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人工智能符号学的主要任务。

The main tasks of a semiotics of artificial intelligence.

作者信息

Leone Massimo

机构信息

University of Turin, Turin, Italy.

Shanghai University, Shanghai, China.

出版信息

Lang Semiot Stud. 2023 Mar 28;9(1):1-13. doi: 10.1515/lass-2022-0006. Epub 2023 Jan 19.

DOI:10.1515/lass-2022-0006
PMID:37252011
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10214016/
Abstract

The article indicates the essential tasks of a semiotics of artificial intelligence: studying the way it simulates the expression of intelligence; the way it produces content that is creatively endowed; the ideological assumptions of artificial intelligence within the culture that produces it. Artificial intelligence is, from a semiotic point of view, the predominant technology of fakery in the current era. On the strength of its studies on the false, semiotics can therefore also be applied to the analysis of the fake that, in increasingly sophisticated forms, is produced through artificial intelligence and through the deep learning of neural networks. The article focuses on the adversarial ones, trying to highlight their ideological assumptions and cultural developments, which seem to indicate the entry of human societies and cultures into the 'realm of the absolute fake'.

摘要

这篇文章指出了人工智能符号学的基本任务

研究其模拟智能表达的方式;其产生具有创造性内容的方式;以及在产生它的文化中人工智能的思想假设。从符号学的角度来看,人工智能是当今时代造假的主要技术。因此,基于其对虚假的研究,符号学也可应用于对通过人工智能和神经网络深度学习以越来越复杂的形式产生的虚假内容的分析。本文聚焦于对抗性虚假内容,试图突出其思想假设和文化发展,这似乎表明人类社会和文化进入了“绝对虚假的领域”。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/61df/10214016/ba4f2f8fb0c6/j_lass-2022-0006_fig_004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/61df/10214016/98b94b22fada/j_lass-2022-0006_fig_001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/61df/10214016/56eca0861b3e/j_lass-2022-0006_fig_002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/61df/10214016/e6f9e6ec5258/j_lass-2022-0006_fig_003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/61df/10214016/ba4f2f8fb0c6/j_lass-2022-0006_fig_004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/61df/10214016/98b94b22fada/j_lass-2022-0006_fig_001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/61df/10214016/56eca0861b3e/j_lass-2022-0006_fig_002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/61df/10214016/e6f9e6ec5258/j_lass-2022-0006_fig_003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/61df/10214016/ba4f2f8fb0c6/j_lass-2022-0006_fig_004.jpg

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本文引用的文献

1
Engineering Tissue Fabrication With Machine Intelligence: Generating a Blueprint for Regeneration.利用机器智能进行工程组织构建:生成再生蓝图。
Front Bioeng Biotechnol. 2020 Jan 10;7:443. doi: 10.3389/fbioe.2019.00443. eCollection 2019.
2
Mastering the game of Go with deep neural networks and tree search.用深度神经网络和树搜索掌握围棋游戏。
Nature. 2016 Jan 28;529(7587):484-9. doi: 10.1038/nature16961.