My last article focused on the recent announcement of Google’s MusicLM, although not accessible to the public, due to copyright issues, it does give one new insights that AI is disrupting the value of human talent in the musical field.
Music has been core to humankind for centuries with the first piece of music, a Hurrian Hymn, discovered in the 1950s on a clay tablet inscribed in Cuneiform text. It’s the oldest surviving melody and is over 3,400 years old. Songs are human’s way of communicating stories and encompassing everything we know of as humans.
Let’s put AI in context to the human art form?
One might logically ask, how long does it take to master a skill in the music profession? Most say that it takes to master a musical instrument 1 to 3 hours per day of study, practice, and rehearsal over 10 to 15 years.
How long does it write a few sentences in Google’s MusicLM to create a new musical song from instruments of your choice – simply now in minutes. If an AI model is trained on all the Vienna Philharmonic musical data sets, and then asked to create a new orchestral piece – how long would it take to produce a new potential master piece by an AI model vs humans? I don’t think I need to answer this question, as I am sure you already know the answer its definitely not in years, but will in hours, vs minutes – at least for now.
Music generating technology like Amper Music and Google’s MusicLM have the ability to read and interpret audio samples. You can not only tell the software what type of music you want, but you can easily instruct AI software models to alter specific attributes like: volume, pitch, tone, accent, instrument selections to achieve the desired outcome. music that you want. Then the AI model not only learns from your choices so as you make future requests it will also remember your selections to continue to produce new forms of music, sensitive to your tastes or you can easily alter them.
There are many pros and cons to AI in the music industry. One may argue that we are inventing a new art form by reducing the time or expenses to create music.
Cons are many in terms of impact to a precious fabric of what makes up our humanitarian society that has been infused with music and stories for centuries. The implication of copyright is enormous as AI only can learn from music and that’s other’s music so the ability to pay for training AI models from musical pieces is a complex and a not well regulated area.
Although many masters of music would argue that AI cannot produce a master piece like humans can, this maybe the case today, but as AI models continue to be refined and fine tuned, they will eventually be on par with human skills to produce music.
Although we now have many new artists like Holly Herndon, called the Godmother of AI Music, and her partner Mat Dryhurst, developed the “baby AI Spawn,” from training data. She had already launched other AI-based songs in her album“Proto.”
What AI models won’t have is the next unique human voice on file to learn from. Perhaps there in lies the opportunity for future singers to ensure their digital identities are securely encoded so a future song cannot be easily produced, and also having cyber security software detect even if a hint of a Beyonce song or beat has been integrated into a future song generating a red alert for musician copyright protection.
In conclusion, it is imperative that the legal and ethical frameworks hasten to protect the music industry on the creative use of AI-enabled tools. Currently producers and musicians either using AI technology are vulnerable as laws will pass and major lawsuits will unfold, and to producers and artists in the music industry, increasing your own knowledge and lobbying for improved laws is key.