环绕技术

Beth Coleman
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引用次数: 0

摘要

在解决人工智能系统中的有害偏见问题时,本文要求考虑超越预测机器学习框架的生成型野生人工智能。这种观点认为,带有标签的训练数据的监督式学习主要是对现状的一种再现。基于这个框架,本文通过分析两种人工智能模式——监督学习(如机器视觉)和无监督学习(如游戏玩法)——来展示人工智能作为一种机制的潜力,这种机制可以在纯粹的繁殖条件之外创造联想模式。这个分析之后是对环绕技术概念的介绍,然后论文转向了解开分类逻辑的理论立场,转向了其他可能的立场——环绕(哈尼和莫顿)、外星智能(帕里西)和主体/客体解析的内部行为(巴拉德)。这篇论文提出了与野外人工智能相关的两个关键概念:殖民崇高和黑人技术。本文最后总结了人工智能在野外可以为颠覆压迫技术做出的贡献,从而实现人工智能的解放潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Technology of the Surround
In addressing the issue of harmful bias in AI systems, this paper asks for a consideration of a generatively wild AI that exceeds the framework of predictive machine learning. The argument places supervised learning with its labeled training data as primarily a form of reproduction of a status quo. Based on this framework, the paper moves through an analysis of two AI modalities—supervised learning (e.g., machine vision) and unsupervised learning (e.g., game play)—to demonstrate the potential of AI as mechanism that creates patterns of association outside of a purely reproductive condition. This analysis is followed by an introduction to the concept of the technology of the surround, where the paper then turns toward theoretical positions that unbind categorical logics, moving toward other possible positionalities—the surround (Harney and Moten), alien intelligence (Parisi), and intra-actions of subject/object resolution (Barad). The paper frames two key concepts in relation to an AI in the wild: the colonial sublime and black techné. The paper concludes with a summation of what AI in the wild can contribute to the subversion of technologies of oppression toward a liberatory potential of AI.
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