The incorporation of algorithmic procedures into the automation of image production has been gradual, but has reached critical mass over the past century, especially with the advent of photography, the introduction of digital computers and the use of artificial intelligence (AI) and machine learning (ML). Due to the increasingly significant influence algorithmic processes have on visual media, there has been an expansion of the possibilities as to how images may behave, and a consequent struggle to define them. This algorithmic turnhighlights inner tensions within existing notions of the image, namely raising questions regarding the autonomy of machines, author- and viewer- ship, and the veracity of representations. In this sense, algorithmic images hover uncertainly between human and machine as producers and interpreters of visual information, between representational and non-representational, and between visible surface and the processes behind it. This paper gives an introduction to fundamental internal discrepancies which arise within algorithmically produced images, examined through a selection of relevant artistic examples. Focusing on the theme of uncertainty, this investigation considers how algorithmic images contain aspects which conflict with the certitude of computation, and how this contributes to a difficulty in defining images.

中文翻译：

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算法图像的不确定性，xCoAx特刊2019

将算法程序整合到图像生成自动化中是渐进的，但在过去的一个世纪中已达到临界点，特别是随着摄影的出现，数字计算机的引入以及人工智能（AI）和机器学习的使用（ ML）。由于算法过程对视觉媒体的影响越来越大，因此关于图像行为方式的可能性有了扩展，因此很难定义它们。这种算法转向强调了图像现有概念内的内在张力，即提出了有关机器自主性，作者和观看者以及表示准确性的问题。从这个意义上讲，算法图像是视觉信息的产生者和解释者在人与机器之间不确定地徘徊，在代表性和非代表性之间，以及可见表面与其背后的过程之间。本文介绍了通过算法产生的图像内部出现的基本内部差异，并通过选择相关的艺术实例进行了研究。围绕不确定性的主题，本研究考虑了算法图像如何包含与计算确定性冲突的方面，以及这如何导致难以定义图像。