Data Sculptures

“Who Made Who (Data Sculpture #3)”, 2025, Painted Wood, 22 by 12 by 14 Inches.

Support Vector Machines (SVM) are supervised machine learning algorithms primarily used for classification tasks. Simply put, an SVM finds the boundary that best separates data points of different classes, so points from Group A are on one side of the plane and points from Group B are on the other.

In this sculpture, the data points are the smallest spheres, separated by a plane that is described by the larger spheres. One of the smaller points is distinctly distant from the others, an outlier. In data science, outliers are sometimes removed from datasets to create models which are more accurate for the majority of the data.

I have found that the most interesting places are liminal, the border places where domains, ideas, disciplines meet. As I am slowly learning the ins and outs of machine learning and artificial intelligence, I am trying to reinforce new mental models with old. And along the way, hopefully I’ll find inspiration.

“A Function of Loss (Data Doesn’t Suffer #2)”. 2025 Mixed Media (Wood, Wire, Glue, Nails, and Paint), ~18 by 12 Inches.

The purpose of a loss function is to measure how far a model’s predictions are from the truth—a map that shows how wrong the model is at any given point. This distance guides how much and how fast the model must change to become more accurate. In an idealized world, the graph of a loss function resembles a smooth bowl or a champagne flute: the steep walls represent regions where the model performs poorly, and larger adjustments are needed; the flatter bottom is where the model is nearer to truth, and changes should be smaller and more cautious. Move too fast here, and you will overshoot the goal, passing over the lowest valley and landing on the opposite slope of error.

But in the real world, the geography of loss is not simple or smooth. The landscape is jagged, full of false peaks and deceptive valleys that can lead the model into settling for something that looks like success but isn’t.

In this piece I tried to maintain the integrity of the state of the materials. The wood is worn, rotting, and stained; the nails and wires are bent and rusty in places. The title/quote “Data doesn’t suffer” is from Nick Cave, when he was confronted by song lyrics a fan had created using ChatGPT in his style (The Red Hand Files #218, https://www.theredhandfiles.com/chat-gpt-what-do-you-think/).

"Model Model (Data Doesn't Suffer #1)". 2025, Painted Wood. Approximately 8 by 8 by 1 Inches.

The idea behind this was to make a 3D model of a simplified drawing of an artificial neural network. A sculptural sketch of the kind of diagram that might be scrawled on a white board to explain how an artificial intelligence model learns.

“Data Sculpture (Nude, Reclined)”. 2024. Mixed Media (Wood), 12 by 6 by 12 Inches.

This was another piece playing around with the idea of communicating binary (and ASCII) as part of a sculpture. The larger cubes represent 1’s, the smaller, 0’s, so the piece spells the word “NUDE” ("N" in binary (ASCII) is 01001110, etc.), and is turned on its side, therefore, “reclined.” The rounded piece, and the base are minimally carved, and refers back to “odalisque” paintings, a genre which was popular a couple of centuries ago in Western art. The intention of the was also to add a limited and abstracted level of representationalism, to make it more than just a 3D, binary-encoded, concrete poem. Sort of an aesthetic flourish to the pun.

“IT Repair Manual of Last Resort”, ~2009, Mixed Media (Egg Tempera on Book), 8 by 6 Inches.

“Portrait of Saint Turing”, 2010, Egg Tempera on Panel, 11 by 9 Inches.

“Looking For Satellites (Portrait of Saint Turing)”, 2023, Acrylic on Paper, 6 by 6 Inches.

“Icon of Saint Turing”, 2025, Acrylic on Wood, 9 by 11 Inches.

“Saint Turing (Fortunate Sons#4)”, 2024, Mixed Media on Paper, 18 by 12 Inches.

I’m not sure when I first painted Alan Turing as a saint. It was probably the tail end of the 90’s, and it was probably on a floppy disk. Since then, I’ve done a number of versions, as well as icons for Hopper, Shannon, and other luminaries. Perhaps they weren’t all “saints,” but there is a lot of wisdom to be found in their writings.

In 1950 Alan Turing’s paper "Computing Machinery and Intelligence" was published in Mind. A paper which contains remarkable insights into the possibility of machine intelligence. It asks questions which 75 years later, we are still struggling with, not the least of which is: is it possible for us to build a machine that is “intelligent.” A few months ago, I gave a talk where I happily and confidently stated we (collectively) had not created general artificial intelligence, that this was something still beyond our grasp. Since then, I have seen a flurry of articles where current experts in the field state that we have, or that we on the very cusp of doing so. For the most part, this seems to be a matter of semantic fluidity. But I’m not going to argue the merits of those statements or try to put forward a definite test for sentience, self-awareness, or consciousness. Rather I suggest that perhaps – perhaps – we look back to Turing’s paper and revisit some of the questions he posed, before we go too much further down this road. Because as Turing wrote: “We can only see a short distance ahead, but we can see plenty there that needs to be done.”