AI in Museums, edited by Sonja Thiel and Johannes C. Bernhardt, explores reflections, perspectives, and applications of AI in the museum sector, the convergence of museology, interactive curatorial practices, and digital education.
The publication describes various AI applications, such as chatbots, robots, and interactive artwork, analysing visitor behaviour, tagging images from digital archives, and preserving heritage, as well as facilitating museum research. AI is also used for tasks such as predicting visitor behaviour, personalising tours, and optimising educational benefits. These technologies enable natural language processing, computer vision, data mining, machine learning, and deep learning. Key Points:
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Rapid Adoption of AI: Highlights the rapid implementation of AI technologies in museums around the world, with over 586 AI projects identified in 56 countries between 2016 and 2021.
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Diverse Applications: Museums use AI in a variety of ways, demonstrating the potential of the technology to transform curatorial practices and visitor experiences.
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Research and Innovation: Several AI research projects in museums have developed innovative solutions, best practices, and new knowledge. These projects have become an inspiration for further research, promoting continuous improvement in the integration of AI into museums.
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Ethical Considerations: There is a need for critical technical practice to address biases, ensure inclusiveness, and uphold ethical standards in AI models and implementation in museums.
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Collaborative Approach: Collaboration and stakeholder engagement are key elements when it comes to leveraging artificial intelligence in museums effectively. By fostering partnerships and sharing best practices, museums can leverage AI effectively while aligning with their institutional and social missions.
Here is a summary overview of the chapters:
Part 1: Reflections
This chapter presents a concept-based approach to understanding how AI is transforming museums. There have been several studies regarding the applied dimensions of museum AI, such as limitations and possibilities for museum applications.
The chapter offers a conceptual framework for empirical studies on AI in museums as part of the growing field of research on AI in museums.
AI technologies have been integrated into various aspects of museum operations and visitor experiences. Notably :
Chatbots: Museums utilise chatbots powered by AI to engage with visitors, provide information about exhibits, answer questions, and enhance the overall visitor experience. Chatbots can offer personalised guidance, share stories, and assist with navigation, even outside of museum opening hours.
Deepfakes: Some studies explore the use of deepfake technology in museums, which involves creating realistic but fabricated audiovisual content. This technology can potentially be used for educational or interactive purposes within museum settings.
Digital Archives: AI is employed in digital archives within museums to scan, analyse, and automatically tag vast amounts of images and data. This helps in creating new forms of participation, learning, and aesthetic experiences for museum visitors.
Changing Working Conditions: The introduction of AI in museums has led to changes in working conditions for museum professionals. This includes adapting to new technologies, redefining roles, and exploring the implications of AI on traditional museum practices.
Concrete AI Projects: Museums have implemented various AI projects to enhance visitor engagement, optimise operations, and explore new possibilities for curatorial practices. These projects range from using AI for exhibition-making to developing AI-supported tools for exploring and curating digital collections.
Part 2: Perspectives
A broad range of AI applications are being used in museums, ranging from understanding visitors to developing new experiences, managing data, and transforming the museum’s professional practices. The chapter emphasises considering the sociocultural and sociotechnical implications of AI implementations. Several research projects have provided solutions that can be used as inspiration for future research.
It is essential to develop perspectives in the field by establishing best practices, funding, and a global mapping of AI usage within museums.
Part 3: Applications
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ARCU&OHM Algorithmic Exhibition-Making: Utilizing algorithms to curate exhibitions based on networks and word embeddings.
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LiviaAI Evaluating the Blackbox: Linking Viennese art through AI to enhance curation processes.
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Clouds of Symbols: The Digital Curator Project aims to curate digital collections using AI.
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xCurator: AI-supported exploration and curation of digital collections.
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Say the Image, Don’t Make It: Empowering human-AI co-creation through interactive installations. “Yannick Hofmann’s interactive installation WishingWell was produced between 2022 and 2023 as part of the ‘intelligent. museum’ project.”
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CHIM—Chatbot in the Museum: Exploring and explaining museum objects using speech-based AI.
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With AI to Art!: Engaging with historical figures through AI chatbots.
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Exploring Beyond the Exhibits: Creating knowledge for social robots in public spaces.
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Tracking the Visitor: Implementing an optical indoor system for visitor research in museums,
Our Take
AI in Museums describes the transformative potential of artificial intelligence in museums, the need for ethical considerations, and the role of research and collaboration in enabling innovation. As museums embrace AI technologies thoughtfully and strategically, they can enhance visitor experiences, preserve cultural heritage, and adjust to the evolving digital landscape smartly. The publication is a must read as offers a balance of in dept research and outlook, while showcasing inspirational initiatives.
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