Can AI replace human musicians? The rise of the AI Musician
AI AND INNOVATION
In this blog post, we explore were AI technology is and where is headed in the music industry, and whether there is a need to worry just yet.
Artificial Intelligence (AI) has been the hottest topic as of lately, everyone has heard about it by this point, making waves in almost every industry, and the music industry is no exception.
With AI constantly evolving and being capable of composing and producing music at a fraction of the time and cost it takes human musicians, many are starting to question:
“Can AI replace human musicians?”
The short answer to the question would most likely be: Yes, it can… but…
We’re not quite there yet, and we’re still seeing the “AI musician” in its infancy stage, with a whole journey to accomplish before it can even dream of replacing a human musician.
So let’s try to evaluate it’s journey, where it’s at, and where it might be heading:
Where is AI at the moment?
Artificial Intelligence (AI) has been exponentially growing and making significant strides in recent years.
Even though the concept of Artificial Intelligence isn’t anything new, we can notice a huge explosion in its development, specially with the popularization of new language and image processing models such as ChatGPT, Dall-e, MidJourney, just to name a few.
To briefly summarize where we are at, we could divide the development of AI technology into 3 phases:
Rule-Based AI: In this first phase, AI was developed using specific rules and algorithms to solve problems.
The technology was limited in its ability to process and understand data, and it was only able to perform specific tasks that had been programmed into it.
Statistical AI: In the second phase, AI began to utilize statistical techniques to process and analyze data.
This allowed AI to learn from the data it was processing, and to make predictions and decisions based on that data.
Deep Learning AI: In the third phase, AI is utilizing deep learning algorithms, which are capable of processing and understanding vast amounts of data and generating highly sophisticated results.
With deep learning, AI is able to understand the relationships and patterns in the data it processes, and to make its own predictions and decisions based on that understanding.
We are already at Phase 3 of AI development.
So from here, we can expect AI to grow even faster, since it can start learning from it’s own data and expand on it.
We are now past the point where we look at AI from a mere development level. It is now more relevant to start evaluating AI based on what it is capable of doing in relation to a human being.
For this we could also divide AI capabilities in 3 categories:
Narrow AI: Narrow AI, also known as Weak AI, is a type of AI that is specifically designed and trained to perform a specific task, such as recognizing speech or playing a video game.
It can only perform the task it was designed for, and it cannot be used for other tasks. (Ex: virtual personal assistants, recommendation systems, image recognition sofware.)
General AI: General AI, also known as Strong AI, is a type of AI that can perform any intellectual task that a human can do.
It can reason, solve problems, understand language and even learn. We could debate we’re reaching this point, but (as of now) the AI we see today is still considered narrow AI.
Super AI: Super AI is a hypothetical type of AI that is far more advanced than even general AI, and is capable of surpassing human intelligence in every way.
While this type of AI is not currently in existence, it is a topic of much debate and speculation in the AI community.
In terms of music, AI is currently in the stage of Narrow AI (Stage 1), meaning that it is capable of performing specific tasks related to music creation, such as generating melodies, creating harmonies, and even composing entire songs.
After all, the composition part of music-making is based upon very precise and mathematical music theory that has existed for centuries, so it isn’t too shocking to see AI being able to somewhat replicate these capacities.
However, it is still far from achieving the level of creativity, expression, and emotion that human musicians are capable of.
While language models like ChatGPT can show how close we are to General AI (Stage 2) when it comes to replicating human language, there is still a lot of barriers to overcome to achieve the same effect in more emotionally-driven sectors of society, which is the case of arts, and in the case of this article, Music creation.
Which leads us to:
The Limitations of AI in Music Creation
One of the main limitations of AI in music creation is its inability to truly understand human emotion and expression.
While AI can analyze and generate patterns and structures based on existing music data, it lacks the natural and organic ability to truly capture the nuances of human emotion and expression that are integral to human music creation and performance.
Another limitation is the lack of true creativity and originality.
While AI can create new melodies and harmonies based on existing data, it cannot truly create something that is completely original and unique, as it is limited by the data it has been trained on.
There are several examples of AI-generated music that is trying to be original, and most of them are still...
Not there yet... to say the least.
The more AI tries to stray from the already established rules of human composition to make something new, the more unnatural and robotic it sounds.
This is because AI lacks the ability to truly connect with an audience in the way that human musicians do.
Music is not just about creating something that sounds good, but it is also about connecting with listeners on an emotional level and creating an experience that is memorable and impactful, and above all:
So, as of now, it’s still safe to assume that AI is very dependent on human creativity and input to truly flourish, and behind every great song where AI has it’s influence, there will be an extremely creative human being pulling it’s strings (pun intended) to make the best music it can ever create.
Will AI replace human musicians?
The question of whether AI will replace human musicians is a complex and controversial one, with no clear answer.
While AI has made significant progress in music creation and production, there are still many factors that prevent it from completely replacing human musicians.
On one hand, AI is capable of composing music at a fraction of the time and cost it takes human musicians, and it can generate music that is “technically proficient” and sounds “somewhat” good.
In some cases, AI has even been able to produce music that is difficult for human musicians to replicate. (for better or worse)
On the other hand, AI lacks the creativity, expression, and emotional resonance that are integral to music creation and performance, being incapable of truly understanding and capturing the nuances of human emotion and expression that are so much needed to music creation and performance.
There are also many important questions to consider, such as the ethical implications of replacing human musicians with AI.
Many musicians argue (rightfully so) that music is not just a product, but an art form that requires human creativity and expression.
In a world where AI is increasingly replacing human labor, some argue that it is important to prioritize human creativity and expression, and to ensure that there is still a place for human musicians in the music industry.
Overall, while AI has made significant progress in music creation and production, it is unlikely to completely replace human musicians in the near future.
However, as AI continues to develop and evolve, it will be interesting to see how it will impact the music industry and whether it will change the way we create and consume music.
We can conclude with this, that human musicians won’t simply be replaced anytime soon, and AI-generated music will be, in the near future, just another tool in the already vast amount of tools available to be leveraged by skilled and creative humans to take music creation to another level.
However, there are good arguments on both sides of the argument, and they both deserve to be explored: