Results for 'AI'

999 found
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  1. Ai Silin Lun Wen Xuan.Silin Ai - 2011 - Zhonghua Shu Ju.
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  2. Logics in Ai European Workshop Jelia '92, Berlin, Germany, September 7-10, 1992 : Proceedings'.David A. Pearce, Gerd Wagner & European Workshop on Logics in Ai - 1992
     
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  3. Transparent, Explainable, and Accountable AI for Robotics.Sandra Wachter, Brent Mittelstadt & Luciano Floridi - 2017 - Science (Robotics) 2 (6):eaan6080.
    To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems.
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  4.  15
    Classical AI Linguistic Understanding and the Insoluble Cartesian Problem.Rodrigo González - 2019 - AI and Society.
    This paper examines an insoluble Cartesian problem for classical AI, namely, how linguistic understanding involves knowledge and awareness of us meaning, a cognitive process that is (...)irreducible to algorithms. As analyzed, Descartesview about reason and intelligence has paradoxically encouraged certain classical AI researchers to suppose that linguistic understanding suffices for machine intelligence. Several advocates of the Turing Test, for example, assume that linguistic understanding only comprises computational processes which can be recursively decomposed into algorithmic mechanisms. Against this background, in the first section, I explain Descartesview about language and mind. To show that Turing bites the bullet with his imitation game, in the second section I analyze this method to assess intelligence. Then, in the third section, I elaborate on Schank and AbelsonsScript Applier Mechanism (SAM, hereby), which supposedly casts doubt on Descartesdenial that machines can think. Finally, in the fourth section, I explore a challenge that any algorithmic decomposition of linguistic understanding faces. This challenge, I argue, is the core of the Cartesian problem: knowledge and awareness of meaning require a first-person viewpoint which is irreducible to the decomposition of algorithmic mechanisms. (shrink)
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  5.  31
    Pathologies of AI: Responsible Use of Artificial Intelligence in Professional Work[REVIEW]Ronald Stamper - 1988 - AI and Society 2 (1):3-16.
    Although the AI paradigm is useful for building knowledge-based systems for the applied natural sciences, there are dangers when it is extended into the domains of (...)business, law and other social systems. It is misleading to treat knowledge as a commodity that can be separated from the context in which it is regularly used. Especially when it relates to social behaviour, knowledge should be treated as socially constructed, interpreted and maintained through its practical use in context. The meanings of terms in a knowledge-base are assumed to be references to an objective reality whereas they are instruments for expressing values and exercising power. Expert systems that are not perspicuous to the expert community will lose their meanings and cease to contain genuine knowledge, as they will be divorced from the social processes essential for the maintenance of both meaning and knowledge. Perspicuity is usually sacrificed when knowledge is represented in a formalism, with the result that the original problem is compounded with a second problem of penetrating the representation language. Formalisms that make business and legal problems easier to understand are one essential research goal, not only in the quest for intelligent machines to replace intelligent human beings, but also in the wiser quest for computers to support collaborative work and other forms of social problem solving. (shrink)
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  6. AI Winter.Steven Umbrello - forthcoming - In Michael Klein & Philip Frana (eds.), Encyclopedia of Artificial Intelligence: The Past, Present, and Future of AI. Santa Barbara, USA: ABC-CLIO.
    Coined in 1984 at the American Association of Artificial intelligence (now the Association for the Advancement of Artificial Intelligence or AAAI), the various boom and bust periods (...)
     
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  7.  31
    The AI Elephant.Liu Feng - 1989 - AI and Society 3 (4):336-345.
    The paper presents a Chinese philosophical point of view of AI, and presents a novel system of the AI machine. There are two basic relations or contradictions (...)
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  8.  33
    From AI to Cybernetics.Keizo Sato - 1991 - AI and Society 5 (2):155-161.
    Well-known critics of AI such as Hubert Dreyfus and Michael Polanyi tend to confuse cybernetics with AI. Such a confusion is quite misleading and should not (...)be overlooked. In the first place, cybernetics is not vulnerable to criticism of AI as cognitivistic and behaviouristic. In the second place, AI researchers are recommended to consider the cybernetics approach as a way of overcoming the limitations of cognitivism and behaviourism. (shrink)
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  9.  32
    AI in Medicine: A Japanese Perspective[REVIEW]Dr Toshiyuki Furukawa - 1990 - AI and Society 4 (3):196-213.
    This article is concerned with the history and current state of research activities into medical expert systems (MES) in Japan. A brief review of expert systems' work (...)
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  10.  62
    The Intelligence Left in AI.Denis L. Baggi - 2000 - AI and Society 14 (3-4):348-378.
    In its forty years of existence, Artificial Intelligence has suffered both from the exaggerated claims of those who saw it as the definitive solution of an ancestral (...)
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  11. Making AI Meaningful Again.Jobst Landgrebe & Barry Smith - 2019 - Synthese:arXiv:1901.02918v1.
    Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when (...)
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  12. Why AI Doomsayers Are Like Sceptical Theists and Why It Matters.John Danaher - 2015 - Minds and Machines 25 (3):231-246.
    An advanced artificial intelligence could pose a significant existential risk to humanity. Several research institutes have been set-up to address those risks. And there is an (...)increasing number of academic publications analysing and evaluating their seriousness. Nick Bostroms superintelligence: paths, dangers, strategies represents the apotheosis of this trend. In this article, I argue that in defending the credibility of AI risk, Bostrom makes an epistemic move that is analogous to one made by so-called sceptical theists in the debate about the existence of God. And while this analogy is interesting in its own right, what is more interesting are its potential implications. It has been repeatedly argued that sceptical theism has devastating effects on our beliefs and practices. Could it be that AI-doomsaying has similar effects? I argue that it could. Specifically, and somewhat paradoxically, I argue that it could amount to either a reductio of the doomsayers position, or an important and additional reason to join their cause. I use this paradox to suggest that the modal standards for argument in the superintelligence debate need to be addressed. (shrink)
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  13. New Developments in the Philosophy of AI.Vincent Müller - 2016 - In Fundamental Issues of Artificial Intelligence. Springer.
    The philosophy of AI has seen some changes, in particular: 1) AI moves away from cognitive science, and 2) the long term risks of AI now appear (...)
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  14. Friendly Superintelligent AI: All You Need is Love.Michael Prinzing - 2017 - In Vincent Müller (ed.), The Philosophy & Theory of Artificial Intelligence. Berlin: Springer. pp. 288-301.
    There is a non-trivial chance that sometime in the (perhaps somewhat distant) future, someone will build an artificial general intelligence that will surpass human-level cognitive proficiency (...) and go on to become "superintelligent", vastly outperforming humans. The advent of superintelligent AI has great potential, for good or ill. It is therefore imperative that we find a way to ensure-long before one arrives-that any superintelligence we build will consistently act in ways congenial to our interests. This is a very difficult challenge in part because most of the final goals we could give an AI admit of so-called "perverse instantiations". I propose a novel solution to this puzzle: instruct the AI to love humanity. The proposal is compared with Yudkowsky's Coherent Extrapolated Volition, and Bostrom's Moral Modeling proposals. (shrink)
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  15.  34
    Feminist AI: Can We Expect Our AI Systems to Become Feminist?Galit Wellner & Tiran Rothman - forthcoming - Philosophy and Technology:1-15.
    The rise of AI-based systems has been accompanied by the belief that these systems are impartial and do not suffer from the biases that humans and (...)older technologies express. It becomes evident, however, that gender and racial biases exist in some AI algorithms. The question is where the bias is rootedin the training dataset or in the algorithm? Is it a linguistic issue or a broader sociological current? Works in feminist philosophy of technology and behavioral economics reveal the gender bias in AI technologies as a multi-faceted phenomenon, and the linguistic explanation as too narrow. The next step moves from the linguistic aspects to the relational ones, with postphenomenology. One of the analytical tools of this theory is theI-technology-worldformula that models our relations with technologies, and through themwith the world. Realizing that AI technologies give rise to new types of relations in which the technology has anenhanced technological intentionality”, a new formula is suggested: “I-algorithm-dataset.” In the third part of the article, four types of solutions to the gender bias in AI are reviewed: ignoring any reference to gender, revealing the considerations that led the algorithm to decide, designing algorithms that are not biased, or lastly, involving humans in the process. In order to avoid gender bias, we can recall a feminist basic understandingvisibility matters. Users and developers should be aware of the possibility of gender and racial biases, and try to avoid them, bypass them, or exterminates them altogether. (shrink)
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  16.  28
    A Tale of Two Deficits: Causality and Care in Medical AI.Melvin Chen - forthcoming - Philosophy and Technology.
    In this paper, two central questions will be addressed: ought we to implement medical AI technology in the medical domain? If yes, how ought we to implement (...)
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  17. Philosophy and Theory of Artificial Intelligence, 34 October (Report on PT-AI 2011).Vincent C. Müller - 2011 - The Reasoner 5 (11):192-193.
    Report for "The Reasoner" on the conference "Philosophy and Theory of Artificial Intelligence", 3 & 4 October 2011, Thessaloniki, Anatolia College/ACT, http://www.pt-ai.org (...). --- Organization: Vincent C. Müller, Professor of Philosophy at ACT & James Martin Fellow, Oxford http://www.sophia.de --- Sponsors: EUCogII, Oxford-FutureTech, AAAI, ACM-SIGART, IACAP, ECCAI. (shrink)
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  18. Hubert L. Dreyfuss Critique of Classical AI and its Rationalist Assumptions.Setargew Kenaw - 2008 - Minds and Machines 18 (2):227-238.
    This paper deals with the rationalist assumptions behind researches of artificial intelligence (AI) on the basis of Hubert Dreyfuss critique. Dreyfus is a leading American philosopher (...)known for his rigorous critique on the underlying assumptions of the field of artificial intelligence. Artificial intelligence specialists, especially those whose view is commonly dubbed asclassical AI,” assume that creating a thinking machine like the human brain is not a too far away project because they believe that human intelligence works on the basis of formalized rules of logic. In contradistinction to classical AI specialists, Dreyfus contends that it is impossible to create intelligent computer programs analogous to the human brain because the workings of human intelligence is entirely different from that of computing machines. For Dreyfus, the human mind functions intuitively and not formally. Following Dreyfus, this paper aims to pinpointing the major flaws classical AI suffers from. The author of this paper believes that pinpointing these flaws would inform inquiries on and about artificial intelligence. Over and beyond this, this paper contributes something indisputably original. It strongly argues that classical AI research programs have, though inadvertently, falsified an entire epistemological enterprise of the rationalists not in theory as philosophers do but in practice. When AI workers were trying hard in order to produce a machine that can think like human minds, they have in a way been testingand testing it up to the last pointthe rationalist assumption that the workings of the human mind depend on logical rules. Result: No computers actually function like the human mind. Reason: the human mind does not depend on the formal or logical rules ascribed to computers. Thus, symbolic AI research has falsified the rationalist assumption thatthe human mind reaches certainty by functioning formallyby virtue of its failure to create a thinking machine. (shrink)
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  19. Theory and Philosophy of AI (Minds and Machines, 22/2 - Special Volume).Vincent C. Müller (ed.) - 2012 - Springer.
    Invited papers from PT-AI 2011. - Vincent C. Müller: Introduction: Theory and Philosophy of Artificial Intelligence - Nick Bostrom: The Superintelligent Will: Motivation and Instrumental Rationality in Advanced (...) Artificial Agents - Hubert L. Dreyfus: A History of First Step Fallacies - Antoni Gomila, David Travieso and Lorena Lobo: Wherein is Human Cognition Systematic - J. Kevin O'Regan: How to Build a Robot that Is Conscious and Feels - Oron Shagrir: Computation, Implementation, Cognition. (shrink)
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  20.  21
    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 Dreyfuss 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 Dreyfuss 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 Dreyfuss work into conversation with the work of Mark Bickhard offers a way of answering this question, and I try to suggest what doing so means for AI research. (shrink)
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  21.  37
    AI and the Conquest of Complexity in Law.L. Wolfgang Bibel - 2004 - Artificial Intelligence and Law 12 (3):159-180.
    The paper identifies some of the problems with legal systems and outlines the potential of AI technology for overcoming them. For expository purposes, this outline is based (...)
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  22. Could a Created Being Ever Be Creative? Some Philosophical Remarks on Creativity and AI Development.Y. J. Erden - 2010 - Minds and Machines 20 (3):349-362.
    Creativity has a special role in enabling humans to develop beyond the fulfilment of simple primary functions. This factor is significant for Artificial Intelligence (AI) developers who (...)
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  23. Comments onThe Replication of the Hard Problem of Consciousness in AI and Bio-AI”.Blake H. Dournaee - 2010 - Minds and Machines 20 (2):303-309.
    In their joint paper entitled The Replication of the Hard Problem of Consciousness in AI and BIO-AI (Boltuc et al. Replication of the hard problem of (...)conscious in AI and Bio- AI: An early conceptual framework 2008), Nicholas and Piotr Boltuc suggest that machines could be equipped with phenomenal consciousness, which is subjective consciousness that satisfies Chalmers hard problem (We will abbreviate the hard problem of consciousness as H-consciousness ). The claim is that if we knew the inner workings of phenomenal consciousness and could understand itsprecise operation, we could instantiate such consciousness in a machine. This claim, called the extra-strong AI thesis, is an important claim because if true it would demystify the privileged access problem of first-person consciousness and cast it as an empirical problem of science and not a fundamental question of philosophy. A core assumption of the extra-strong AI thesis is that there is no logical argument that precludes the implementation of H-consciousness in an organic or in-organic machine provided we understand its algorithm. Another way of framing this conclusion is that there is nothing special about H-consciousness as compared to any other process. That is, in the same way that we do not preclude a machine from implementing photosynthesis, we also do not preclude a machine from implementing H-consciousness. While one may be more difficult in practice, it is a problem of science and engineering, and no longer a philosophical question. I propose that Boltucs conclusion, while plausible and convincing, comes at a very high price; the argument given for his conclusion does not exclude any conceivable process from machine implementation. In short, if we make some assumptions about the equivalence of a rough notion of algorithm and then tie this to human understanding, all logical preconditions vanish and the argument grants that any process can be implemented in a machine. The purpose of this paper is to comment on the argument for his conclusion and offer additional properties of H-consciousness that can be used to make the conclusion falsifiable through scientific investigation rather than relying on the limits of human understanding. (shrink)
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  24.  94
    Did Searle Attack Strong Strong AI or Weak Strong AI?Aaron Sloman - 1986 - In Artificial Intelligence and its Applications. Chichester.
    John Searle's attack on the Strong AI thesis, and the published replies, are all based on a failure to distinguish two interpretations of that thesis, a (...)strong one, which claims that the mere occurrence of certain process patterns will suffice for the occurrence of mental states, and a weak one which requires that the processes be produced in the right sort of way. Searle attacks strong strong AI, while most of his opponents defend weak strong AI. This paper explores some of Searle's concepts and shows that there are interestingly different versions of the 'Strong AI' thesis, connected with different kinds of reliability of mechanisms and programs. (shrink)
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  25.  7
    Did Searle Attack Strong Strong or Weak Strong AI.Aaron Sloman - 1986 - In A. G. Cohn and & R. J. Thomas (eds.), Artificial Intelligence and its Applications. John Wiley and Sons.
    John Searle's attack on the Strong AI thesis, and the published replies, are all based on a failure to distinguish two interpretations of that thesis, a (...)strong one, which claims that the mere occurrence of certain process patterns will suffice for the occurrence of mental states, and a weak one which requires that the processes be produced in the right sort of way. Searle attacks strong strong AI, while most of his opponents defend weak strong AI. This paper explores some of Searle's concepts and shows that there are interestingly different versions of the 'Strong AI' thesis, connected with different kinds of reliability of mechanisms and programs. (shrink)
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  26.  7
    Beyond the Middle Finger: Plato, Schiller and the Political Aesthetics of Ai Weiwei.Jason Miller - 2016 - Critical Horizons 17 (3-4):304-323.
    The photograph of Ai Weiweis middle finger set against the backdrop of Tiananmen Square has become an icon of politically subversive art. But can we see (...)beyond the middle finger? Here I argue that current theories of political aesthetics operate on an oversimplified dichotomy between two competing paradigms of political art, and that this threatens a more nuanced engagement with contemporary artistic practices. In the first part, I re-examine both the antagonistic relation between art and politics exemplified in Plato's verdict against poetry as a socially corrosive form of imitation as well as the instrumental relation of art and politics developed in Friedrich Schillers conception of aesthetic education as a means of social and political reform. Then, drawing on recent work by the controversial Chinese artist, I argue for a model of political art that can account for the more complex interrelation of criticism and cultural affirmation evident in a growing body of politically-oriented art. (shrink)
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  27.  28
    Utilising Appreciative Inquiry (AI) in Creating a Shared Meaning of Ethics in Organisations.L. J. van Vuuren & F. Crous - 2005 - Journal of Business Ethics 57 (4):399-412.
    . The management of ethics within organisations typically occurs within a problem-solving frame of reference. This often results in a reactive, problem-based and externally induced approach (...)to managing ethics. Although basing ethics management interventions on dealing with and preventing current and possible future unethical behaviour are often effective in that it ensures compliance with rules and regulations, the approach is not necessarily conducive to the creation of sustained ethical cultures. Nor does the approach afford (mainly internal) stakeholders the opportunity to be co-designers of the organisations ethical future. The aim of this paper is to present Appreciative Inquiry (AI) as an alternative approach for developing a shared meaning of ethics within an organisation with a view to embrace and entrench ethics, thereby creating a foundation for the development of an ethical cul- ture over time. A descriptive case study based on an application of AI is used to illustrate the utility of AI as a way of thinking and doing to precede and complement problem-based ethics management systems and interventions. (shrink)
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  28.  25
    Die Starke KI-TheseThe Strong AI-Thesis.Stephan Zelewski - 1991 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 22 (2):337-348.
    Summary The controversy about the strong AI-thesis was recently revived by two interrelated contributions stemming from J. R. Searle on the one hand and from P. (...)M. and P. S. Churchland on the other hand. It is shown that the strong AI-thesis cannot be defended in the formulation used by the three authors. It violates some well accepted criterions of scientific argumentation, especially the rejection of essentialistic definitions. Moreover, Searle's ‘proof’ is not conclusive. Though it may be reconstructed in a conclusive manner, the modified proof is trivial. Beyond that, the most interesting aspect is formulated as an axiom that is not justified either. Therefore Searle's criticism of strong AI-thesis fails to be a convincing proof — it can be reduced to an unjustified presupposition. (shrink)
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  29.  22
    AI and Law: What About the Future[REVIEW]Anja Oskamp, Maaike Tragter & Cees Groendijk - 1995 - Artificial Intelligence and Law 3 (3):209-215.
    The introduction of results of AI and Law research in actual legal practice advances disturbingly slow. One of the problems is that most research can be classified (...)
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  30. The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review.Jessica Morley, Caio C. V. Machado, Christopher Burr, Josh Cowls, Indra Joshi, Mariarosaria Taddeo & Luciano Floridi - manuscript
    Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical (...)
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  31. Designing AI for Social Good: Seven Essential Factors.Josh Cowls, Thomas C. King, Mariarosaria Taddeo & Luciano Floridi - manuscript
    The idea of Artificial Intelligence for Social Good (henceforth AI4SG) is gaining traction within information societies in general and the AI community in particular. It has the (...)
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  32. Toward an Ethics of AI Assistants: an Initial Framework.John Danaher - 2018 - Philosophy and Technology 31 (4):629-653.
    Personal AI assistants are now nearly ubiquitous. Every leading smartphone operating system comes with a personal AI assistant that promises to help you with basic cognitive tasks: (...)
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  33.  10
    AI and the Path to Envelopment: Knowledge as a First Step Towards the Responsible Regulation and Use of AI-Powered Machines.Scott Robbins - forthcoming - AI and Society:1-10.
    With Artificial Intelligence entering our lives in novel waysboth known and unknown to usthere is both the enhancement of existing ethical issues associated with AI as (...) well as the rise of new ethical issues. There is much focus on opening up theblack boxof modern machine-learning algorithms to understand the reasoning behind their decisionsespecially morally salient decisions. However, some applications of AI which are no doubt beneficial to society rely upon these black boxes. Rather than requiring algorithms to be transparent we should focus on constraining AI and those machines powered by AI within microenvironmentsboth physical and virtualwhich allow these machines to realize their function whilst preventing harm to humans. In the field of robotics this is calledenvelopment’. However, to put anenvelopearound AI-powered machines we need to know some basic things about them which we are often in the dark about. The properties we need to know are the: training data, inputs, functions, outputs, and boundaries. This knowledge is a necessary first step towards the envelopment of AI-powered machines. It is only with this knowledge that we can responsibly regulate, use, and live in a world populated by these machines. (shrink)
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  34.  40
    15 Challenges for AI: or What AI CanT Do.Thilo Hagendorff & Katharina Wezel - forthcoming - AI and Society:1-11.
    The currentAI Summeris marked by scientific breakthroughs and economic successes in the fields of research, development, and application of systems with artificial intelligence. But, aside (...)
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  35.  26
    Non-Artificial Non-Intelligence: Amazons Alexa and the Frictions of AI.Tero Karppi & Yvette Granata - 2019 - AI and Society 34 (4):867-876.
    This paper examines a case where Amazons cloud-based AI assistant Alexa accidentally ordered a dollhouse for a 6-year-old girl. In the press, the case (...)was defined as a technical recognition problem. Building on this idea, we argue that the dollhouse case helps us to analyze the limits of current AI applications. By drawing on the writings of Gilles Deleuze and François Laruelle, we argue that these limits are not merely technical but more deeply embedded in the structures where the thinking of AI can potentially happen. We point out that AI research has been compromised by the concepts of what constitutes bothartificialand by what constitutesintelligence’. First, we use the notion of artificial non-intelligence to explain how different modes of digital capitalism such as voice commerce establish limits for AI. Second, we use the notion of non-artificial intelligence to illustrate the limits of associating AIs modes of thinking with human thought. (shrink)
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  36.  92
    Supporting Human Autonomy in AI Systems.Rafael Calvo, Dorian Peters, Karina Vold & Richard M. Ryan - forthcoming - In Christopher Burr & Luciano Floridi (eds.), Ethics of Digital Well-being: A Multidisciplinary Approach.
    Autonomy has been central to moral and political philosophy for millenia, and has been positioned as a critical aspect of both justice and wellbeing. Research in psychology (...)
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  37.  70
    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 (...)
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  38.  6
    Interperforming in AI: Question ofNaturalin Machine Learning and Recurrent Neural Networks.Tolga Yalur - forthcoming - AI and Society:1-9.
    This article offers a critical inquiry of contemporary neural network models as an instance of machine learning, from an interdisciplinary perspective of AI studies and performativity. It (...)
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  39. AI, Concepts, and the Paradox of Mental Representation, with a Brief Discussion of Psychological Essentialism.Eric Dietrich - 2001 - J. Of Exper. And Theor. AI 13 (1):1-7.
    Mostly philosophers cause trouble. I know because on alternate Thursdays I am one -- and I live in a philosophy department where I watch all of them cause (...) trouble. Everyone in artificial intelligence knows how much trouble philosophers can cause (and in particular, we know how much trouble one philosopher -- John Searle -- has caused). And, we know where they tend to cause it: in knowledge representation and the semantics of data structures. This essay is about a recent case of this sort of thing. One of the take-home messages will be that AI ought to redouble its efforts t o understand concepts. (shrink)
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  40. AI, Situatedness, Creativity, and Intelligence; or the Evolution of the Little Hearing Bones.Eric Dietrich - 1996 - J. Of Experimental and Theoretical AI 8 (1):1-6.
    Good sciences have good metaphors. Indeed, good sciences are good because they have good metaphors. AI could use more good metaphors. In this editorial, I would like (...)
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  41.  42
    From Alan Turing to Modern AI: Practical Solutions and an Implicit Epistemic Stance.George F. Luger & Chayan Chakrabarti - 2017 - AI and Society 32 (3):321-338.
    It has been just over 100 years since the birth of Alan Turing and more than 65 years since he published in Mind his seminal paper, Computing (...)
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  42. AI and the Mechanistic Forces of Darkness.Eric Dietrich - 1995 - J. Of Experimental and Theoretical AI 7 (2):155-161.
    Under the Superstition Mountains in central Arizona toil those who would rob humankind o f its humanity. These gray, soulless monsters methodically tear away at our meaning, (...)
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  43. On the Role of AI in the Ongoing Paradigm Shift Within the Cognitive Sciences.Tom Froese - 2007 - In M. Lungarella (ed.), 50 Years of AI. Springer Verlag.
    This paper supports the view that the ongoing shift from orthodox to embodied-embedded cognitive science has been significantly influenced by the experimental results generated by AI (...)research. Recently, there has also been a noticeable shift toward enactivism, a paradigm which radicalizes the embodied-embedded approach by placing autonomous agency and lived subjectivity at the heart of cognitive science. Some first steps toward a clarification of the relationship of AI to this further shift are outlined. It is concluded that the success of enactivism in establishing itself as a mainstream cognitive science research program will depend less on progress made in AI research and more on the development of a phenomenological pragmatics. (shrink)
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  44.  10
    Culture, the Process of Knowledge, Perception of the World and Emergence of AI.Badrudin Amershi - forthcoming - AI and Society:1-14.
    Considering the technological development today, we are facing an emerging crisis. We are in the midst of a scientific revolution, which promises to radically change not only (...)
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  45.  39
    AI, Agency and Responsibility: the VW Fraud Case and Beyond.Deborah G. Johnson & Mario Verdicchio - 2019 - AI and Society 34 (3):639-647.
    The concept of agency as applied to technological artifacts has become an object of heated debate in the context of AI research because some AI researchers ascribe (...)
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  46.  41
    Society Under Threatbut Not From AI.Ajit Narayanan - 2013 - AI and Society 28 (1):87-94.
    25 years ago, when AI & Society was launched, the emphasis was, and still is, on dehumanisation and the effects of technology on human life, including reliance on (...) technology. What we forgot to take into account was another very great danger to humans. The pervasiveness of computer technology, without appropriate security safeguards, dehumanises us by allowing criminals to steal not just our money but also our confidential and private data at will. Also, denial-of-service attacks prevent us from accessing the information we need when we want it. We are being dehumanised not by the technology but by criminals who use the ubiquity of the technology and its lack of security to steal from us and prevent us from doing what we want. What is more interesting is that this malevolent use of the technology doesnt come from monolithic corporate structures eager to control our lives but mainly from individuals keen to demonstrate their knowledge of the technology for social networking purposes. The aim of this paper is to turn the clock back 25 years and present an alternative perspective: the single, biggest threat of dehumanisation is not the pervasiveness and ubiquity of computers but the lack of ensuring that humans are provided with the basic security they need for using the technology safely and securely. Cyberspace is not a safe space to be. This was something that even far-sighted researcher colleagues in the 1970s and 1980s overlooked. The paper will explore where we went wrong 25 years ago in our predictions and concerns. We will also present a scenario that allows future generations to have a safer cyberworld. (shrink)
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  47.  22
    Posthuman Learning: AI From Novice to Expert?Cathrine Hasse - 2019 - AI and Society 34 (2):355-364.
    Will robots ever be able to learn like humans? To answer that question, one first needs to ask: what is learning? Hubert and Stuart Dreyfus had a (...)
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  48.  29
    Why AI Shall Emerge in the One of Possible Worlds?Ignacy Sitnicki - 2019 - AI and Society 34 (2):365-371.
    The aim of this paper is to present some philosophical considerations about the supposed AI emergence in the future. However, the predicted timeline of this process is (...)
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  49.  12
    Organic and Dynamic Tool for Use with Knowledge Base of AI Ethics for Promoting EngineersPractice of Ethical AI Design.Kaira Sekiguchi & Koichi Hori - forthcoming - AI and Society:1-21.
    In recent years, ethical questions related to the development of artificial intelligence are being increasingly discussed. However, there has not been enough corresponding increase in the research (...)
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  50.  62
    The Social Acceptability of AI Systems: Legitimacy, Epistemology and Marketing[REVIEW]Romain Laufer - 1992 - AI and Society 6 (3):197-220.
    The expression, ‘the culture of the artificialresults from the confusion between nature and culture, when nature mingles with culture to produce theartificialand science becomes (...)
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