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A collection of over 250 uses for artificial intelligence

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Personal or genre music playlist creation

Introduction

This paper aims at analyzing the impact of the digital streaming platforms on music industry. In the recent past, people have changed the way they consume music due to the availability of various online platforms. No more doing it the old way of physically creating mixtapes or burning CDs; it is now a different world with algorithmically generated playlists. This paper focuses on how ML and AI have come to contribute in generating personal or genre music playlists. It has the capacity to work with large sets of information and recommend a song which the listener might want to here next, given their history and preferences. This is a shift in the paradigm of music consumption and transmission as it provides individualized service to listeners.

Challenges

There are, however, a number of barriers that must be addressed in order to realise the potential of AI and ML in creating music playlists. The first one is to understand and predict the personal music taste preferences which is rather a subjective process. It is not only limited to the parameters like the genre, artist, or the song’s popularity but it also includes the listener’s mood, context, and personal preferences. Secondly, it is difficult to keep the playlist relevant as people may easily get bored of listening to the same songs over and over again. Thirdly, the problem of how to add new songs to the playlist and play familiar ones at the same time is also an issue. Finally, there is the problem of copyright and how to handle it, including how to make sure that artists are properly paid for their music.

AI Solutions

There are challenges with regards to data privacy, algorithmic bias, and lack of diversity in the music industry. This has made AI and ML to offer solutions to these challenges. Some of the companies that have embraced these technologies include Spotify and Pandora in their playlist generation process. This has been seen with Spotify’s ‘Discover Weekly’ and ‘Daily Mix’ playlists which are a clear indication of how the company has leveraged on AI and ML. They apply collaborative filtering, an ML approach, to break down user’s behavior and similarity of songs. Pandora on the other hand uses the Music Genome Project which is a system that involves humans in the process of assigning tags to songs based on specific characteristics and then using artificial intelligence to identify patterns and offer recommendations. In addition, AI can also help in keeping the playlist up to date as it will always be analyzing new music releases and the user’s behavior and it can do this while trying to balance between the familiar and the new by using reinforcement learning which is a process of rewarding the system for making right recommendations.

Benefits

There are several advantages of applying AI and ML in playlist curation. It makes the music experience more specific to the user and therefore increases the chances of the user engaging with the platform and coming back. It also introduces listeners to music that they might not have found on their own which is a plus for both the listeners and the artists. Also, it enables music streaming services to grow their business and expand their operations without having to employ more and more curation teams. Finally, understanding the listener’s behavior, the companies can find out what the listeners prefer and like, as well as trends that may appear in the audience, which is crucial for marketing and strategic planning.

Return on Investment

There are significant return on investment when using AI and ML in creating music playlists. For streaming companies, more engaged listeners translate to more subscriptions and ad revenue. Personalized music recommendations have also been known to increase customer retention thus reducing churn rate and increasing customer lifetime value. For artists, getting a spot in popular playlists can greatly improve their exposure and streams and in turn increase their earnings from royalties. However, it is difficult to measure the ROI in a precise manner as it encompasses both qualitative and quantitative returns and the effect may be different based on certain factors such as the specific use of the AI and ML.

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