How AI is shaping music, art of the future

Responding to the viral AI-created version of Munch, Drake wrote on Instagram, "This is the final straw AI."

The renowned rapper Ice Cube stepped into the conversation to express his own concerns, urging Drake to sue the person who made another cloned Drake sound on a now-viral track called "Heart on My Sleeve."

"I don't wanna hear an AI Drake song. Yeah. I don't wanna hear that [expletive]. He should sue whoever made it," the 53-year-old rapper said while on a podcast.

The Guardian reports that the fake song, which features The Weeknd's voice too, was pulled down from streaming services by Universal Music Group.

"The label condemned the song for 'infringing content created with generative AI'," The Guardian notes, adding, "The track was originally posted on TikTok by a user called Ghostwriter977 and shared on streaming services under the artist name Ghostwriter. By the time it was removed, it had racked up 600,000 Spotify streams, 15 million TikTok views, and 275,000 YouTube views."

Universal Music Group representatives said in an interview with Billboard Magazine that the viral postings "demonstrate why platforms have a fundamental legal and ethical responsibility to prevent the use of their services in ways that harm artistes."

As the concerns about infringing copyright laws and damaging artiste's brands continue to come up, so does the use of AI to generate music and art continue to rise.

Clone sounds

And it's surprisingly easy for anyone to create these clone sounds, as detailed by the music website MusicRadar.

"The majority of people creating these covers are using open-source software called SoftVC VITS Singing Voice Conversion, or So-VITS-SVC, to process vocals. This is an AI-powered deep learning model that can be trained by using audio files of any vocal timbre to convert vocal recordings into the singing voice it's been trained on."

The report adds: "It isn't the only model available that can do this, but it's currently the most popular. By ripping the vocal stems from an artist's recordings and using these to train So-VITS-SVC, users can teach the software how to process any vocal recording to sound like the artist in question."

It might sound like harmless fun and a way for fans to get more creative, but artistes and industry stakeholders have continued to sound the alarm over possible repercussions of AI to music.

"Unchecked generative AI poses many dangers," Universal Music Chief Executive Lucian Grainge told investors in April.

Financial Times reports, "Universal Music recently sent a letter to all the leading streaming platforms warning them against allowing AI technology to train itself on copyrighted music."

It adds: "There are a few reasons for such concerns. The first one is obvious: copyright infringement. An AI-generated fake Drake can only sound like the star because it learned to do so by listening to Drake. So the music companies argue Drake should receive some of the money these songs earn."

listener habits

The report further notes that music executives are working on finding a workable model around the use of AI in music, and it might look like all AI-generated music is being syphoned to a different platform entirely, while professional music may be kept to premium services.

Robert Abelow, the founder of Where's Music Going tells Complex that listener habits may change in the future.

"We may see a paradox movement back to an artist and originality focus for listener habits. Artists over songs. You must stand out. You must connect. Authenticity and originality as a premium."

All creative industries will feel the impact of AI, as has already begun to happen with art.

In March, images of the Pope dressed fashionably in a puffer jacket went viral, confusing millions of viewers as to whether it was real.

"The AI-generated images of Francis appeared to originate on a subreddit dedicated to the AI programme Midjourney on Friday and were later widely circulated on Twitter," NBC News reported.

The report called the images "one of the first instances of wide-scale misinformation stemming from artificial intelligence."

These realistic images are not the first to be widely shared and to strike up conversations online, as the use of AI to create viral images has been increasingly popular this year.

On social media, you will likely come across listicle Instagram reels and TikTok videos of AI-generated images, from "AI images of beautiful women in East Africa", to "AI images of what angels portrayed in the bible look like", and it goes on and on.

In January, realistic-looking images of women at a party went viral.

Business Today reported: "Step into a world where the guests at the party are not quite what they seem. Meet the AI-generated ladies who have taken the internet by storm, proving that in the digital age, anything is possible - even non-existent women going viral."

Tech experts have raised questions on how the public can differentiate between what is real and what is fake as the tool gets more popular.

"Henry Ajder, the AI expert and presenter of the BBC radio series The Future Will be Synthesised, said that this technology has developed with 'lightning speed' within the last year, and shows no signs of slowing down," UK's iNews reported.

"Not only have the tools become "radically accessible" and easy for anyone to use, but they have also become more sophisticated - generating images that look increasingly realistic."

Deep Fakes

"Making realistic fake videos, often called deepfakes, once required elaborate software to put one person's face onto another's. But now, many of the tools to create them are available to everyday consumers - even on smartphone apps, and often for little to no money," wrote New York Times in a piece titled 'Making Deepfakes Gets Cheaper and Easier Thanks to AI'.

There is a positive side to the use of AI in art, however, as visual arts and design school Aela writes on its website.

"AI tools and techniques allow artists to create works in a fraction of the time it would take using traditional techniques. For example, GANs (Generative Adversarial Networks) Generative Adversarial Networks can generate realistic images in a matter of seconds, whereas manually creating a similar image could take hours or even days."