Note — Mar 01, 2020

On Distortion

Seen in → No.115

Source →

Nick Foster and Simone Rebaudengo making a useful parallel between distortion in guitar amps and distortion in machine learning results. In guitars it was an initial defect that was appropriated by musicians to become part of performances and styles, perhaps the same is happening with the imperfections of machine learning results as artists and coders appropriate them for their own purposes. I also endorse their choice to use machine learning and machine intelligence instead of artificial intelligence.

In all of these examples, there is a common theme: a focus is on perfection, of a pixel or note perfect reproduction of the real world, or at least a world so believable that it feels ‘real’. As these processes are being developed however, they stumble and fumble en route to this ‘perfect’ state, arriving with glitches, artifacts, blips and smears. […]

There’s clearly a precedent here, but what we’re observing is a willful tweaking of Machine Intelligence systems to find and then break their edges, just as Jackie Brenston did with his guitar amplifier. Systems intended for perfect reproduction or synthesis are being explored, messed with and teased into generating new, undefined, and hitherto undesirable outcomes. […]

The fluid, childish watercolor aesthetic of semantic images synthesis, (created by the insecurities of the neural network in interpreting human input) is rapidly becoming its own aesthetic, and as viewers, we are also developing a sensitivity, and perhaps an attraction to it, an evolution of the New Aesthetic as described by writer and artist James Bridle. […]

We prefer the term ‘Machine Intelligence’ as it allows these technologies space to breathe, to be unencumbered from comparison, and allows the technology to show its own grain, its own vagaries and find its own direction.

More → One of the types of imperfections mentioned is “puppy slugs,” have a look at my friend Boris’ piece on the topic.