Saturday May 4, 5-7pm
To some, art derived from digital media may conjure the automated, rigid, and impersonal, devoid of expression or depth – but the work of the artists presented here demonstrate that the opposite is true. Dynamic and unpredictable, in many ways the code-driven work shown has more in common with organic systems than traditional art media. In the age of data, algorithms digest and transform information in surprising ways. GANs (Generative Adversarial Networks) and other machine learning systems are “trained” on data that comes from us. As a result, the compelling and often bizarre forms they conjure have much of us in them. The outputs of generative work are different with each iteration, yet they maintain a strong fingerprint of the artist in how they are coded, trained, and rendered.
Anna Ridler trains neural networks on carefully curated inputs which feed back on themselves, iteratively transforming both the GANs processed output and her own handmade source material in the process. Xiaohan Zhang programs sublime abstractions of light, space, and velocity, which respond dynamically to viewer interaction in complex and subtle ways. Jonathan Cherneff’s generative drawings shuffle thousands of lines into crystalline compositions that are more than the sum of their parts, and virtually never repeat. Shawn Towne’s kaleidoscopic compositions appear as organic expansions of light.
If technology represents an ability to engineer the physical world with control and precision, these works invert that attitude and celebrate the surreal, unanticipated, and elusive. But their content is not random or arbitrary -- built from lines of code, each piece is forged in a medium that is intrinsically deliberate. In that in-between space, the artists strike a balance between Boolean logic and organic drift.