A few weeks ago, I visited the Westmoreland Museum of Art. There, I saw a piece entitled “Between the Days” by Matt Bollinger, as part of the museum’s show on American realism:
As a painter, I enjoyed this Bollinger’s combination of painting realism with a digital medium to enable a narrative. You see his “hand” in that you have to imagine painting and repainting each frame in a way that you don’t with static paintings.
My own painting practice is nearly entirely private. My finished pieces go either on my wall, on the wall of whoever commissioned the piece, or under my bed. The only exception is that I occasionally send pictures to my loved ones, or –increasingly rarely – post them on social media.
I seek to demonstrate the awkward intermediate steps of creating a painting as a way of expressing and assembling my own quiddity. In this way, people are able to see how a painting develops over time, but also the missteps and context in which it is created.
I first engaged in data archeology to find these progress paintings. This proved to be an awkward endeavor in itself. Not only was it technologically challenging, in extracting digital ephemera from various devices and social media accounts over multiple years, but somewhat traumatizing in seeking for the needles of painting pictures amidst the haystack of past and sad parts of my life.
The result is a time lapse video where each intermediate picture is given 0.07 seconds on the screen. This represents an enormous compression of time – each of the two paintings presented took approximately 6 months each to complete, yet the resulting video is less than one minute. I have not cropped or sought to standardize the images in order to keep their context intact – context kept hidden to viewers of finished work.
At first, without sound, I found the result awkward. However, I found this awkwardness funny, so I decided to lean into this, so recorded a non-verbal but vocalized audio track of my reaction to each frame. In doing so, these grunts, cheers, and urghs provide an optional interpretive frame for the viewer to understand how I reacted to seeing my own process assembled – but I encourage viewers to first watch the video on mute to first collect their own interpretation.
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David Gray Widder is a Doctoral Student in the School of Computer Science at Carnegie Mellon University where he studies how people creating “Artificial Intelligence” systems think about the downstream harms their systems make possible. He has previously worked at Intel Labs, Microsoft Research, and NASA’s Jet Propulsion Laboratory. He was born in Tillamook, Oregon and raised in Berlin and Singapore. You can follow his research on Twitter, art on Instagram, and life on both.
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