In "Diffusion," I delve into the intersection of memory and technology, merging machine learning, personal history, and photography to explore the fluidity of recollection. Through a diffusion model, I have trained an AI on my family albums and hometown photos. The model disperses the information within the photograph, introducing 'noise' to erode the image, then gradually removing it to reconstruct new imagery based on visual patterns learned from the dataset. As a result, photographs no longer possess fixed meanings; instead, they become malleable data that allows reinterpretation.

Guided by my drawings, the AI helps me create abstract visuals that blend the past with my current reflections. In these visuals, I depict "giants" — the influential figures from my childhood, who enforced their expectations upon me, shaping my identity under their weight. By transforming them into tapestries, those fragments of memory behind the giants are deconstructed into materials and woven into tangible expressions. Yet, paradoxically, these tapestries—traditionally used to preserve stories—are now crafted by an automated machine following predetermined programs. This natural and organic appearance is, nonetheless, achieved through technology which both enhances and distorts our understanding of the past.

The diffusion process, with its erosion and reconstruction of images, mirrors my journey of reimagining the past. "Diffusion" reflects on our methods of capturing and remembering the past and also invites you to reconsider how we can reshape our historically constructed narratives.