Results for 'Plant intelligence'

998 found
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  1.  44
    Healing with Plant Intelligence: A Report From Ayahuasca.Richard Doyle - 2012 - Anthropology of Consciousness 23 (1):28-43.
    Numerous and diverse reports indicate the efficacy of shamanic plant adjuncts (e.g., iboga, ayahuasca, psilocybin) for the care and treatment of addiction, post-traumatic stress disorder, cancer, cluster headaches, and depression. This article reports on a first-person healing of lifelong asthma and atopic dermatitis in the shamanic context of the contemporary Peruvian Amazon and the sometimes digital ontology of online communities. The article suggests that emerging language, concepts, and data drawn from the sciences of plant signaling and behavior regarding (...)
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  2. Unraveling Trie Enigma of Human Intelligence: Evolutionary Psychology and the Multimodular Mind.Of Intelligence - 2002 - In Robert J. Sternberg & J. Kaufman (eds.), The Evolution of Intelligence. Lawrence Erlbaum. pp. 145.
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  3.  11
    Brilliant Green: The Surprising History and Science of Plant Intelligence by Stefano Mancuso and Alessandra Viola.Franklin G. Miller - 2014 - Perspectives in Biology and Medicine 57 (4):569-574.
  4.  36
    The Philosophy of Plant Neurobiology: A Manifesto.Paco Calvo - 2016 - Synthese 193 (5).
    Plant neurobiology’ has emerged in recent years as a multidisciplinary endeavor carried out mainly by steady collaboration within the plant sciences. The field proposes a particular approach to the study of plant intelligence by putting forward an integrated view of plant signaling and adaptive behavior. Its objective is to account for the way plants perceive and act in a purposeful manner. But it is not only the plant sciences that constitute plant neurobiology. Resources (...)
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  5.  52
    The Philosophy of Plant Neurobiology: A Manifesto.Paco Calvo - 2016 - Synthese 193 (5):1323-1343.
    Plant neurobiology’ has emerged in recent years as a multidisciplinary endeavor carried out mainly by steady collaboration within the plant sciences. The field proposes a particular approach to the study of plant intelligence by putting forward an integrated view of plant signaling and adaptive behavior. Its objective is to account for the way plants perceive and act in a purposeful manner. But it is not only the plant sciences that constitute plant neurobiology. Resources (...)
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  6.  29
    Pictures, Plants, and Propositions.Alex Morgan - 2019 - Minds and Machines 29 (2):309-329.
    Philosophers have traditionally held that propositions mark the domain of rational thought and inference. Many philosophers have held that only conceptually sophisticated creatures like us could have propositional attitudes. But in recent decades, philosophers have adopted increasingly liberal views of propositional attitudes that encompass the mental states of various non-human animals. These views now sit alongside more traditional views within the philosophical mainstream. In this paper I argue that liberalized views of propositional attitudes are so liberal that they encompass states (...)
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  7.  50
    Plant Autonomy and Human-Plant Ethics.Matthew Hall - 2009 - Environmental Ethics 31 (2):169-181.
    It has recently been asserted that legislative moves to consider plants as ethical subjects are philosophically foolish because plants lack autonomy. While by no means the sole basis or driving criterion for moral behavior, it is possible to directly challenge skeptical attitudes by constructing a human-plant ethics centered on fundamental notions of autonomy. Autonomous beings are agents who rule themselves, principally for their own purposes. A considerable body of evidence in the plant sciences is increasingly recognizing the capacity (...)
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  8.  13
    Passive Flora? Reconsidering Nature's Agency Through Human-Plant Studies.John Ryan - unknown
    Plants have been—and, for reasons of human sustenance and creative inspiration, will continue to be—centrally important to societies globally. Yet, plants—including herbs, shrubs, and trees—are commonly characterized in Western thought as passive, sessile, and silent automatons lacking a brain, as accessories or backdrops to human affairs. Paradoxically, the qualities considered absent in plants are those employed by biologists to argue for intelligence in animals. Yet an emerging body of research in the sciences and humanities challenges animal-centred biases in determining (...)
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  9.  2
    History and Epistemology of Plant Behaviour: A Pluralistic View?Quentin Hiernaux - forthcoming - Synthese:1-26.
    Some biologists now argue in favour of a pluralistic approach to plant activities, understandable both from the classical perspective of physiological mechanisms and that of the biology of behaviour involving choices and decisions in relation to the environment. However, some do not hesitate to go further, such as plant “neurobiologists” or philosophers who today defend an intelligence, a mind or even a plant consciousness in a renewed perspective of these terms. To what extent can we then (...)
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  10.  6
    Collaborative Dynamic Acquisition of Simulation Models for Micro Chemical Plant.Naoki Kimura, Hideyuki Matsumoto & Chiaki Kuroda - 2006 - Transactions of the Japanese Society for Artificial Intelligence 21:36-44.
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  11. Intelligence Via Ultrafilters: Structural Properties of Some Intelligence Comparators of Deterministic Legg-Hutter Agents.Samuel Alexander - forthcoming - Journal of Artificial General Intelligence.
    Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg- Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose (...)
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  12. 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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  13.  58
    Cognition in Plants. Calvo, P. & Keijzer, F. A. - unknown
    To what extent can plants be considered cognitive from the perspective of embodied cognition? Cognition is interpreted very broadly within embodied cognition, and the current evidence for plant intelligence might find an important theoretical background here. However, embodied cognition does stress the presence of animal-like perception-action coupling as a key feature for cognitive systems to arise. In this paper, we discuss whether, or to what extent, plants may qualify as cognitive systems, given this criterion.
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  14. 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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  15. 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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  16.  89
    Legg-Hutter Universal Intelligence Implies Classical Music is Better Than Pop Music for Intellectual Training.Samuel Alexander - 2019 - The Reasoner 13 (11):71-72.
    In their thought-provoking paper, Legg and Hutter consider a certain abstrac- tion of an intelligent agent, and define a universal intelligence measure, which assigns every such agent a numerical intelligence rating. We will briefly summarize Legg and Hutter’s paper, and then give a tongue-in-cheek argument that if one’s goal is to become more intelligent by cultivating music appreciation, then it is bet- ter to use classical music (such as Bach, Mozart, and Beethoven) than to use more recent pop (...)
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  17. 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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  18. 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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  19. 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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  20. 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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  21.  48
    The Plant Ontology Facilitates Comparisons of Plant Development Stages Across Species.Ramona Lynn Walls, Laurel Cooper, Justin Lee Elser, Maria Alejandra Gandolfo, Christopher J. Mungall, Barry Smith, Dennis William Stevenson & Pankaj Jaiswal - 2019 - Frontiers in Plant Science 10.
    The Plant Ontology (PO) is a community resource consisting of standardized terms, definitions, and logical relations describing plant structures and development stages, augmented by a large database of annotations from genomic and phenomic studies. This paper describes the structure of the ontology and the design principles we used in constructing PO terms for plant development stages. It also provides details of the methodology and rationale behind our revision and expansion of the PO to cover development stages for (...)
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  22.  9
    ODNI as an Analytic Ombudsman: Is Intelligence Community Directive 203 Up to the Task?Alexandru Marcoci, Ans Vercammen & Mark Burgman - forthcoming - Intelligence and National Security.
    In the wake of 9/11 and the assessment of Iraq's WMD, several inquiries placed the blame primarily on the Intelligence Community. Part of the reform that followed was a codification of analytic tradecraft standards into Intelligence Community Directive (ICD) 203 and the appointment of an analytic ombudsman in the newly created Office of the Director of National Intelligence charged with monitoring the quality of analytic products from across the intelligence community. In this paper we identify three (...)
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  23. 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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  24. 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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  25.  62
    The Facets of Artificial Intelligence: A Framework to Track the Evolution of AI.Fernando Martínez-Plumed, Bao Sheng Loe, Peter Flach, Sean O. O. HEigeartaigh, Karina Vold & José Hernández-Orallo - 2018 - In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence Evolution of the contours of AI. pp. 5180-5187.
    We present nine facets for the analysis of the past and future evolution of AI. Each facet has also a set of edges that can summarise different trends and contours in AI. With them, we first conduct a quantitative analysis using the information from two decades of AAAI/IJCAI conferences and around 50 years of documents from AI topics, an official database from the AAAI, illustrated by several plots. We then perform a qualitative analysis using the facets and edges, locating AI (...)
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  26. 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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  27.  70
    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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  28. On the Artificiality of Artificial Intelligence.Hans F. M. Crombag - 1993 - 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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  29.  18
    Artificial Intelligence and the Body: Dreyfus, Bickhard, and the Future of AI.Daniel Susser - 2013 - In Vincent C. Müller (ed.), Philosophy and Theory of Artificial Intelligence. Berlin: Springer. pp. 277-287.
    For those who find Dreyfus’s critique of AI compelling, the prospects for producing true artificial human intelligence are bleak. An important question thus becomes, what are the prospects for producing artificial non-human intelligence? Applying Dreyfus’s work to this question is difficult, however, because his work is so thoroughly human-centered. Granting Dreyfus that the body is fundamental to intelligence, how are we to conceive of non-human bodies? In this paper, I argue that bringing Dreyfus’s work into conversation with (...)
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  30.  58
    Three Philosophical Lessons for the Analysis of Criminal and Military Intelligence.Christopher Mole - 2012 - 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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  31. Artificial Intelligence: Its Scope and Limits.James H. Fetzer - 1990 - Kluwer Academic Publishers.
    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 ..
  32. Intelligence Vs. Wisdom: The Love of Money, Machiavellianism, and Unethical Behavior Across College Major and Gender.Thomas Li-Ping Tang & Yuh-Jia Chen - 2008 - 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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  33. The Pragmatic Turn in Explainable Artificial Intelligence (XAI).Andrés Páez - 2019 - Minds and Machines (3):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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  34.  97
    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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  35. 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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  36. Intelligence Without Representation.Rodney Brooks - 1991 - Artificial Intelligence 47 (1--3):139-159.
    Artificial intelligence research has foundered on the issue of representation. When intelligence is approached in an incremental manner, with strict reliance on interfacing to the real world through perception and action, reliance on representation disappears. In this paper we outline our approach to incrementally building complete intelligent Creatures. The fundamental decomposition of the intelligent system is not into independent information processing units which must interface with each other via representations. Instead, the intelligent system is decomposed into independent and (...)
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  37.  44
    Money is Power: Monetary Intelligence—Love of Money and Temptation of Materialism Among Czech University Students. [REVIEW]Soňa Lemrová, Eva Reiterová, Renáta Fatěnová, Karel Lemr & Thomas Li-Ping Tang - 2014 - Journal of Business Ethics 125 (2).
    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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  38.  35
    Falling or Not Falling Into Temptation? Multiple Faces of Temptation, Monetary Intelligence, and Unethical Intentions Across Gender.Thomas Li-Ping Tang & Toto Sutarso - 2013 - 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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  39.  31
    Temptation, Monetary Intelligence (Love of Money), and Environmental Context on Unethical Intentions and Cheating.Jingqiu Chen, Thomas Li-Ping Tang & Ningyu Tang - 2014 - 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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  40.  51
    Witness Testimony Evidence: Argumentation, Artificial Intelligence, and Law.Douglas N. Walton - 2008 - 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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  41. The Planteome Database: An Integrated Resource for Reference Ontologies, Plant Genomics and Phenomics.Laurel Cooper, Austin Meier, Marie-Angélique Laporte, Justin L. Elser, Chris Mungall, Brandon T. Sinn, Dario Cavaliere, Seth Carbon, Nathan A. Dunn, Barry Smith, Botong Qu, Justin Preece, Eugene Zhang, Sinisa Todorovic, Georgios Gkoutos, John H. Doonan, Dennis W. Stevenson, Elizabeth Arnaud & Pankaj Jaiswal - 2018 - Nucleic Acids Research 46 (D1):D1168–D1180.
    The Planteome project provides a suite of reference and species-specific ontologies for plants and annotations to genes and phenotypes. Ontologies serve as common standards for semantic integration of a large and growing corpus of plant genomics, phenomics and genetics data. The reference ontologies include the Plant Ontology, Plant Trait Ontology, and the Plant Experimental Conditions Ontology developed by the Planteome project, along with the Gene Ontology, Chemical Entities of Biological Interest, Phenotype and Attribute Ontology, and others. (...)
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  42. 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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  43. Impact of Emotional Intelligence, Ethical Climate, and Behavior of Peers on Ethical Behavior of Nurses.Satish P. Deshpande & Jacob Joseph - 2009 - 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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  44. The Philosophy of Artificial Intelligence.Margaret A. Boden (ed.) - 1990 - 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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  45. 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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  46. Views Into the Chinese Room: New Essays on Searle and Artificial Intelligence.John M. Preston & John Mark Bishop (eds.) - 2002 - Oxford University Press.
  47. Freedom and Privacy in Ambient Intelligence.Philip Brey - 2005 - 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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  48. 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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  49.  11
    The Limits of Machine Intelligence.Henry Shevlin, Karina Vold, Matthew Crosby & Marta Halina - 2019 - EMBO Reports 49177 (20).
    Despite there being little consensus on what intelligence is or how to measure it, the media and the public have become increasingly preoccupied with the concept owing to recent accomplishments in machine learning and research on artificial intelligence (AI). Governments and corporations are investing billions of dollars to fund researchers who are keen to produce an ever‐expanding range of artificial intelligent systems. More than 30 countries have announced such research initiatives over the past 3 years 1. For example, (...)
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  50. Universal Intelligence: A Definition of Machine Intelligence.Shane Legg & Marcus Hutter - 2007 - 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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