    Shane Legg & Marcus Hutter (2007). Universal Intelligence: A Definition of Machine Intelligence. Minds and Machines 17 (4):391-444.
    A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of (...) for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines. (shrink)
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  42.  40
    Jacob Joseph, Kevin Berry & Satish P. Deshpande (2009). Impact of Emotional Intelligence and Other Factors on Perception of Ethical Behavior of Peers. Journal of Business Ethics 89 (4):539 - 546.
    This study investigates factors impacting perceptions of ethical conduct of peers of 293 students in four US universities. Self-reported ethical behavior and recognition of emotions in others (a dimension of emotional intelligence) impacted perception of ethical behavior of peers. None of the other dimensions of emotional intelligence were significant. Age, Race, Sex, GPA, or type of major (business versus nonbusiness) did not impact perception of ethical behavior of peers. Implications of the results of the study for business schools (...)
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  43.  57
    Hutan Ashrafian (2015). AIonAI: A Humanitarian Law of Artificial Intelligence and Robotics. Science and Engineering Ethics 21 (1):29-40.
    The enduring progression of artificial intelligence and cybernetics offers an ever-closer possibility of rational and sentient robots. The ethics and morals deriving from this technological prospect have been considered in the philosophy of artificial intelligence, the design of automatons with roboethics and the contemplation of machine ethics through the concept of artificial moral agents. Across these categories, the robotics laws first proposed by Isaac Asimov in the twentieth century remain well-recognised and esteemed due to their specification of preventing (...)
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  44.  20
    Selmer Bringsjord & Naveen Sundar Govindarajulu (2012). Given the Web, What is Intelligence, Really? Metaphilosophy 43 (4):464-479.
    This article argues that existing systems on the Web cannot approach human-level intelligence, as envisioned by Descartes, without being able to achieve genuine problem solving on unseen problems. The article argues that this entails committing to a strong intensional logic. In addition to revising extant arguments in favor of intensional systems, it presents a novel mathematical argument to show why extensional systems can never hope to capture the inherent complexity of natural language. The argument makes its case by focusing (...)
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  45. John T. Sanders (1985). Experience, Memory and Intelligence. 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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  46. Jack Copeland (1993). Artificial Intelligence: A Philosophical Introduction. Wiley-Blackwell.
    Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding. There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence and neuroscience. (...)
     
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  47.  74
    Peter-Paul Verbeek (2009). Ambient Intelligence and Persuasive Technology: The Blurring Boundaries Between Human and Technology. [REVIEW] NanoEthics 3 (3):231-242.
    The currently developing fields of Ambient Intelligence and Persuasive Technology bring about a convergence of information technology and cognitive science. Smart environments that are able to respond intelligently to what we do and that even aim to influence our behaviour challenge the basic frameworks we commonly use for understanding the relations and role divisions between human beings and technological artifacts. After discussing the promises and threats of these technologies, this article develops alternative conceptions of agency, freedom, and responsibility that (...)
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  48.  75
    Selmer Bringsjord (2010). Meeting Floridi's Challenge to Artificial Intelligence From the Knowledge-Game Test for Self-Consciousness. Metaphilosophy 41 (3):292-312.
    Abstract: In the course of seeking an answer to the question "How do you know you are not a zombie?" Floridi (2005) issues an ingenious, philosophically rich challenge to artificial intelligence (AI) in the form of an extremely demanding version of the so-called knowledge game (or "wise-man puzzle," or "muddy-children puzzle")—one that purportedly ensures that those who pass it are self-conscious. In this article, on behalf of (at least the logic-based variety of) AI, I take up the challenge—which is (...)
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  49.  82
    Clancy Blair (2006). How Similar Are Fluid Cognition and General Intelligence? A Developmental Neuroscience Perspective on Fluid Cognition as an Aspect of Human Cognitive Ability. Behavioral and Brain Sciences 29 (2):109-125.
    This target article considers the relation of fluid cognitive functioning to general intelligence. A neurobiological model differentiating working memory/executive function cognitive processes of the prefrontal cortex from aspects of psychometrically defined general intelligence is presented. Work examining the rise in mean intelligence-test performance between normative cohorts, the neuropsychology and neuroscience of cognitive function in typically and atypically developing human populations, and stress, brain development, and corticolimbic connectivity in human and nonhuman animal models is reviewed and found to (...)
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    Robert Strand & R. Edward Freeman (2013). Scandinavian Cooperative Advantage: The Theory and Practice of Stakeholder Engagement in Scandinavia. [REVIEW] Journal of Business Ethics 127 (1):1-21.
    In this article, we first provide evidence that Scandinavian contributions to stakeholder theory over the past 50 years play a much larger role in its development than is presently acknowledged. These contributions include the first publication and description of the term “stakeholder”, the first stakeholder map, and the development of three fundamental tenets of stakeholder theory: jointness of interests, cooperative strategic posture, and rejection of a narrowly economic view of the firm. We then explore the current practices of Scandinavian (...)
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