YACHTand JENNIFER VANILLA
“We wanted to find a way to interrogate technology more deeply,” explains Claire L. Evans, one-third of the Los Angeles-based pop group YACHT. “From the ground up,” adds her partner and YACHT founder Jona Bechtolt.
The group—rounded out by longtime collaborator Rob Kieswetter—would know: their seventeen-year career has been marked by a series of conceptual stunts, experiments, and attempts to use technology “sideways,” from rock-and-roll PowerPoint presentations to their Webby-award-nominated campaign for I Thought the Future Would Be Cooler (2015), which featured, among other things, a music video that played only during Los Angeles’ rush hour and an album cover that could only be seen via fax. Even the band’s name speaks to this: YACHT is an acronym for Young Americans Challenging High Technology.
CHAIN TRIPPING is the band’s seventh album and third with DFA Records. Recorded between the band’s home in Los Angeles and Marfa, TX, the ten-song collection marks a shift in the group’s relationship with technology and their art. Rather than trying to comment on existing platforms from within their own filter bubble, the band stripped their process down and rebuilt it using a technology entirely new to them—Artificial Intelligence, and more specifically, machine learning. The result is an album that merges invention and intimacy, “challenging” technology from code to content.
In order to compose CHAIN TRIPPING, YACHT needed to invent their own AI songwriting process. It was a journey of nearly three years. They first tried to discover any existing YACHT formulas by collaborating with engineers and creative technologists to explore their own back catalogue of 82 songs using machine learning tools. “We listened to raw audio samples of a neural network trying to sing in Claire’s voice,” says Bechtolt. “We reduced our entire compositional output to MIDI data. We flattened our lyrics into text files, into words among millions that would be fed to a weird custom algorithm. We talked to pioneers of algorithmic composition, and to startup founders who promised us they could reproduce our sound in two hours or less.”
Eventually they created their own working method, an uncharacteristically low-tech approach to a high-tech possibility, painstakingly stitching meaningful fragments of plausible nonsense together from extensive, seemingly endless fields of machine-generated music and lyrics, themselves emerging from models created with the help of generous experts in neural networks, deep learning, and AI. Armed with these custom and custom-tweaked machine learning models, YACHT was able to generate massive volumes of lyrical and melodic information—much of which was “hiding in-between” melodies in their our own back catalogue—which they then approached as source material, using a process more influenced by analog cut-up writing techniques than anything explicitly technological.
“We’re a band that has always loved to operate within constraints, so for this we created a strict set of rules. We were allowed to subtract and rearrange, but never to add a note. Everything on the album is generated, but the structure, arrangement, and performance is entirely ours,” explains Kieswetter.
YACHT have always been multifaceted, willing to collaborate with thinkers far beyond the pop realms. In the past, they’ve created perfumes, apparel, apps (the beloved Los Angeles calendar app 5 Every Day among them) and sculptures, and Evans has a considerable career as a writer; her first book, Broad Band: The Untold Story of the Women who Made the Internet, was published by Penguin in 2018. This project was no different. The list of collaborators for Chain Trippingis a who’s who of the art and machine learning community, from the senior research scientists at Google Brain’s Magenta group to the award-winning creative technologist and poet Ross Goodwin; artwork, typography, liner notes, lyric videos and even YACHT’s band photo were all
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