AMT Lab @ CMU

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A general look on Artificial Intelligence used in Museum Audience Engagement

Recently, museums are utilizing Artificial Intelligence Technology to engage audiences and personalize visitor experiences. Before doing any further research, it is important to understand what the AI technologies are that are used for audience engagement. How are they implemented into museums? And are there any challenges or problems? The infographic below summarizes the AI subcategories that museums most frequently use, the best practices for each subcategory, the activities that AI are implemented into, and the challenges that technologies could bring. 

Among all the subcategories of AI, the three most frequently mentioned sections are Chatbot, Neural Network/Deep Learning, and Machine Learning. The first subcategory is Chatbot, a software robot that interacts with human would connecting with audiences by personalizing the tour and interacting with audiences through intelligent customer service.

Chatbot Dot, the Akron Art Museum's digital tour guide

Secondly, Machine Learning. Machine learning technology could learn, categorize, and make predictions on existing data. The data analysis could be utilized in facial recognition (artworks' style, color, texture, objects recognition), geographic track in social media, and attendance tracking to show audiences' preference for the future exhibition strategy.

Last but not least, based on machine learning, the Neural Network/Deep Learning is also changing the museum experiences. Neural Network/Deep Learning are computing systems that create new data that looks like an existing dataset that is loosely modeled on the human brain. This technology can be used in archival research in museums. For example, a deep learning system can recognize handwriting Latin old documents and make it digital based on its gargantuan memory bank. Besides, the Neural Network can also change the ways for artists to create art. Generative Adversarial Networks(GANs) could be a great example and are already used in the exhibition "Memories of Passerby I" and a prototype "Generist Maps" made by Met, Microsoft, and MIT. 

prototype "Generist Maps(Gen Studio)" made by Met, Microsoft, and MIT

To conclude, the uses of AI technologies in audience engagement are dataset organizing and recreating, behavior and attendance analyzing, and experience personalizing. To better understand the implementation of AI in museums. There are several challenges that we need to consider. For example, to what extent does the museum intent to implement the technology? As we all know, most of the exhibitions are curator-designed based on an expert perspective. However, the analysis of audiences' behavior, the utilization of personalized touring, and data prediction will enable museums to put audiences' preferences into consideration. Will the trends impact the prestige of museum experts, or will the exhibition become visitor-generated? Those are the questions we can focus on in future research.

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