For most people, Artificial Intelligence brings to mind a futuristic, sci-fi scenario of autonomous robots or machines capable of making their own decisions, and more often than not, resulting in the demise of their human counterparts. For now, the applications of AI are less apocalyptic, like helping drones spot dog poo on footpaths , turning Robert DeNiro German and proving who wrote the Dead Sea Scrolls . WIRED’s excellent AI Database is a good place to look for hundreds of examples: some novelty, some sinister, all fascinating.
In this three-part series, which will run over the next three days, we’re going to explore the potential impact AI is having and will have for modelling an artist’s likeness, how producers and engineers work in the studio, what it means for DJing, and how the hyper-personalisation of our online experience could soon migrate to the way we experience music.
Though many of the concepts touched on in this series are already in motion, their cultural impact is largely yet to be felt, leaving us staring down the barrel of a contradiction. Inevitability usually implies certainty, but the nature of the tech means it’s almost impossible to accurately predict the consequences. What we do know is that the genie is out of the bottle.
In music, AI, and more specifically machine learning (ML), is quietly emerging as the Black Box behind almost all of our interactions with music online. In fact, most of us have unknowingly been using AI- and ML-powered technology for years. Music listening platforms like YouTube, Spotify, Apple Music and Pandora use AI to perfect our experience on their respective services, like recommending us the perfect track to play next, eliminating dead air and adjusting volume in real-time.
"I think this is going to be one of the biggest ethical conversations in music going forward, and we’re so not ready" — Holly Herndon
Machine learning is an arm of AI that essentially teaches a machine how to learn. It uses ‘training data’ to spot patterns and uses those patterns to build a model based on that data. Deep learning is a subset of machine learning that, once the model is built, can continue to improve without human intervention, by gathering more data from how it’s being used and further tweaking its output.
Spotify’s Discover Weekly is the most obvious example of this. Apple’s voice assistant Siri is another; its synthesised speech is learned from real-world recordings , and it better recognises your voice over time. (For the purposes of this article, we’ll use ML as a catch-all term for both machine learning and Deep learning.)
But it’s not just recommendations that ML can attempt to master. A form of ML has been used since the 1980s to generate music, when composer David Cope trained a computer on Bach’s catalogue in an attempt to beat writer’s block. More recently, artists like Actress and Holly Herndon have been training models and building data sets on their own music, vocals, stems and style, in an attempt to create a virtual collaborator modelled in their own likeness.
Plugins and music-making software have also begun to adopt ML, with iZotope’s Neutron welcoming a new era for how we produce music in the studio. More recently, Splice, Loopmasters and more are using ML to recommend new samples to improve your track, and allow you to scan their libraries of millions of sounds based on more abstract attributes like harmonic profile and tone. Apps like Endel and AIMI have used stems from collaborators like Richie Hawtin, Grimes, Black Loops and Shanti Celeste to create personalised generative music that never repeats, and never ends.
DJing hasn’t been left untouched by ML, either. VirtualDJ and Algoriddim’s djay software have introduced real-time stem separation powered by AI. DJ software’s AutoMix functions also use ML to understand how a song blends with the next, offering the perfect automated DJ for your next after party.
