Abstract
The application of artificial intelligence (AI) to music stretches back many decades, and presents numerous unique opportunities for a variety of uses, such as the recommendation of recorded music from massive commercial archives, or the (semi-)automated creation of music. Due to unparalleled access to music data and effective learning algorithms running on high-powered computational hardware, AI is now producing surprising outcomes in a domain fully entrenched in human creativity—not to mention a revenue source around the globe. These developments call for a close inspection of what is occurring, and consideration of how it is changing and can change our relationship with music for better and for worse. This article looks at AI applied to music from two perspectives: copyright law and engineering praxis. It grounds its discussion in the development and use of a specific application of AI in music creation, which raises further and unanticipated questions. Most of the questions collected in this article are open as their answers are not yet clear at this time, but they are nonetheless important to consider as AI technologies develop and are applied more widely to music, not to mention other domains centred on human creativity.
Highlights
Artificial intelligence (AI) is a well-established discipline of computer science focused on making computers perform tasks that would normally require human intelligence (Russell and Norvig 1995).Due to the convergence of massive data availability, computational resources and novel deep-learning-based architectures, the machine learning (ML) sub-field of AI has experienced major breakthroughs over the past decade (Goodfellow et al 2018)
The engineering of AI systems can benefit by working in transparent ways as well, e.g., clarifying the reasons why specific metrics are used and the societal values which underlie them (Glymour and Herington 2019; Kilbertus et al 2017); describing the processes of data collection and use, which may result in discrimination (Hand 2018); even improving the composition of a research team, where a lack of team diversity can compound the impact of the problems mentioned above and reinforce blind spots (Ruiz et al 2002)
Relevant initiatives in ML research are centred on developing AI systems that are not just accurate, and fair, accountable, transparent and interpretable
Summary
Artificial intelligence (AI) is a well-established discipline of computer science focused on making computers perform tasks that would normally require human intelligence (Russell and Norvig 1995). A tangible example of many challenges and open questions of music AI is given by the folkrnn project (Sturm et al 2016).16 This project has built and trained several music AI models on data produced from tens of thousands of transcriptions of folk music available online.. See for example these articles: https://www.motherjones.com/media/2019/03/what-will-happen-when-machines-writesongs-just-as-well-as-your-favorite-musician/; https://www.theverge.com/2019/4/17/18299563/ai-algorithm-music-lawcopyright-human; https://www.theverge.com/2018/8/31/17777008/artificial-intelligence-taryn-southern-amper-music. This perspective shifts the focus in engineering from developing music AI, and the incentives of doing so, to surveying the impact of such technology and its development, intended or not, and the implicit assumptions made in performing such work. There are no easy answers to many of the questions raised in this article, but they must continue to be identified and formalized to assist their future discussion
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