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Titlebook: Ethics of Artificial Intelligence; Francisco Lara,Jan Deckers Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive l

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發(fā)表于 2025-3-23 10:02:45 | 只看該作者
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發(fā)表于 2025-3-23 16:13:34 | 只看該作者
https://doi.org/10.1057/9780230584426cial impacts of AI applications in several fields has been remarkable. More recently, several contributions have started exploring the ecological impacts of AI. Machine learning systems do not have a neutral environmental cost, so it is important to unravel the ecological footprint of these techno-s
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發(fā)表于 2025-3-23 20:03:19 | 只看該作者
https://doi.org/10.1057/9781137466433it does so. First, it explores various proposed definitions of AGSI and the potential implications of its emergence, including the possibility of collaboration or conflict with humans, its impact on our daily lives, and its potential for increased creativity and wisdom. The concept of the Singularit
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發(fā)表于 2025-3-24 01:36:29 | 只看該作者
Francisco Lara,Jan DeckersProvides an up-to-date overview of various ethical issues around AI.Offers a comprehensive and structured understanding of AI ethics.Is relevant for experts and students of applied ethics focusing on
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發(fā)表于 2025-3-24 05:44:12 | 只看該作者
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發(fā)表于 2025-3-24 07:26:25 | 只看該作者
978-3-031-48137-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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發(fā)表于 2025-3-24 11:32:51 | 只看該作者
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發(fā)表于 2025-3-24 18:29:20 | 只看該作者
Introduction,o spent some time, in 2017, as a visiting researcher in the School of Medicine at Newcastle University (United Kingdom). Both of us had been interested for quite some time in the ethics of human enhancement by biotechnological means. Both saw significant problems with these ambitions and associated
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發(fā)表于 2025-3-24 21:28:52 | 只看該作者
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發(fā)表于 2025-3-25 02:34:21 | 只看該作者
Opacity, Machine Learning and Explainable AIman resources management, among many others. Behind most of these Artificial Intelligence tools is a pattern recognition model generated by Machine Learning. To do this, it is necessary to start from a dataset that characterizes the problem under study, and “train” this model to represent the former
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