The Pollen Series has been exhibited at Current States of Being: Exploring AI’s Influence on Memory, Identity, and Creativity; at Saint Lawrence University’s Richard F. Brush Art Gallery.

Pollen was once imagined to have agency, because it moved above the surface of water as if by its own intention. In 1827, a scientist named Robert Brown baked some pollen – killing it – and dropped it into water, to see if it still moved. It did. Sentience, despite all illusions, was taken off of the table.

In 1905, Albert Einstein went for a walk along a lake and observed pollen on the water's surface. His realization that what had once been deemed "intelligent behavior" from pollen was the result of complex flows of unseen bits called molecules. That insight would lead to his first major scientific publication. He called this idea – that pollen and other particles spread out further over time – a diffusion equation.

In generative AI there are diffusion models, and they're tangentially related. In AI, diffusion models refer to the spreading out of noise within an image in a process that moves information further from the source in every step. AI borrowed science for a metaphor, as it always does, to explain this spreading of noise throughout the images used for training data. Knowing how it spread, you could denoise that spread to restore the image. Start with random noise, and it would correct the noise to make new images out of related concepts.

Noise, in computer systems, creates an illusion of liveliness. Perlin noise is used in video games and CGI to procedurally generate "clouds, fire, water, stars, marble, wood, rock, soap films and crystal." Gaussian noise is more scattered.

Noise pollinates the image set, giving the model a sense of agency. Start with the same image of noise and ask it for different things, and you get similarly structured different things.

The Pollen Series was created as a beta tester for SpawningAI’s Inference model, an image generating diffusion model trained entirely on freely given, public domain images. Artists were able to build personal datasets that emphasized their own images, or weighed aspects of the existing training data more heavily.

The Pollen Series is the result of building a dataset of glitches: bad scans, and archival debris; the missing pages that usually get stripped out of the datasets. These were combined by my archive of failed AI generated images, created in an attempt to get the machine to make images of noise when they are designed to strip noise away.

The resulting images are photographs of a model displaying the scattershot chaos of noise and pollen, connecting the dots between the liveliness of nature and the limits of our models, the illusions that give rise to our belief in the intelligence of a unintelligent machine.

They are designed to be printed and framed for galleries at approximately 3 x 4 feet. Prints are available for interested curators and collectors.

Pollen Series (2025)