Discerning Relational Data in Breath Patterns. Gilbert Simondon’s Philosophy in the Context of Sequence Transduction

Lisa Müller-Trede
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引用次数: 1

Abstract

This article discusses Gilbert Simondon’s philosophies of the technical object, information, and individuation to frame the potential inherent in a practical application of his notions of intensity, amplification, and transduction of relational processes, which have been largely neglected in the traditions of substantialist and hylomorphic thought. Specifically, the study introduces a method to discern relational information by amplifying audible breath patterns of a collective via a wearable digital stethoscope (WDS). The non-lexical modality of the breath grants insights into non-verbal phases of communication during which multiple points of view may exist simultaneously. These points of view can be understood as a subject’s sense of orientation within phases prior to signification, i.e., before affect becomes a specific emotion and before perception becomes a concrete action—using the terms as they are defined by Simondon. Bodily movement is audible within the breath and can be further transcribed into preliminary signs with the help of a sequence transduction machine learning (ML) model. Discerning semiosis within audible breath patterns exemplifies a logic of computation which is not concerned with quantitative and qualitative information but, instead, computes intense data to grasp relational dynamics.
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辨别呼吸模式中的关系数据。序列转导语境下的吉尔伯特·西蒙顿哲学
本文讨论了吉尔伯特·西蒙顿关于技术对象、信息和个性化的哲学,以构建他的关系过程的强度、放大和转导等概念在实际应用中的内在潜力,这些概念在实体主义和形态思想的传统中被很大程度上忽视了。具体而言,该研究介绍了一种通过可穿戴数字听诊器(WDS)放大集体可听呼吸模式来识别相关信息的方法。呼吸的非词汇形态使我们能够深入了解交流的非语言阶段,在此期间,多个观点可能同时存在。这些观点可以被理解为主体在意义之前的阶段中的方向感,也就是说,在情感成为一种特定的情感之前,在感知成为一种具体的行为之前——使用西蒙顿定义的术语。在呼吸中可以听到身体运动,并且可以在序列转导机器学习(ML)模型的帮助下进一步转录为初步信号。在可听到的呼吸模式中识别符号学例证了一种计算逻辑,它不涉及定量和定性信息,而是计算密集数据以掌握关系动态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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