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Contextual information helps understand messages written with textisms - Data Repository ...
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The Multimodal Turing Test for Realistic Humanoid Robots with Embodied Artificial Intelligence
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Deceitful media: Artificial intelligence and social life after the Turing Test
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Simone Natale. - : Oxford University Press, 2021. : country:USA, 2021. : place:New York, 2021
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Manual Re-evaluation of Translation Quality of WMT 2018 English-Czech systems
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Questioning Turing test
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Abstract:
The Turing Test (TT) is an experimental paradigm to test for intelligence, where an entity’s intelligence is inferred from its ability, during a text-based conversation, to be recognized as a human by the human judge. The advantage of this paradigm is that it encourages alternative versions of the test to be designed; and it can include any field of human endeavour. However, it has two major problems: (i) it can be passed by an entity that produces uncooperative but human-like responses (Artificial Stupidity); and (ii) it is not sensitive to how the entity produces the conversation (Blockhead). In light of these two problems, I propose a new version of the TT, the Questioning Turing Test (QTT). In the QTT, the task of the entity is not to hold a conversation, but to accomplish an enquiry with as few human-like questions as possible. The job of the human judge is to provide the answers and, like in the TT, to decide whether the entity is human or machine. The QTT has the advantage of parametrising the entity along two further dimensions in addition to ‘human-likeness’: ‘correctness’, evaluating if the entity accomplishes the enquiry; and ‘strategicness’, evaluating how well the entity carries out the enquiry, in terms of the number of questions asked – the fewer, the better. Moreover, in the experimental design of the QTT, the test is not the enquiry per se, but rather the comparison between the performances of humans and machines. The results gained from the QTT show that its experimental design minimises false positives and negatives; and avoids both Artificial Stupidity and Blockhead.
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Keyword:
artificial intelligence; Turing Test
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URL: https://hdl.handle.net/1842/37290
https://doi.org/10.7488/era/576
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Twenty Years Beyond the Turing Test: Moving Beyond the Human Judges Too
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Improbotics: Exploring the Imitation Game Using Machine Intelligence in Improvised Theatre
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In: Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment; Vol. 14 No. 1 (2018): Fourteenth Artificial Intelligence and Interactive Digital Entertainment Conference ; 2334-0924 ; 2326-909X (2018)
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From Big Data to Deep Learning: A Leap Towards Strong AI or ‘Intelligentia Obscura’?
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In: Big Data and Cognitive Computing ; Volume 2 ; Issue 3 (2018)
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AI to Bypass Creativity. Will Robots Replace Journalists? (The Answer Is “Yes”)
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In: Information ; Volume 9 ; Issue 7 (2018)
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Schémas Winograd en français: une étude statistique et comportementale
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In: Actes de TALN 2017 ; TALN 2017 ; https://hal.archives-ouvertes.fr/hal-01628342 ; TALN 2017, Jun 2017, Orléans, France (2017)
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Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement
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Universal intelligence: A definition of machine intelligence
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In: Minds and Machines: journal for artificial intelligence, philosophy and cognitive sciences (2015)
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Universal intelligence: a definition of machine intelligence
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In: Minds and Machines (2015)
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Universal intelligence: a definition of machine intelligence
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In: Minds and Machines (2015)
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Universal intelligence: A definition of machine intelligence
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In: Minds and Machines: journal for artificial intelligence, philosophy and cognitive sciences (2015)
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El Test de Turing para la evaluación de resumen automático de texto
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In: Linguamática, Vol 7, Iss 2 (2015) (2015)
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Using Cognitive Tunnels in a New Approach to Building Social Elevators in the Information Society
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