Artificial Intelligence and the Useful Art Museum: A Cross-Disciplinary Approach Towards Machine Learning and its Implications in the Museum Sphere

人工智能和有用的艺术博物馆：机器学习的跨学科方法及其在博物馆领域的影响

• 批准号: 2302434
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2302434
• 关键词: Artificial; Intelligence; Useful; Art; Museum

This proposed research will explore the contribution of Artificial Intelligence (AI) to the public art museum. Through practice-based, collaborative research with industry partners in the arts and non-arts sectors, this project will develop knowledge of the role of AI in a cultural environment and understand what impact AI will have on public trust. Specifically, this research will investigate the following key questions:(1) What is the role and potential uses of AI as a curatorial strategy?(2) How can AI be used to interpret and classify existing collections and inform acquisition?(3) In what ways will the use of AI in museums challenge and/or enhance public trust?This research will explore how AI will curate, classify and cluster big data sets, whilst acting as a co-producer of museums, and will discover the implications of new forms of intelligence embedded within museums; asking whether algorithmic outputs are aligned with (new) curatorial strategies, museum stakeholders and cultural policies. This research will question how ML may inform curatorial practice and whether it will introduce bias or as yet unpredictable and currently unknown patterns. This aims to push the art historical discourse beyond common boundaries, gathering knowledge with the help of algorithms and creating new connections between objects, their meanings and their place within museum collections. It is particularly important in the digital humanities 'to contest and transform particular institutional structures' (Bassett et al., 2017), especially as museums have often been seen as institutions where social inequalities have been 'constituted, reproduced and reinforced' (Sandell, 2005). This project will significantly contribute to the field of digital humanities, to critically reflect and understand 'how these technologies operate to structure the world around them, and in doing so transform humanities knowledge and practice' (Berry and Fagerjord, 2017). Furthermore, this research will explore how AI can help to foster the social mission of useful museums for the public - away from a 'disciplinary museum' (Hooper-Greenhill, 1992) towards a diverse museum that is digitally fit and aware of its social responsibilities - being a transparent (Rader et al., 2018) and useful place where audiences/users can gain familiarity with AI, enabling scholars to research interactions and to provide explanations.I propose to undertake practice-based, interdisciplinary and applied research which will explore the research questions through investigation of curatorial practices, exhibition design and display which draw on AI and the properties of ML and algorithms, and of audience responses to art which is (co)-produced with, filtered, mediated or classified by AI technologies.The methodology will involve partnership with museums and with collaborators from the creative industries and other sectors who are working with AI within their research practices, and who are seeking opportunities for public engagement and co-production in which to test these ideas. Partners who have already agreed to join this project are Alistair Hudson, Director of the Manchester City Galleries and the Whitworth, and Prof Richard Taylor, BNFL Chair in Nuclear Energy Systems at the UoM's Dalton Nuclear Institute, which applies AI technologies to support research in nuclear science and is fostering cross-disciplinary research via its BEAM network. Specifically, this project will discover new ways of implementing immersive and AI technologies in a way that will be useful to four main research stakeholder constituencies:Museum sector/non-arts and cultural industrial sectors/academic/the public.BNFL Chair in Nuclear Energy Systems at the University of Manchester's Dalton NuclearInstitute, which applies AI technologies to support research in nuclear science and is fosteringcross-disciplinary research via its BEAM network.

这项拟议的研究将探讨人工智能（AI）对公共艺术博物馆的贡献。通过与艺术和非艺术领域的行业合作伙伴基于实践的合作研究，该项目将发展有关AI在文化环境中的作用的知识，并了解AI对公共信任的影响。具体而言，这项研究将研究以下关键问题：（1）AI作为策略的作用和潜在用途是什么？（2）如何使用AI来解释和分类现有收藏并告知收购？（3）在博物馆挑战和/或增强公众信任中，将AI的使用将如何？这项研究将探讨AI将如何策划，分类和聚集大数据集，同时充当博物馆的共同制作人，并将发现新的含义嵌入博物馆内的情报形式；询问算法产出是否与（新的）策略，博物馆利益相关者和文化政策保持一致。这项研究将质疑ML如何为策展实践提供信息，以及它是否会引入偏见或目前未知且目前未知的模式。这旨在将艺术历史论述超越共同的界限，借助算法收集知识，并在对象，其含义和它们在博物馆收藏中的位置之间建立新的联系。在数字人文科学中，尤其重要的是，竞争和改变特定的机构结构”（Bassett等，2017），尤其是当博物馆经常被视为“构成，复制和加强社会不平等”的机构（Sandell，Sandell， 2005）。该项目将极大地为数字人文科学领域做出重大贡献，以批判性地反映和理解“这些技术如何运作周围的世界，并以此改变人文知识和实践”（Berry and Fagerjord，2017年）。此外，这项研究将探讨AI如何帮助促进有用的博物馆的社会使命 - 远离“纪律博物馆”（Hooper -Greenhill，1992），朝着数字化适合并意识到其社会责任的多元化博物馆发展。 - 成为透明的（Rader等，2018）和有用的地方，受众/用户可以在这里熟悉AI，使学者能够研究互动并提供解释。我建议进行基于实践的，跨学科和应用研究研究通过调查策展实践，展览设计和展示来提出质疑，这些设计和展示借鉴了AI以及ML和算法的特性，以及受众对艺术的反应（CO）与AI Technologies一起产生，过滤，中介或分类。方法论将涉及与博物馆以及来自创意产业的合作者以及在研究实践中与AI合作的其他部门的合作者，他们正在寻找机会进行公众参与和共同制作的机会来测试这些想法。已经同意加入该项目的合作伙伴是曼彻斯特城画廊和惠特沃思总监Alistair Hudson，以及UOM的Dalton Nou Institute的BNFL核能系统主席Richard Taylor教授，该学院应用人工智能技术来支持核研究的研究科学并通过其光束网络促进跨学科研究。具体而言，该项目将以一种对四个主要研究利益相关者选区有用的方式来发现实施身临其境和AI技术的新方法：博物馆行业/非艺术和文化工业部门/学术/学术/公共主席。在曼彻斯特大学的道尔顿核定场（Dalton Nucinstitute），该基督教委员会（Dalton Nuitinstitute）应用人工智能技术来支持核科学研究，并通过其光束网络促进了科罗斯科学科研究。