In “Emergence”, Lia Coleman uses a neural network to expand upon their musings on time, the collective, and remembrance, as it relates to their Chinese-American heritage. Through mesmerizing visual transformations that mimic the biological emergence of life, Coleman invites viewers to reflect upon the powerful interconnectedness that weaves together our collective history on this planet.

Through use of Python-based tools such as PyTorch, ffmpeg, and moviepy, Coleman creates the artwork in a process where time plays a vital role. In an act of digital archival of the current era, Coleman collected and curated a dataset of hundreds of images of Chinese papercuts. In the training loop of a machine learning system, where past generations inextricably influence the current generation, each artwork emerges from a different moment in time. While each edition represents a different snapshot during the evolutionary process of creation, the full collection is of the same lineage, of the same family tree.

Embracing the concept of the collective, Coleman pays homage to the profound influence of those who came before us, such as our ancestors and artists of previous generations, while also highlighting the symbiotic relationships we cultivate with contemporaries like friends, family, and collaborators. The undulating black & white aesthetic of “Emergence" finds analogues in the work of Ryoichi Kurokawa, Ryoji Ikeda, and James Whitney.

Each collected edition of “Emergence” includes a set of bonus files from the artist, consisting of several checkpoints of Coleman's complete machine learning model at different stages of training in the form of .pkl files, allowing collectors to generate their own images and animations in the spirit of collective co-creation.

Note: Collected artworks are 4k and 60fps. For optimal smooth playback, please use high-performance hardware.