Project Description

Glow Box

A project by Yeseul Song & Michael Simpson

Nature has inspired mankind as long back as history is recorded. Among other things, our tools, techniques, and even aesthetics derive so much of their form and function from the clever decisions arrived at through centuries of evolution. In more recent years, Nature has become a direct source of strategy for designers and researchers tackling some of the world’s most challenging problems. The neural network, a core component in deep learning, is no exception to this grand tradition. In their simplest form, a neural network models the high level behavior of neural perceptrons. Essentially, they implement the basic functioning of a living brain. This technique has become invaluable in resolving problems that were once extreme challenges to quantify and compute.

But, categorically, what is a neural network? What do they look like? Do they think like we do? The ubiquity of these AI techniques begs for a more critical and creative understanding of the algorithms themselves. This project strives to do so by presenting viewers with a real-time window into the mind of a neural network as it repeatedly attempts to prove a simple equation. In particular, Glow Box evokes curiosity and, potentially, a questioning of the role for analog-digital hybrids as they inch closer and closer toward invalidating accepted definitions of sentience and free will.

Glow Box is a hybrid object which imbues life into the object by displaying a real-time visualization of a neural network as it works to solve problems. The installation exploits the organic-like nature of the neural network algorithm and combines this with the almost magical ability of the physical object to appear illuminated without apparent electricity. The result is something which blurs the distinction between real and virtual imploring the viewer to question this distinction altogether.

Press KitVideo Documentation

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