Introduction

As I quickly approach my graduation from one of Carnegie Mellon University’s data analytics programs, I’m encouraged to reflect on the key lessons I will take into the work force. One consistent lesson is what it takes to incorporate Artificial Intelligence (AI) into an organization: Carnegie Mellon University (CMU) was ranked number one in 2022 for Artificial Intelligence Programs by U.S. News and World Report. Incorporating CMU’s AI lessons is critical for all industries but particularly those that are historically considered to be “non-technical fields.” Within these fields, the ones who thrive will be those who incorporate at least the lower levels of the stack, namely data management, but an understanding of each layer of the stack will provide industries with a road map to understand how they can increase the efficiency and output of their organization’s business processes.

Explanation of AI Stack

As technology becomes an increasingly large part of the workplace, organizations are encouraged to engage in “full stack” operations. At its heart, this means that organizations should work through developing each level as shown below:

While there are MANY definitions and paths to achieving artificial intelligence within an organization, Carnegie Mellon defines AI as understanding human needs and making “…smart decisions based on that understanding.” Each of the levels in the diagram is achieved through iterative developments. Below is a brief definition of each layer of the stack and key considerations for the arts to consider within each.

Computing & Devices

Defined as “…the collection of computer machinery, data storage devices, and networks that support the processing and exchange of electronic information,” the computing environment is likely the electronics within an organization that everyone will interact with and touch. If computers are too slow, data is strictly stored in old servers, etc, this can cause a shaky foundation for the rest of the stack. MIT defines three areas that make computing and devices different with AI in mind:

1. Carry out calculations in parallel – “speed” historically is how fast a computer could perform a single task (for example, loading a web browser). But now, computers are expected to load a web browser in the foreground while updating a database in the background. Talking to the organization’s IT team about the capabilities of devices (particularly in terms of parallelization) is important in determining the technology expansion capabilities of the organization.

2. “Learning” rather than listening – Historically, computers took a single input from users and executed a task. When we think about devices in the context of the AI stack, however, devices are now expected to learn. In personal devices, this takes the form of voice recognition (Siri) and image recognition (unlocking an iPhone using facial recognition). The application of this in the art world is a greater application to recognize images and sounds. For example, with the proper devices and computing capabilities, museums can implement technology such as determining if a piece of artwork is a fake work or not.

3. Computerization of the commonplace – Think of this aspect of computing and devices in terms of a “smart house.” Modern infrastructures (i.e. storage of data on the cloud) enables things as simple as a doorbell to expand their capabilities to become “smart” technology. If smart technology has a place within an industry, it’s important to first identify if the computing and devices infrastructure can support this change.

Data Management

Data Management is critical to business success. Higher level processing and productivity within an organization cannot happen without proper data management, so in many ways, data management is the most critical level of the AI stack. Data management is defined as “… the practice of collecting, keeping, and using data securely, efficiently, and cost-effectively.”The path to proper data management for different organizations can take many forms, but below is an example process for establishing data management:

The key to data management ultimately comes down to one word:

VALUE

Data management has shown to bring organizations increased value by streamlining process and revealing where to focus business strategy. In the art industry, this could mean greater customer analytics within an art museum to see which populations visit most often, and therefore which populations to target with promotions and deals. For example, if detailed data management and analytics reveals that families with kids between the ages of 2 and 12 make up the majority of museum visitors over the summer, the museum could focus its displays and programs during those months on catering to that population.

Beyond the art industry, civic organizations and local governments have already shown how much value proper data management can generate: by creating the infrastructure for open source data management, the public sector “… has sparked innovation, driven efficiency, and fueled economic development.” Imagine the possibilities for the art industry if more art organizations do the same.

Machine Learning and Modeling

Machine Learning (ML) is an over-used and often misunderstood term. ML is defined as “…the systematic study of algorithms and systems that improve their knowledge or performance with experience” (Akoglu). In plain terms, ML and modeling are advanced math applied to data using computers. The most promising applications for ML in the art industry currently are the automation of business processes (see below), but there are also many examples for how ML is being used to GENERATE art. For more information on this, check out this Ted Talk on music and art generation using machine learning:

Decision Support, Planning, & Acting

This portion of the stack refers to systems that have surpassed the functionality of modern machine learning. The decision support layer simply “helps humans make decisions,” while the planning and acting layer “…rely on optimization, safety, the knowledge network, and strategic reasoning” to learn from information they are given. It’s difficult to understand why and how this layer is different from ML and AI (or why it fits in between the two layers in CMU’s AI stack), but this layer teaches us that the level of task automation and computer “learning” exists on a spectrum.

Autonomy / Human AI Interaction

Human AI interaction is defined as “… the completion of a user’s task with the help of AI support.” While AI can often be an intimidating concept and mistrusted by those who are unfamiliar with it, it has many potential applications within the art industry for automating production, identifying and maintaining consumer markets, and other task completion and automation.

Ethics

A discussion of the AI stack would be incomplete without stressing the importance of ethics in AI development decisions. Beginning with the data management layer, the art industry has the potential to store and track large amounts of personal data on its customers (similar to other consumer industries). The art industry is largely trusted by its consumers, so as its capabilities within the stack expand, ethical design is critical. Large companies within the art industry have identified its necessity and have started to build infrastructure around ethical decision making which can provide a guide for other smaller organizations. For example, Spotify created an Ethics Guild to make ethical considerations “more concrete.” They created a simple Google document open for other organizations to use that evaluates the decisions within the AI and technology stack that different groups within an organization are making as shown below:

How to Bring ‘the Stack’ to the Arts

1. Increase data literacy

Data literacy is defined as the collection, examination, analysis, and interpretation of data to inform a decision. Using data to inform decisions is one of the most important first steps an organization must take before considering any higher parts of the stack. Organizations can do this by heavily democratizing data (ensuring all members of the organization have access to it) and increasing ease of use whenever possible.

To be clear, data democratization is not synonymous with data anarchy; not every member of the organization should have access to all the data. For example, the managerial and janitorial staff of a concert hall may not need access to detailed demographic data on the concert hall’s visitors and guests. However, it would be very useful for them to have visitor frequency data (what times of the year or what types of shows bring in the most visitors) so that they can plan accordingly for peak bust times.

2. Use art itself

In 2016, an education study was conducted in Brazil that looked at how to increase data literacy in young children. After testing different methods, the research team found that those students who learn using visual arts activities performed far better in tests than those who learned through traditional educational methods. The take-away? When looking at how to bring data literacy and eventually the stack to arts workplaces, use art! It’s proven that people respond best learning a new concept through creative methods.

One of the researchers, Catherine D’Ignazio, identified tactics that could help to build data literacy[2] that included building learner-centric tools and favoring creativity over purity. Teaching technical concepts doesn’t require intensely ‘academic’ verbiage or techniques.

Data literacy is intimately intertwined with increased use of visualizations to communicate results. To generate change within an organization or inspire people to action, visualizations are often the best way to captivate an audience. Consider the visualizations below:

Although beautiful in its colors and layout, the visualization has a specific purpose: to educate people on ways child mortality has been reduced historically. Visualizations appeal to a wide range of emotions (similar to art), and often provide information to viewers that would be lost in traditional reports. For example, if you are a manager for a music production company, creating visualizations that show declining sales in the form of a descending scale on a piece of sheet music, this could grab the attention of the audience more because it is shown in a familiar medium. The visualization options are vast, and the benefits of incorporating art into data management strategies are endless.

3. Automate when possible

During the pandemic, 42% of art organizations had to decrease the number of people on their staff, but 65% were still delivering some sort of content. The arts industry must adopt some form of technology for administrative tasks and distribution in order to stay afloat in an increasingly digital world.

Business automation tasks include the following:

• Responding to emails – when it comes to customer feedback, develop a system to automatically sort and respond to any correspondence an organization receives
• Data management functionalities – depending on the data collection and storage methods used by an organization, data “pulls” and back-ups can be easily automated using out-of-the-box software systems
• Customer engagement – social media posts, emails, and other promotions can be setup on a time schedule so that employee time is not taken up by maintaining a customer engagement schedule

There is a plethora of task automation tools available on the internet, and it is important to develop time to determining the best fit between software systems and the organization.

Conclusion

“I say, run toward the data. Take your data back and turn it into something meaningful.”

Laurie Frick, From Paint to Pixels

The utility of the AI stack is evident: the organizations that choose to implement it will succeed, while those who fail to implement will likely cease to exist. Although it seems overwhelming to implement the stack within the art industry, organizations can start simply by focusing on the data. Strive towards data literacy and education. It may be the decision that saves the arts.

References:

“7 Steps to Foster a Data-Driven Culture for Your Organization.” (February 18, 2021). Unscrambl. Retrieved March 8, 2022 from https://unscrambl.com/blog/7-steps-to-foster-a-data-driven-culture-for-your-organization/

Akoglu, Leman. “Lecture 1: Machine Learning: Basic Concepts.” (Spring 2019). Course Notes for 95828: Machine Learning for Problem Solving.

“AI Stack” (2022). Carnegie Mellon University. Retrieved May 3, 2022 from https://ai.cs.cmu.edu/about

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Berkel, Niels van, Mikael Skov, and Jesper Kjeldskov (Nov-Dec 2021). “Human-AI Interaction: Intermittent, Continuous, and Proactive.” Interactions. Retrieved May 4, 2022 from https://interactions.acm.org/archive/view/november-december-2021/human-ai-interaction

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Bhargava, Rahul, Ricardo Kadouaki, Emily Bhargava, Guilherme Castro, and Catherine D’Ignazio. (Oct 22, 2016). Data Murals: Using the Arts to Build Data Literacy. The Journal of Community Informatics. Vol. 12, No 3. Retrieved March 8, 2022 from https://openjournals.uwaterloo.ca/index.php/JoCI/article/view/3285

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The most prolific way that people engaged with music in 2020 was streaming platforms, of which, Spotify continues to be the most popular. Even though it’s easier than ever for artists to upload content to a large audience, the question remains, are personalization algorithms and streaming services through Spotify harming or helping musical artists? Ultimately, the research enclosed concluded that the Spotify algorithm and other personalization algorithms like it help connect artists with more listeners, but just like distribution methods of the past, it does not guarantee musical artists fame, fortune, or even a salary that they can live on.

History of Music Distribution

As mentioned above, music distribution got its start in sheet music. After the Thomas Edison invented the phonograph in 1877, music recording got its start. Standard records as we think of them today gained popularity in the mainstream in the 1920s.

Records maintained their position as the primary means of music distribution until the late 1970s when tapes took over. Walkman portable music players were the first form of portable music, which began a new era in being able to take music with you wherever you go.

In 1991, ‘compact discs’ (CDs) overtook tapes as the primary means for music distribution. On a more personal note, I can distinctly walking through the isles of Best Buy and Target admiring all of the music albums. Cars had nifty CD holders that fit in the sun visors for easy access when you wanted to play a CD in the car. It was pervasive in many of our musical upbringings… Until it wasn’t…

In February of 2018, Best Buy announced that it would phase out all CD sales by July of 2018. This was the direct cause of the rise of streaming. While Napster was the first digital sharing platform, it received a lot of criticism for taking advantage of the music industry and pirating music. As Napster and other sharing platforms fell, Spotify rose to the challenge.

CDs require CD players, records require some kind of record player, and these forms of playing music (while superior in sound quality) are limited (i.e. non-transportable, inability to easily share music, etc). Spotify allows users to freely share music across devices they use on a regular basis (phones / computers), which could help increase artist exposure through social networking. It filled a gap in user desire in 2008 when it was founded, and it has continued to grow since.

In addition to its success in music distribution, Spotify has set itself apart from other music streaming platforms with its algorithm, so much so that features like “the Year in Music Wrapped” playlist has become a part of our culture. Its pervasiveness, personalization, and power are impressive, and it has turned itself into a force to be reckoned with.

Overall, the transition to streaming services is fascinating to follow. The visualization below gives a great picture of the decline of physical means for music distribution since 2001. Streaming took over physical means of distribution as recently as 2017, so a lot still remains to be seen as to the last effects of the transition to streaming.

How the Algorithm Works

Spotify’s personalization algorithm is called ‘Bandits for Recommendations as Treatment,’ or BaRT for short. The first thing the algorithm does is it collects A LOT of data on its users: what playlists are created, what songs are skipped, and the physical location of the user (if it can). The algorithm treats a recommendation as “good” if the user spends more than 30 seconds listening, or “bad” if the user skips the song a certain number of times. In order to recommend new songs, the algorithm uses a similarity function to calculate the “distance” between your preferences and the preferences of others. Many algorithms use a distance formula like the Euclidean distance shown below:

Here’s a simplified example of how it would work. Let’s say Spotify only has four genres of music (country, jazz, folk, and classic rock) and five users as shown below:

Each of the songs that the five users listen to the most are shown in table below:

User5 is a new user, so the algorithm has to determine what songs to recommend. So far, they have listened to mostly songs in the country genre, so the algorithm estimates their position above. Because the distance between user5 and user4 is the smallest, the algorithm is likely to recommend to user5 song9 and song10 (because those are songs that user4 listens to frequently). If user5 skips these songs, it’s possible that the “position” of user5 would adjust:

Now user5 is closer to user3, and the algorithm may recommend song6 and song7. The more user5 listens, the more the algorithm is able to estimate user5’s position and therefore recommend more and more songs.

How Artists Have to Change Strategy in Today’s Industry

There are four main strategies that musicians should consider in today’s world of streaming. While all four may not be for everyone, some combination of the four could result in a larger audience reach and higher revenues.

1. Death of the Album, Rise of the Single.

Christoffer Hylander, known as Killigrew in the music world, created a genre of music known as ‘chillstep’, and he’s made a fulltime living on Spotify doing so. On the podcast, “Music, Money, and Life,” he had some advice for listeners. His first piece of advice was to focus on singles rather than albums: the way the Spotify algorithm works, new songs are often put on playlists like “Discover Weekly” and “Release Radar” to determine how users will like them. Whole albums are therefore less likely to get promoted. In another podcast, vocal editor Alex Krotz details Spotify’s strategy of “boosting” songs. If you opt into Spotify’s boosting program as an artist, you will receive less revenue per song, but Spotify is more likely to include your songs on different playlist recommendations for users. Albeit a risky strategy, it focuses on single songs rather than albums. Therefore, for artists looking to use Spotify as their primary means of distribution, rapidly releasing songs (for example, one a week), it more advantageous than releasing an entire album every couple of months.

2. Find your niche.

In this piece of advice, Christoffer and Alex differ. Christoffer believes that part of his success was finding and focusing on a niche portion of the music market. By creating chillstep, he was able to focus on a specific audience and tailor his music to what that audience wanted to hear. Alex points out that because of Spotify’s algorithm, it is better to create songs like what people are listening to already to get “picked up” by the algorithm. Looking at the Global Music Report for 2021, the Top 10 Global Songs seem to be of a similar vein: artists like The Weekend, Drake, and Billie Eilish appear multiple times. Ultimately, artists must decide for themselves what risks they are willing to take in terms of their style and targeted audience.

3. Sign with a big distributer or do it yourself.

Another piece of advice from Christoffer is the fact that part of the reason he has been able to make a living is he doesn’t have a ‘middleman.’ Every piece of revenue he gets for streams goes directly into his pocket. This is supported by documentation of the payment per stream: before the artist is paid, the songs’ rights holders and distributer are paid. If an artist owns the rights to their own songs, they will obviously receive more money. Therefore, it’s a balancing act: there are benefits to distributers outside of Spotify to include structured publicity, but if an artist is focusing solely on Spotify streaming, being your own distributer may be a better strategy.

4. Diversify your music revenue beyond streaming

The reality is, not all artists can strike it big with streaming like Christoffer has, so it’s important for artists trying to make a living from their music to diversify their distribution. The diagram below shows where people listened to music in the year 2020. If an artist gets 1 million streams in a year, the average revenue will be around $3,500. If streaming only accounts for a quarter of listening, most artists can expect to make $14,000 for their music. This is obviously an over-simplification, but it demonstrates how difficult it is to make a living off Spotify alone. Even though only 2% of listening is live music, this still presents artists with the greatest opportunity to earn cash flow. An additional consideration for artists in today’s world is the rise of virtual concerts. The IFPI Global Music Report details how the pandemic changed the music landscape, and listeners are now more likely to engage in a virtual concert rather than a live one.

The Future of Music Distribution

Ultimately, the future of music distribution is tumultuous. The Spotify algorithm helps to connect artists and listeners, but artists should carefully consider necessary changes in their strategy to survive in today’s music landscape. Spotify promises “algorithmic responsibility“, so perhaps Spotify is conducting business with its customers in mind. Personalization algorithms will continue to shape the music and entertainment landscape in the future, and artists must continue to adjust their strategies accordingly.

Want to Explore More?

Listen to Christoffer Hylander’s (Killigrew) full story on the Music, Money, and Life podcast:

Listen to Alex Krotz on the ANAK Creates Podcast:

Are you a musician who wants more tips on how to get more streams on Spotify? Check out the Best Friends Club blog here!

References

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