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索人工智能(AI)对公共美术馆的贡献。通过与艺术和非艺术领域的行业合作伙伴进行基于实践的合作研究，该项目将发展关于人工智能在文化环境中的角色的知识，并了解人工智能将对公众信任产生什么影响。具体地说，这项研究将调查以下关键问题：(1)人工智能作为策展策略的作用和潜在用途是什么？(2)人工智能如何用于解释和分类现有藏品并为获取提供信息？(3)在博物馆中使用人工智能将以哪些方式挑战和/或增强公众信任？本研究将探索人工智能如何作为博物馆的联合生产者对大数据集进行管理、分类和集群，并将发现嵌入博物馆的新形式智能的影响；询问算法输出是否与(新的)策展战略、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迄今不可预测和目前未知的模式。这旨在将艺术历史话语推向共同的边界，借助算法收集知识，并在物品、它们的意义和它们在博物馆藏品中的位置之间建立新的联系。在数字人文科学中，“挑战和改变特定的制度结构”尤其重要(Bassett等人，2017年)，特别是因为博物馆经常被视为社会不平等已被“构成、复制和强化”的机构(Sandell，2005)。该项目将对数字人文领域做出重大贡献，批判性地反思和理解“这些技术如何运作来构建他们周围的世界，并通过这样做转变人文知识和实践”(Berry和Fagerjord，2017)。此外，这项研究将探索人工智能如何帮助培养对公众有用的博物馆的社会使命--从一个‘学科博物馆’(Hooper-Greenhill，1992)转向一个与数字相适应并意识到其社会责任的多元化博物馆--成为一个透明的(Rader等人，2018)和有用的地方，在那里观众/用户可以熟悉人工智能，使学者能够研究互动并提供解释。我建议进行基于实践的、跨学科的和应用的研究，通过调查利用人工智能以及ML和算法的特性的策展实践、展览设计和展示来探索研究问题。以及观众对与人工智能技术(共同)制作、过滤、中介或分类的艺术的反应。方法将涉及与博物馆以及创意产业和其他部门的合作者合作，他们在研究实践中与人工智能合作，并寻找机会让公众参与和联合制作，以检验这些想法。已经同意加入该项目的合作伙伴是曼彻斯特城市画廊和惠特沃斯博物馆的主任阿里斯泰尔·哈德森，以及密歇根大学道尔顿核研究所的BNFL核能系统教席理查德·泰勒教授，该研究所应用人工智能技术支持核科学研究，并通过其BEAM网络促进跨学科研究。具体地说，该项目将发现以一种对四个主要研究利益攸关方有用的方式实施沉浸式和人工智能技术的新方法：博物馆部门/非艺术和文化产业部门/学术/公众。曼彻斯特大学道尔顿核研究所核能系统BNFL教席，应用人工智能技术支持核科学研究，并通过其BEAM网络促进跨学科研究。