ABSTRACT

According to Brooks [2017. “The Big Problem with Self-driving Cars Is People”. IEEE Spectrum: Technology, Engineering, and Science News], artificial intelligence has had a variable track-record of usefulness in situations where context and environmental knowledge are responsible for shaping human interactions. In 2021, providing contextually aware training to supervised machine learning is still a non-trivial task for AI models that involve complex systems. In addition, knowledge held only across distributed members of a community, within culture, or tacitly within the wider environment of the ambient commons [McCullough 2013. Ambient Commons: Attention in the Age of Embodied Information. Cambridge, MA: MIT Press] evades consistent generalizable modelling – even in technical domains such as traffic flow management, atmospheric chemistry, or the prediction of election results. Yet it is precisely these interactions of context, community, culture and environment that also define how music can be created. The creative arts can themselves be thought of as a complex system. Assuming that creativity is non-generalizable, this paper assesses creative processes through a humanities-centric lens of machine learning and robotics, aiming to better understand relationships between context, environment and experimental system in artistic research. These relationships are now themselves significantly digitally mediated, requiring a change in academic discourse away from artefacts which need discrete research justification towards a more holistic, and often non-linear view of networks that require cultural situation. In doing so, issues of creative accountability [Field 2021. “Changing the Vocabulary of Creative Research: The Role of Networks, Risk, and Accountability in Transcending Technical Rationality.” In Sound Work: Composition as Critical Technical Practice, edited by J. Impett, 303–317.Orpheus Institute Series. Leuven: Leuven University Press] and the implications of substituting “creative question” for “research question” are examined within creative research. Early twentieth century ideas related to progressivism which have instrumentalized creative practice, particularly where technology forms part of art making, are challenged by re-thinking change through new models. The Three Horizons change model [Sharpe 2016. “Three Horizons: A Pathways Practice for Transformation.” Ecology and Society 21 (2): 47] originally intended to describe environmental ecosystems, is assessed as a practical tool for designing creative research.

摘要

根据布鲁克斯 [2017. “自动驾驶汽车的最大问题是人”。IEEE Spectrum：技术、工程和科学新闻]，在上下文和环境知识负责塑造人类互动的情况下，人工智能的有用性记录参差不齐。到 2021 年，为有监督的机器学习提供上下文感知训练对于涉及复杂系统的 AI 模型来说仍然是一项艰巨的任务。此外，知识仅在社区的分布式成员、文化内或在更广泛的环境公地环境中默认持有 [McCullough 2013。环境公地：体现信息时代的注意力. 马萨诸塞州剑桥市：麻省理工学院出版社] 避开了一致的可推广建模——即使在交通流量管理、大气化学或选举结果预测等技术领域也是如此。然而，正是这些背景、社区、文化和环境的相互作用也定义了音乐的创作方式。创意艺术本身可以被认为是一个复杂的系统。假设创造力是不可概括的，本文通过以人文为中心的机器学习和机器人技术来评估创作过程，旨在更好地理解艺术研究中背景、环境和实验系统之间的关系。现在，这些关系本身已经通过数字化方式进行了显着的调解，需要改变学术话语，从需要离散研究证明的人工制品转向需要文化情境的网络的更全面且通常是非线性的观点。在此过程中，创造性责任问题 [2021 年领域。“改变创造性研究的词汇：网络、风险和责任在超越技术理性中的作用。” 在Sound Work: Composition as Critical Technical Practice，J. Impett 编辑，303–317.Orpheus Institute 系列。鲁汶：鲁汶大学出版社]以及用“创造性问题”代替“研究问题”的含义在创造性研究中进行了研究。20 世纪早期与进步主义相关的思想已将创造性实践工具化，特别是在技术构成艺术创作的一部分的情况下，通过新模式重新思考变革，这对它们提出了挑战。三个视野变革模型 [Sharpe 2016。“三个视野：转型的路径实践”。Ecology and Society 21 (2): 47] 最初旨在描述环境生态系统，被评估为设计创造性研究的实用工具。