Results for 'intelligence test'

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  1. 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 (...)
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  2. 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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  3. Post-Turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence.Albert Efimov - 2020 - Lecture Notes in Computer Science 12177.
    This article offers comprehensive criticism of the Turing test and develops quality criteria for new artificial general intelligence (AGI) assessment tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” re-flected the engineering level of his time. In fact, the Turing “imitation game” employed only symbolic communication and ignored the physical world. This paper suggests that by restricting thinking ability to symbolic systems alone Turing unknowingly constructed (...)
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  4. Do Chatbots Dream of Androids? Prospects for the Technological Development of Artificial Intelligence and Robotics.Albert R. Efimov - 2019 - Philosophical Sciences 62 (7):73-95.
    The article discusses the main trends in the development of artificial intelligence systems and robotics (AI&R). The main question that is considered in this context is whether artificial systems are going to become more and more anthropomorphic, both intellectually and physically. In the current article, the author analyzes the current state and prospects of technological development of artificial intelligence and robotics, and also determines the main aspects of the impact of these technologies on society and economy, indicating the (...)
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  5. Turing Test, Chinese Room Argument, Symbol Grounding Problem. Meanings in Artificial Agents (APA 2013).Christophe Menant - 2013 - American Philosophical Association Newsletter on Philosophy and Computers 13 (1):30-34.
    The Turing Test (TT), the Chinese Room Argument (CRA), and the Symbol Grounding Problem (SGP) are about the question “can machines think?” We propose to look at these approaches to Artificial Intelligence (AI) by showing that they all address the possibility for Artificial Agents (AAs) to generate meaningful information (meanings) as we humans do. The initial question about thinking machines is then reformulated into “can AAs generate meanings like humans do?” We correspondingly present the TT, the CRA and (...)
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  6. On the Claim That a Table-Lookup Program Could Pass the Turing Test.Drew McDermott - 2014 - Minds and Machines 24 (2):143-188.
    The claim has often been made that passing the Turing Test would not be sufficient to prove that a computer program was intelligent because a trivial program could do it, namely, the “Humongous-Table (HT) Program”, which simply looks up in a table what to say next. This claim is examined in detail. Three ground rules are argued for: (1) That the HT program must be exhaustive, and not be based on some vaguely imagined set of tricks. (2) That the (...)
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  7.  59
    Minimum Intelligent Signal Test as an Alternative to the Turing Test.Paweł Łupkowski & Patrycja Jurowska - 2019 - Diametros 59:35-47.
    The aim of this paper is to present and discuss the issue of the adequacy of the Minimum Intelligent Signal Test (MIST) as an alternative to the Turing Test. MIST has been proposed by Chris McKinstry as a better alternative to Turing’s original idea. Two of the main claims about MIST are that (1) MIST questions exploit commonsense knowledge and as a result are expected to be easy to answer for human beings and difficult for computer programs; and (...)
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  8. Πενήντα χρόνια τεχνητής νοημοσύνης: γιατί δεν επιτύχαμε ακόμα; [Fifty years of artificial intelligence: Why have we not succeeded yet?].Vincent C. Müller - 2006 - Cogito 4:48-49.
    1 Οι Αρχές - 2 Η δοκιμασία του Turing - 3 Η κλασική τεχνητή νοημοσύνη - 4 Η τεχνητή νοημοσύνη σήμερα - 5 Η τεχνητή νοημοσύνη του μέλλοντος - Με τις τεχνολογίες του παρόντος μάλλον θα δυσκολευτούμε να φτάσουμε στην κατασκευή μηχανών με τεχνητή νοημοσύνη. Κατά την γνώμη μου, θα δούμε άλλες τεχνικές λύσεις με την κλασική τεχνητή νοημοσύνη και μέθοδο «από κάτω προς τα πάνω», αλλά δεν περιμένω να υπάρξει ριζοσπαστική πρόοδος πριν μάθουμε πολλά παραπάνω για τον εγκέφαλό μας. (...)
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  9. Laws of Form and the Force of Function: Variations on the Turing Test.Hajo Greif - 2012 - In Vincent C. Müller & Aladdin Ayesh (eds.), Revisiting Turing and His Test: Comprehensiveness, Qualia, and the Real World. AISB. pp. 60-64.
    This paper commences from the critical observation that the Turing Test (TT) might not be best read as providing a definition or a genuine test of intelligence by proxy of a simulation of conversational behaviour. Firstly, the idea of a machine producing likenesses of this kind served a different purpose in Turing, namely providing a demonstrative simulation to elucidate the force and scope of his computational method, whose primary theoretical import lies within the realm of mathematics rather (...)
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  10. Revisiting Turing and His Test: Comprehensiveness, Qualia, and the Real World.Vincent C. Müller & Aladdin Ayesh (eds.) - 2012 - AISB.
    Proceedings of the papers presented at the Symposium on "Revisiting Turing and his Test: Comprehensiveness, Qualia, and the Real World" at the 2012 AISB and IACAP Symposium that was held in the Turing year 2012, 2–6 July at the University of Birmingham, UK. Ten papers. - http://www.pt-ai.org/turing-test --- Daniel Devatman Hromada: From Taxonomy of Turing Test-Consistent Scenarios Towards Attribution of Legal Status to Meta-modular Artificial Autonomous Agents - Michael Zillich: My Robot is Smarter than Your Robot: On (...)
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  11. El test de Turing: dos mitos, un dogma.Rodrigo González - 2007 - Revista de Filosofía 63:37-53.
    Este artículo analiza el Test de Turing, uno de los métodos más famosos y controvertidos para evaluar la existencia de vida mental en la Filosofía de la Mente, revelando dos mitos filosóficos comúnmente aceptados y criticando su dogma. En primer lugar, se muestra por qué Turing nunca propuso una definición de inteligencia. En segundo lugar, se refuta que el Test de Turing involucre condiciones necesarias o suficientes para la inteligencia. En tercer lugar, teniendo presente el objetivo y el (...)
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  12. Introduction to a Systemic Theory of Meaning - March 2020 Update.Christophe Menant - manuscript
    Information and meanings are present everywhere around us and within ourselves. Specific studies have been implemented in order to link information and meaning (Linguistic, Semiotic, Biosemiotic, Psychology, Psychiatry, Biology, Neurology, Cognition, Artificial Intelligence... ). No general coverage is available for the notion of meaning. We propose to complement this lack by a system approach for meaning genaration. A Meaning Generator System based on constraint satisfaction is presented. It can be used for animals, humans and artificial agents, and makes available (...)
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  13. Measuring Progress in Robotics: Benchmarking and the ‘Measure-Target Confusion’.Vincent C. Müller - 2019 - In Fabio Bonsignorio, John Hallam, Elena Messina & Angel P. Del Pobil (eds.), Metrics of sensory motor coordination and integration in robots and animals. Berlin: Springer. pp. 169-179.
    While it is often said that robotics should aspire to reproducible and measurable results that allow benchmarking, I argue that a focus on benchmarking can be a hindrance for progress in robotics. The reason is what I call the ‘measure-target confusion’, the confusion between a measure of progress and the target of progress. Progress on a benchmark (the measure) is not identical to scientific or technological progress (the target). In the past, several academic disciplines have been led into pursuing only (...)
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  14.  34
    Технологические предпосылки неразличимости человека и его компьютерной имитации.Albert Efimov - 2019 - Искусственные Общества 10.
    In the article, the author analyzes the problems of human-computer communication in the context of artificial intelligence, augmented reality and a Turing methodology for comparing the capabilities of artificial and natural intelligence in a dialogue. It is argued that the tool with which the computer and humans communicate is of no less importance than the computer program with which the dialogue is conducted. As an example of the implementation of such visualization, the project “E.LENA” of a digital television (...)
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  15. The Mind as the Software of the Brain.Ned Block - 1995 - In Daniel N. Osherson, Lila Gleitman, Stephen M. Kosslyn, S. Smith & Saadya Sternberg (eds.), An Invitation to Cognitive Science, Second Edition, Volume 3. Cambridge MA: MIT Press. pp. 377-425.
    In this section, we will start with an influential attempt to define `intelligence', and then we will move to a consideration of how human intelligence is to be investigated on the machine model. The last part of the section will discuss the relation between the mental and the biological.
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  16.  64
    Intelligence Analysis.Nicolae Sfetcu - manuscript
    The analysts are in the field of "knowledge". Intelligence refers to knowledge and the types of problems addressed are knowledge problems. So, we need a concept of work based on knowledge. We need a basic understanding of what we know and how we know, what we do not know, and even what can be known and what is not known. The analysis should provide a useful basis for conceptualizing intelligence functions, of which the most important are "estimation" and (...)
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  17. MICHAEL POLANYI: CAN THE MIND BE REPRESENTED BY A MACHINE?Paul Richard Blum - 2010 - Polanyiana 19 (1-2):35-60.
    In 1949, the Department of Philosophy at the University of Manchester organized a symposium “Mind and Machine” with Michael Polanyi, the mathematicians Alan Turing and Max Newman, the neurologists Geoff rey Jeff erson and J. Z. Young, and others as participants. Th is event is known among Turing scholars, because it laid the seed for Turing’s famous paper on “Computing Machinery and Intelligence”, but it is scarcely documented. Here, the transcript of this event, together with Polanyi’s original statement and (...)
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  18. Turing and the Evaluation of Intelligence.Francesco Bianchini - 2014 - Isonomia: Online Philosophical Journal of the University of Urbino:1-18.
    The article deals with some ideas by Turing concerning the background and the birth of the well-known Turing Test, showing the evolution of the main question proposed by Turing on thinking machine. The notions he used, especially that one of imitation, are not so much exactly defined and shaped, but for this very reason they have had a deep impact in artificial intelligence and cognitive science research from an epistemological point of view. Then, it is suggested that the (...)
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  19.  68
    The Turing Guide.Jack Copeland, Jonathan Bowen, Robin Wilson & Mark Sprevak (eds.) - 2017 - Oxford: Oxford University Press.
    This volume celebrates the various facets of Alan Turing (1912–1954), the British mathematician and computing pioneer, widely considered as the father of computer science. It is aimed at the general reader, with additional notes and references for those who wish to explore the life and work of Turing more deeply. -/- The book is divided into eight parts, covering different aspects of Turing’s life and work. -/- Part I presents various biographical aspects of Turing, some from a personal point of (...)
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  20.  78
    The Representation of Context: Ideas From Artificial Intelligence.James Franklin - 2003 - Law, Probability and Risk 2:191-199.
    To move beyond vague platitudes about the importance of context in legal reasoning or natural language understanding, one must take account of ideas from artificial intelligence on how to represent context formally. Work on topics like prior probabilities, the theory-ladenness of observation, encyclopedic knowledge for disambiguation in language translation and pathology test diagnosis has produced a body of knowledge on how to represent context in artificial intelligence applications.
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  21. A Modal Defence of Strong AI.Steffen Borge - 2007 - In Dermot Moran Stephen Voss (ed.), The Proceedings of the Twenty-First World Congress of Philosophy. The Philosophical Society of Turkey. pp. 127-131.
    John Searle has argued that the aim of strong AI of creating a thinking computer is misguided. Searle’s Chinese Room Argument purports to show that syntax does not suffice for semantics and that computer programs as such must fail to have intrinsic intentionality. But we are not mainly interested in the program itself but rather the implementation of the program in some material. It does not follow by necessity from the fact that computer programs are defined syntactically that the implementation (...)
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  22. Vindication of the Rights of Machine.Kris Rhodes - manuscript
    In this paper, I argue that certain Machines can have rights independently of whether they are sentient, or conscious, or whatever you might call it.
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  23.  66
    Technological Prerequisites for Indistinguishability of a Person and His/Her Computer Replica.Albert Efimov - 2019 - Artificial Societies 4.
    Some people wrongly believe that A. Turing’s works that underlie all modern computer science never discussed “physical” robots. This is not so, since Turing did speak about such machines, though making a reservation that this discussion was still premature. In particular, in his 1948 report [8], he suggested that a physical intelligent machine equipped with motors, cameras and loudspeakers, when wandering through the fields of England, would present “the danger to the ordinary citizen would be serious.” [8, ]. Due to (...)
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  24. Analogy, Mind, and Life.Vitor Manuel Dinis Pereira - 2015 - In Quoc Nam Tran & Hamid Arabnia (eds.), Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology. Elsevier. pp. 377–388.
    I'll show that the kind of analogy between life and information – that seems to be central to the effect that artificial mind may represents an expected advance in the life evolution in Universe – is like the design argument and that if the design argument is unfounded and invalid, the argument to the effect that artificial mind may represents an expected advance in the life evolution in Universe is also unfounded and invalid. However, if we are prepared to admit (...)
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  25.  74
    Review of The Emotion Machine by Marvin Minsky (2007).Michael Starks - 2016 - In Suicidal Utopian Delusions in the 21st Century: Philosophy, Human Nature and the Collapse of Civilization-- Articles and Reviews 2006-2017 2nd Edition Feb 2018. Michael Starks. pp. 627.
    Dullest book by a major scientist I have ever read. I suppose if you know almost nothing about cognition or AI research you might find this book useful. For anyone else it is a horrific bore. There are hundreds of books in cog sci, robotics, AI, evolutionary psychology and philosophy offering far more info and insight on cognition than this one. Minsky is a top rate senior scientist but it barely shows here. He has alot of good references but they (...)
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  26. Ontology-Based Fusion of Sensor Data and Natural Language.Erik Thomsen & Barry Smith - 2018 - Applied Ontology 13 (4):295-333.
    We describe a prototype ontology-driven information system (ODIS) that exploits what we call Portion of Reality (POR) representations. The system takes both sensor data and natural language text as inputs and composes on this basis logically structured POR assertions. The goal of our prototype is to represent both natural language and sensor data within a single framework that is able to support both axiomatic reasoning and computation. In addition, the framework should be capable of discovering and representing new kinds of (...)
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  27. Intelligence Via Ultrafilters: Structural Properties of Some Intelligence Comparators of Deterministic Legg-Hutter Agents.Samuel Alexander - 2019 - Journal of Artificial General Intelligence 10 (1):24-45.
    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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  28. 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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  29. A Novel Approach for Identifying a Human-Like Self-Conscious Behavior.Gianpiero Negri - manuscript
    In this paper a possible extension of Turing test [1] will be presented, which is intended to overcome the limits highlighted by several researchers and scientists in the last seventy years. The main problem related to the execution in Turing test is substantially dealing with the trouble in identification of a human-like intelligence based on a pure evaluation of external behavior of a machine. In this work first of all a description of classical Turing test will (...)
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  30. Ethics of Artificial Intelligence.Vincent C. Müller - forthcoming - In Anthony Elliott (ed.), The Routledge social science handbook of AI. London: Routledge. pp. 1-20.
    Artificial intelligence (AI) is a digital technology that will be of major importance for the development of humanity in the near future. AI has raised fundamental questions about what we should do with such systems, what the systems themselves should do, what risks they involve and how we can control these. - After the background to the field (1), this article introduces the main debates (2), first on ethical issues that arise with AI systems as objects, i.e. tools made (...)
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  31. 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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  32. 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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  33. Classification of Global Catastrophic Risks Connected with Artificial Intelligence.Alexey Turchin & David Denkenberger - 2020 - AI and Society 35 (1):147-163.
    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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  34. 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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  35. 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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  36. 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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  37. Shortcuts to Artificial Intelligence.Nello Cristianini - forthcoming - In Marcello Pelillo & Teresa Scantamburlo (eds.), Machines We Trust. MIT Press.
    The current paradigm of Artificial Intelligence emerged as the result of a series of cultural innovations, some technical and some social. Among them are apparently small design decisions, that led to a subtle reframing of the field’s original goals, and are by now accepted as standard. They correspond to technical shortcuts, aimed at bypassing problems that were otherwise too complicated or too expensive to solve, while still delivering a viable version of AI. Far from being a series of separate (...)
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  38. 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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  39. 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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  40. Il test della falsa credenza.Marco Fenici - 2013 - Analytical and Philosophical Explanation 8:1-56.
    La ricerca empirica nelle scienze cognitive può essere di supporto all’indagine filosofica sullo statuto ontologico e epistemologico dei concetti mentali, ed in particolare del concetto di credenza. Da oltre trent’anni gli psicologi utilizzano il test della falsa credenza per valutare la capacità dei bambini di attribuire stati mentali a se stessi e a agli altri. Tuttavia non è stato ancora pienamente compreso né quali requisiti cognitivi siano necessari per passare il test né quale sia il loro sviluppo. In (...)
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  41. 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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  42. Komputer, Kecerdasan Buatan dan Internet: Filsafat Hubert L. Dreyfus tentang Produk Industri 3.0 dan Industri 4.0 (Computer, Artificial Intelligence and Internet: Dreyfus’s Philosophy on the Product of 3.0 and 4.0 Industries).Zainul Maarif - 2019 - Prosiding Paramadina Research Day.
    The content of this paper is an elaboration of Hubert L. Dreyfus’s philosophical critique of Artificial Intelligence (AI), computers and the internet. Hubert L. Dreyfus (1929-2017) is Ua SA philosopher and alumni of Harvard University who teach at the Massachusetts Institute of Technology (MIT) and University of California, Berkeley. He is a phenomenological philosopher who criticize computer researchers and the artificial intelligence community. In 1965, Dreyfus wrote an article for Rand Corporation titled “Alchemy and Artificial Intelligence” which (...)
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  43. 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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  44.  15
    What Should We Do About Sheep? The Role of Intelligence in Welfare Considerations.Heather Browning - 2019 - Animal Sentience 4 (25):23.
    Marino & Merskin (2019) demonstrate that sheep are more cognitively complex than typically thought. We should be cautious in interpreting the implications of these results for welfare considerations to avoid perpetuating mistaken beliefs about the moral value of intelligence as opposed to sentience. There are, however, still important ways in which this work can help improve sheeps’ lives.
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  45. Coercive Paternalism and the Intelligence Continuum.Nathan Cofnas - 2020 - Behavioural Public Policy 4 (1):88-107.
    Thaler and Sunstein advocate 'libertarian paternalism'. A libertarian paternalist changes the conditions under which people act so that their cognitive biases lead them to choose what is best for themselves. Although libertarian paternalism manipulates people, Thaler and Sunstein say that it respects their autonomy by preserving the possibility of choice. Conly argues that libertarian paternalism does not go far enough, since there is no compelling reason why we should allow people the opportunity to choose to bring disaster upon themselves if (...)
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  46. The Future of Human-Artificial Intelligence Nexus and its Environmental Costs.Petr Spelda & Vit Stritecky - forthcoming - Futures.
    The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al., 2019 in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable inductive inferences. (...)
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  47. 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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  48. Measuring the Intelligence of an Idealized Mechanical Knowing Agent.Samuel Alexander - forthcoming - Lecture Notes in Computer Science.
    We define a notion of the intelligence level of an idealized mechanical knowing agent. This is motivated by efforts within artificial intelligence research to define real-number intelligence levels of compli- cated intelligent systems. Our agents are more idealized, which allows us to define a much simpler measure of intelligence level for them. In short, we define the intelligence level of a mechanical knowing agent to be the supremum of the computable ordinals that have codes the (...)
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  49. 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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  50. Measuring Moral Reasoning Using Moral Dilemmas: Evaluating Reliability, Validity, and Differential Item Functioning of the Behavioral Defining Issues Test (bDIT).Youn-Jeng Choi, Hyemin Han, Kelsie J. Dawson, Stephen J. Thoma & Andrea L. Glenn - 2019 - European Journal of Developmental Psychology 16 (5):622-631.
    We evaluated the reliability, validity, and differential item functioning (DIF) of a shorter version of the Defining Issues Test-1 (DIT-1), the behavioral DIT (bDIT), measuring the development of moral reasoning. 353 college students (81 males, 271 females, 1 not reported; age M = 18.64 years, SD = 1.20 years) who were taking introductory psychology classes at a public University in a suburb area in the Southern United States participated in the present study. First, we examined the reliability of the (...)
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