Preservation of digital material is a challenge for which many archives feel underprepared and ill equipped. The National Archives (UK) has been working in collaboration with statisticians from the University of Warwick and partners from across the UK archives sector to develop a decision-support system which quantifies the risks involved in digital preservation. Through interdisciplinary collaboration, this partnership has developed an interactive tool for managing risks to digital material, based on a Bayesian statistical network. The tool provides archivists with a different way of thinking about digital preservation, supported by an evidence base they can use to advocate for action. The project illustrates the potential benefit of a collaborative approach, combining insight from different disciplines.
Open AI's language model, GPT-3, has shown great potential for many NLP tasks, with applications in many different domains. In this work we carry out a first study on GPT-3's capability to communicate musical decisions through textual explanations when prompted with a textual representation of a piece of music. Enabling a dialogue in human-AI music partnerships is an important step towards more engaging and creative human-AI interactions. Our results show that GPT-3 lacks the necessary intelligence to really understand musical decisions. A major barrier to reach a better performance is the lack of data that includes explanations of the creative process carried out by artists for musical pieces. We believe such a resource would aid the understanding and collaboration with AI music systems.
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