AI is about to shake up music forever – but not in the way you think

Take a hike, Bieber. Step aside, Gaga. And watch out, Sheeran. Artificial intelligence is here and it’s coming for your jobs.

That’s, at least, what you might think after considering the ever-growing sophistication of AI-generated music.

While the concept of machine-composed music has been around since the 1800s (computing pioneer Ada Lovelace was one of the first to write about the topic), the fantasy has become reality in the past decade, with musicians such as Francois Pachet creating entire albums co-written by AI.

Some have even used AI to create ‘new’ music from the likes of Amy Winehouse, Mozart and Nirvana, feeding their back catalogue into a neural network.

Even stranger, this July, countries across the world will even compete in the second annual ‘AI Song Contest’, a Eurovision-style competition in which all songs must be created with the help of artificial intelligence. (In case you’re wondering, the UK scooped more than nul points in 2020, finishing in a respectable 6place).

But will this technology ever truly become mainstream? Will artificial intelligence, as artist Grimes fears, soon “make musicians obsolete?”

To answer these questions and more, we sat down with Prof Nick Bryan-Kinns, director of the Media and Arts Technology Centre at Queen Mary University of London. Below he explains how AI music is composed, why this technology won’t crush humanity creativity – and how robots could soon become part of live performances.

Music AIs use neural networks that are really large sets of bits of computers that try and mimic how the brain works. And you can basically throw lots of music at this neural network and it learns patterns – just like how the human brain does by repeatedly being shown things.

What’s tricky about today’s neural networks is they’re getting bigger and bigger. And they’re becoming harder and harder for humans to understand what they’re actually doing.

We’re getting to a point now where we have these essentially black boxes that we put music into and nice new music comes out. But we don’t really understand the details of what it’s doing.

These neural networks also consume a lot of energy. If you’re trying to train AI to analyse the last 20 years of pop music, for instance, you’re chucking all that data in there and then using a lot of electricity to do the analysis and to generate a new song. At some point, we’re going to have to question whether the environmental impact is worth this new music.

I’m a sceptic on this. A computer may be able to make hundreds of tracks easily, but there is still likely still a human selecting which ones they think are nice or enjoyable.

There’s a little bit of smoke and mirrors going on with AI music at the moment. You can throw in Amy Winehouse’s back catalogue into an AI and a load of music will come out. But somebody has to go and edit that. They have to decide which parts they like and which parts the AI needs to work on a bit more.

The problem is that we’re trying to train the AI to make music that we like, but we’re not allowing it to make music that it likes. Maybe the computer likes a different kind of music than we do.