What AI-powered design could look like with no data scraping
Designers Domenico Di Paolo and Kieran Feechan built a deep listening device that runs locally and on artist-trained models to show what electronic music could look like if technology actually respected creative work.
An interesting, design-driven response to some of AI’s biggest issues, such as data scraping, energy consumption, and the systematic disregard for creative work, comes from two young graduates of Central Saint Martins, London. And it’s not another manifesto, but an actual object that uses AI in a very different way, with a specific, anti-extraction attitude. Like all innovative items, IMAGO is hard to categorize.
Part instrumental synthesizer, part deep listening device, it was designed and built by Domenico Di Paolo and Kieran Feechan – designers as well as electronic music enthusiasts – and is currently on show at the CSM’s Graduation Show (18–21 June), after appearing at Alcova during Milan Design Week. It was developed in conversation with a community of musicians, technologists and researchers focusing on the big question that shakes the whole creative community: is there a way to take what’s genuinely interesting about AI and firmly reject the rest?
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IMAGO looks, loosely, like a record player: a circular interface, a disc you place on it with the same deliberate gesture you’d use with vinyl. But what happens when you place that disc is nothing like playing a record. That small aluminium puck is actually a key: it contains an NFC tag that unlocks an AI model trained entirely on sounds provided, willingly and specifically, by a musician.
Once unlocked, you can move through those sounds with your fingers and shape them into something entirely your own. There is no scraping, no cloud, no data going anywhere in this act because the whole thing runs offline, on the device. And what you hear when you play it you will never hear again.
Despite being digital and running on AI, IMAGO basically is and does all the exact opposite of what digital technologies and AI got us used to…We took a closer look at the project with the two designers, Domenico and Kieran.
IMAGO – Interview:
What exactly is IMAGO and how does it work?
Kieran Feechan:
It’s a musical device that intentionally blurs the lines between an instrumental synthesizer and a deep listening tool. A small dedicated aluminium disc contains the music tracks. When placed on the device, with a gesture similar to putting a record on a turntable, it acts as a decryptor and activates the machine learning model inside. Without it, the model is totally inaccessible; you can’t extract it from the device. Once you place the disc, the composition starts.
The music is made up of three layers (bass, drums, guitar, for instance) that appear as lit-up spots on the circular white interface. As you move them around with your fingers, you change both the composition and explore the sounds of the dataset.
Domenico Di Paolo:
The white circle is basically a sound map. When you interact with it, you are playing with clusters of sound grouped by similarity. We could have shown this visually, but we wanted the user to reconstruct a mental image of the artist’s world by listening and to navigate it by ear rather than by eye.
That’s the deep listening idea and it comes from our shared passion for electronic music and the Japanese domestic hi-fi tradition, where many homes have a space dedicated entirely to listening. Respecting music means having it not as background but as something close to your body that brings you into a meditative space. IMAGO, which you can hold against your body as you play it with your fingers, was born from the idea of bringing that experience into a domestic environment.
So what the user hears isn’t the artist’s music, exactly?
Domenico Di Paolo:
The best way to describe it: the artist builds a training process for the machine and a sound palette, hence a dataset of recordings. When we train the model on this material, it reconstructs all those sounds into a multidimensional sound map. The output is basically an instrument.
And here comes the product design element: we developed a device that hosts that instrument locally, gives it tactility, keeps it fully offline, and allows both the user and the artist to engage in a conversation of creating music. That’s the act of co-composition. They both play a part in the output, which is totally unexpected and constantly changing. And what you hear, you will never hear again. The system and the rules are so complex and in flux that you really have to be present to get it.
How did you involve artists in the project?
Kieran Feechan:
We got in touch with a community of musicians in Paris and London who had been experimenting with machine learning and neural tools for a while. Artists with very different practices but who share our vision for a respectful use of music datasets, exploring what technology can do while rejecting data scraping and extraction. For instance, we worked with French DJ and electronic music producer Canblaster and with Rob Leidlow, a composer for the BBC Philharmonic.
Domenico Di Paolo:
Our position was: the dataset you develop is your creative act. And artists responded in different ways. Throwing Snow, an artist we’re working with on the latest dataset, took sounds from his personal archive and composed new material specifically for IMAGO. Rob Leidlow curated a selection from a BBC Philharmonic archive from the late forties and fifties with no copyright issues and we trained the model on that. Different approaches, but in every case the dataset is a creative act.
Why is it an object and not an app?
Kieran Feechan:
The offline hosting has ecological benefits and really helps the experience. But it’s also, massively, a matter of privacy. If we made these models available online or through an app, the artist’s data would be quite at risk. With the physical device, you quite literally have a key, and the key is the puck. Without it, everything stays completely secured inside the device with no way to extract it.
Domenico Di Paolo:
Having an object that works locally, with a small-scale dataset, on a specific function, I think is really the key to how AI objects are going to develop in the next years.
With regard to this, should IMAGO be considered a political gesture?
Domenico Di Paolo:
There is absolutely a political take on the state of AI in this project, just as there was in previous ones we developed together. We are passionate about the work of thinkers such as Kate Crawford and Yuk Hui, who focus on data scraping, cosmotechnics, and the need to localize technology.
Kieran Feechan:
There is, very rightfully, a strong public opinion against machine learning in music and we think it’s more justifiable than ever. The biggest issue is mass scraping: artists’ work taken without consent is a real problem. Rebuilding the method of working with an artist, treating it like a creative act, is an attempt to reconstruct that system from the ground up. It’s a small-scale gesture, but one that goes in a direction artists appreciate.
Has this changed how you think about what design can do?
Domenico Di Paolo:
When you work within such a controversial field as artificial intelligence, you need to be realistic about what you can and can’t do. For us it’s more about charting a trajectory for our own practice. We’re aware we can’t affect the bigger system, but with this project we’ve really created a community of debate around Paris and London. And I think that’s the most important thing: to try to create a network, and then translate that network into design action.
Kieran Feechan:
It’s sort of like a small protest: creating little local technologies as almost a counterpart to the big platforms and high-growth tech startups becoming fully integrated into daily life. That’s what the research pointed to. And honestly, it’s become the thing we keep coming back to in our work.
About the author
Journalist, DesignAtLarge founder, internimagazine.it curator, scientific committee of the Compasso d’Oro Award member, Circular Design professor at NABA Milan.
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