The human-machine collaboration created configurations of yarn that you probably wouldn’t give to your in-laws for Christmas, but they were interesting. The user citikas was the first to post a try at one of the earliest patterns, “reverss shawl.” It was strange, but it did have some charisma.

Shane nicknamed the whole effort “Project Hilarious Disaster.” The community called it SkyKnit.

The idea of using neural networks to do computer things has been around for decades. But it took until the last 10 years or so for the right mix of techniques, data sets, chips, and computing power to transform neural networks into deployable technical tools. There are many different kinds suited to different sorts of tasks. Some translate between different languages for Google. Others automatically label pictures. Still others are part of what powers Facebook’s News Feed software. In the tech world, they are now everywhere.

The different networks all attempt to model the data they’ve been fed by tuning a vast, funky flowchart. After you’ve created a statistical model that describes your real data, you can also roll the dice and generate new, never-before-seen data of the same kind.

How this works—like, the math behind it—is very hard to visualize because values inside the model can have hundreds of dimensions and we are humble three-dimensional creatures moving through time. But as the neural-network enthusiast Robin Sloan puts it, “So what? It turns out imaginary spaces are useful even if you can’t, in fact, imagine them.”

Out of that ferment, a new kind of art has emerged. Its practitioners use neural networks not to attain practical results, but to see what’s lurking in the these vast, opaque systems. What did the machines learn about the world as they attempted to understand the data they’d been fed? Famously, Google released DeepDream, which produced trippy visualizations that also demonstrated how that type of neural network processed the textures and objects in its source imagery.

Google’s David Ha has been working with drawings. Sloan is working with sentences. Allison Parrish makes poetry. Ross Goodwin has tried several writerly forms.

But all these experiments are happening inside the symbolic space of the computer. In that world, a letter is just a defined character. It is not dark ink on white paper in a certain shape. A picture is an arrangement of pixels, not oil on canvas.

And that’s what makes the knitting project so fascinating. The outputs of the software had to be rendered in yarn.

Knitting instructions are a bit like code. There are standard maneuvers, repetitive components, and lots of calculation involved. “My husband says knitting is just maths. It’s maths done with string and sticks. You have this many stitches,” said the Ravelry user Woolbeast in the thread about the project. “You do these things in these places that many times, and you have a design, or a shape.”