商业神经网络的内容政策和访问限制是对艺术创作的激励

IF 0.2 0 HUMANITIES, MULTIDISCIPLINARY
Stanislav V. Milovidov
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引用次数: 0

摘要

本文采用案例研究法,调查了集中在 Twitter(后更名为 X)上的艺术神经网络社区,该社区在意识形态上受到 OpenAI 内容政策和限制的影响。如今,许多在艺术作品中使用机器学习技术的年轻艺术家(Midjourney、Stable Diffusion、Kandinsky)指出,尽管在博物馆和当代数字艺术展览中出现的通过提示生成图像的神经网络生成器领域取得了重大进展,但仍有大量艺术作品主要使用 2021 年创建的过时的文本到图像算法。这些神经网络至今仍在艺术领域大行其道。这种做法得以持续的原因可以从 2021 年艺术家与 OpenAI 之间的软性意识形态冲突中找到。当时,神经网络尚未成为主流,主流主题是深度造假,这成为全面讨论现代社会实施人工智能算法的可能性和后果的基础。与神经网络工作相关的一系列丑闻引起了企业的警觉,它们担心神经网络的错误和偏差会带来声誉上的损失。与此同时,当代艺术中关于言论、思想和自我表达自由的现有论述导致了意识形态冲突,因为创作者对艺术表达工具引入了限制。以前,艺术家的行为不受技术手段的制约。因此,社区并不接受这种状态,作为合作和 "集体智慧 "的结果,他们在 GitHub 和 Google Colab 平台上创建了自己的算法,并公开了代码,每个人都可以利用这些算法进行视觉实验。艺术家们面临着与全球主义和艺术反进步作斗争的意识形态问题,他们要置身于体制之外,但又要与之抗争。这一过程导致了神经网络艺术实践的分化,媒体艺术家 Ryan Murdock 将其概括为 2021 年的黑客努力或现代算法文本到图像(2022 年之后)通向文本引导的视觉艺术的大门。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Content policy and access limitations on commercial neural networks as an incentive to artivism
This article employs a case‐study method to investigate the artivism neural network community concentrated on Twitter (since renamed X), which has been ideologically influenced by the content policy and limitations of OpenAI. Today, many young artists using machine learning technologies in their artworks (Midjourney, Stable Diffusion, Kandinsky) note that despite significant progress in the field of neural network generators of image through prompts present in museums and exhibitions of contemporary digital art, a significant number of artworks are still made chiefly using outdated text-to-image algorithms created in 2021. These neural networks continue to be popular in art to this day. The reasons for the sustainability of such practices can be found in the soft ideological conflict between artists and OpenAI in 2021. At that time, neural networks had not yet become mainstream, and the dominant theme was deep fakes, which became the basis for a comprehensive discussion about the possibilities and consequences of implementing AI algorithms in modern society. A series of scandals related to the work of neural networks alerted businesses, which feared the reputational costs of neural network errors and biases. At the same time, the existing discourse on freedom of speech, thought, and self-expression in contemporary art has led to ideological conflict, as the creators have introduced constraints on tools of artistic expression. Previously, the actions of artists were not moderated by technical means. Thus, the community did not accept this state of affairs, and as a result of cooperation and “collective intelligence” created, on the GitHub and Google Colab platforms, their own algorithms with open code, with which everyone could carry out their visual experiments. Artists face the ideological question of fighting globalism and anti-progress in art to be outside the system but to riot against it. This process led to a division of artistic practises in neural network art, outlined by media artist Ryan Murdock as a gateway to text-guided visual art by the hacker effort of 2021 or the modern generation of algorithm text-to-images (after 2022).
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来源期刊
Artnodes
Artnodes HUMANITIES, MULTIDISCIPLINARY-
CiteScore
0.70
自引率
0.00%
发文量
26
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