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Entretanto, pouco se sabe sobre a eficiência desta forma de “Ética”. ) em Ética da IA apontam uma convergência sobre certos princípios éticos que, supostamente, governam a indústria da IA. Diversas organizações de cunho privado, público e não governamentais têm publicado diretrizes propondo princípios éticos para a regulamentação do uso e desenvolvimento de sistemas inteligentes autônomos.
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O campo da Segurança e da Ética da Inteligência Artificial são áreas de pesquisa emergentes que vêm ganhando popularidade nos últimos anos. Atualmente, tecnologias como robótica, nanotecnologia, genética e inteligência artificial prometem transformar nosso mundo e a maneira como vivemos. ( shrink)Ī 4ª Revolução Industrial é o culminar da era digital. The imitation game, for Turing, was not an intelligence test, but a technological aspiration whose realization would likely involve a change in society’s attitude toward machines. Turing thought that such a change would eventually occur: He believed that when scientists overcome the technological challenge of constructing sophisticated machines that could imitate human verbal behavior-i.e., do well in the imitation game-humans’ prejudiced attitude toward machinery will have altered in such a way that machines could be said to be intelligent. He also realized, though, that if humans’ a priori, chauvinistic attitude toward machinery changed, the existence of intelligent machines would become logically possible. ) human society held a prejudiced attitude toward machinery-seeing machines a priori as mindless objects-machines could not be said to be intelligent, by definition. Turing, I claim, saw intelligence as a social concept, meaning that possession of intelligence is a property determined by society’s attitude toward the entity.
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Following a series of papers by Diane Proudfoot, I offer a socio-technological interpretation of Turing’s paper and present an alternative way of understanding both the imitation game and Turing’s concept of intelligence. ( shrink)Īlan Turing’s 1950 imitation game has been widely understood as a means for testing if an entity is intelligent. Finally, we show how this analysis predicts that a widespread adoption of language generators as tools for writing could result in permanent pollution of our informational ecosystem with massive amounts of very plausible but often untrue texts. This, we moreover claim, can hijack our intuitive capacity to evaluate the accuracy of its outputs. We claim that these kinds of models cannot be forced into producing only true continuation, but rather to maximise their objective function they strategize to be plausible instead of truthful. The real reason GPT’s answers seem senseless being that truth-telling is not amongst them. Using ideas of compression, priming, distributional semantics and semantic webs we offer our own theory of the limits of large language models like GPT-3, and argue that GPT can competently engage in various semantic tasks. Following a critical assessment of the methodology which led previous scholars to dismiss GPT’s abilities, we argue against claims that GPT-3 completely lacks semantic ability. ) as a probabilistic measure based on Item Response Theory from psychometrics. We start by formalising the recently proposed notion of reversible questions, which Floridi & Chiriatti propose allow one to ‘identify the nature of the source of their answers’, (.
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This article contributes to the debate around the abilities of large language models such as GPT-3, dealing with: firstly, evaluating how well GPT does in the Turing Test, secondly the limits of such models, especially their tendency to generate falsehoods, and thirdly the social consequences of the problems these models have with truth-telling.