150 research outputs found

    The Eliza effect and its dangers: from demystification to gender critique

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    This essay provides a gender critique of the Eliza effect. It delineates the way in which the Eliza effect is operationalised in AI research even as it is ostensibly demystified, for example in the writings of Douglas Hofstadter and Joseph Weizenbaum. It then exposes the gendered assumptions embedded in the nomenclature used to name this misperception of the computer as having capabilities equivalent to the human. It traces the genealogy of that nomenclature back through Weizenbaum’s ELIZA, to George Bernard Shaw’s Pygmalion. A close reading of the play is deployed in order to reveal the structural inequities of gender, class, and who or what gets to be human, that are both explored in the play and encoded in the operation and operationalisations of the Eliza effect. It concludes by attending to that operation and operationalisation in relation to today’s Virtual Personal Assistant’s, and makes a case for the importance of critique in order to expose the inequitable structures of power obscured and compounded by the Eliza effect – both its name, and that which it names

    Theopolis Monk: Envisioning a Future of A.I. Public Service

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    Visions of future applications of artificial intelligence tend to veer toward the naively optimistic or frighteningly dystopian, neglecting the numerous human factors necessarily involved in the design, deployment and oversight of such systems. The dream that AI systems may somehow replace the irregularities and struggles of human governance with unbiased efficiency is seen to be non-scientific and akin to a religious hope, whereas the current trajectory of AI development indicates that it will increasingly serve as a tool by which humans exercise control over other humans. To facilitate the responsible development of AI systems for the public good, we discuss current conversations on the topics of transparency and accountability

    Buber, educational technology, and the expansion of dialogic space

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    Buber’s distinction between the ‘I-It’ mode and the ‘I-Thou’ mode is seminal for dialogic education. While Buber introduces the idea of dialogic space, an idea which has proved useful for the analysis of dialogic education with technology, his account fails to engage adequately with the role of technology. This paper offers an introduction to the significance of the I-It/I-Thou duality of technology in relation to opening dialogic space. This is followed by a short schematic history of educational technology which reveals the role technology plays, not only in opening dialogic space, but also in expanding dialogic space. The expansion of dialogic space is an expansion of what it means to be ‘us’ as dialogic engagement facilitates the incorporation, into our shared sense of identity, of aspects of reality that are initially experienced as alien or ‘other’. Augmenting Buber with an alternative understanding of dialogic space enables us to see how dialogue mediated by technology, as well as dialogue with monologised fragments of technology (robots), can, through education, lead to an expansion of what it means to be human

    Judgment of the Humanness of an Interlocutor Is in the Eye of the Beholder

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    Despite tremendous advances in artificial language synthesis, no machine has so far succeeded in deceiving a human. Most research focused on analyzing the behavior of “good” machine. We here choose an opposite strategy, by analyzing the behavior of “bad” humans, i.e., humans perceived as machine. The Loebner Prize in Artificial Intelligence features humans and artificial agents trying to convince judges on their humanness via computer-mediated communication. Using this setting as a model, we investigated here whether the linguistic behavior of human subjects perceived as non-human would enable us to identify some of the core parameters involved in the judgment of an agents' humanness. We analyzed descriptive and semantic aspects of dialogues in which subjects succeeded or failed to convince judges of their humanness. Using cognitive and emotional dimensions in a global behavioral characterization, we demonstrate important differences in the patterns of behavioral expressiveness of the judges whether they perceived their interlocutor as being human or machine. Furthermore, the indicators of interest displayed by the judges were predictive of the final judgment of humanness. Thus, we show that the judgment of an interlocutor's humanness during a social interaction depends not only on his behavior, but also on the judge himself. Our results thus demonstrate that the judgment of humanness is in the eye of the beholder

    The Human Sweet Tooth

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    Humans love the taste of sugar and the word "sweet" is used to describe not only this basic taste quality but also something that is desirable or pleasurable, e.g., la dolce vita. Although sugar or sweetened foods are generally among the most preferred choices, not everyone likes sugar, especially at high concentrations. The focus of my group's research is to understand why some people have a sweet tooth and others do not. We have used genetic and molecular techniques in humans, rats, mice, cats and primates to understand the origins of sweet taste perception. Our studies demonstrate that there are two sweet receptor genes (TAS1R2 and TAS1R3), and alleles of one of the two genes predict the avidity with which some mammals drink sweet solutions. We also find a relationship between sweet and bitter perception. Children who are genetically more sensitive to bitter compounds report that very sweet solutions are more pleasant and they prefer sweet carbonated beverages more than milk, relative to less bitter-sensitive peers. Overall, people differ in their ability to perceive the basic tastes, and particular constellations of genes and experience may drive some people, but not others, toward a caries-inducing sweet diet. Future studies will be designed to understand how a genetic preference for sweet food and drink might contribute to the development of dental caries

    Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement

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    The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10462-016-9505-7.The evaluation of artificial intelligence systems and components is crucial for the progress of the discipline. In this paper we describe and critically assess the different ways AI systems are evaluated, and the role of components and techniques in these systems. We first focus on the traditional task-oriented evaluation approach. We identify three kinds of evaluation: human discrimination, problem benchmarks and peer confrontation. We describe some of the limitations of the many evaluation schemes and competitions in these three categories, and follow the progression of some of these tests. We then focus on a less customary (and challenging) ability-oriented evaluation approach, where a system is characterised by its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several possibilities: the adaptation of cognitive tests used for humans and animals, the development of tests derived from algorithmic information theory or more integrated approaches under the perspective of universal psychometrics. We analyse some evaluation tests from AI that are better positioned for an ability-oriented evaluation and discuss how their problems and limitations can possibly be addressed with some of the tools and ideas that appear within the paper. Finally, we enumerate a series of lessons learnt and generic guidelines to be used when an AI evaluation scheme is under consideration.I thank the organisers of the AEPIA Summer School On Artificial Intelligence, held in September 2014, for giving me the opportunity to give a lecture on 'AI Evaluation'. This paper was born out of and evolved through that lecture. The information about many benchmarks and competitions discussed in this paper have been contrasted with information from and discussions with many people: M. Bedia, A. Cangelosi, C. Dimitrakakis, I. GarcIa-Varea, Katja Hofmann, W. Langdon, E. Messina, S. Mueller, M. Siebers and C. Soares. Figure 4 is courtesy of F. Martinez-Plumed. Finally, I thank the anonymous reviewers, whose comments have helped to significantly improve the balance and coverage of the paper. This work has been partially supported by the EU (FEDER) and the Spanish MINECO under Grants TIN 2013-45732-C4-1-P, TIN 2015-69175-C4-1-R and by Generalitat Valenciana PROMETEOII2015/013.José Hernández-Orallo (2016). Evaluation in artificial intelligence: From task-oriented to ability-oriented measurement. 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