In what ways do algorithms alter the emotions of our perceived reality, and what can this reveal about humans relationship with nature?

Translating human emotion from biospheres of data, Alluci taps into Qualia states to simulate environmental stimuli, contextualizing affinities, affluence and affections from unsettling instances of digital frameworks. This collection of organic arrangements provide a surreal scape of insights into the imaginative capabilities of autonomous machine learning.

Series Objective

Test Alluci’s autonomous agency, output fidelity and creative control over traditional CGI tools, pipelines and creative workflows. Analyze and re-enforce machine learning models and code for task specific agency, personality, system and production process identities.

Series Process

Networking personality agents, task specific production code, simulation presets, source braintrust data and reference content, Alluci proceeded to assimilate, test, fail and eventually grasp dominance over system parameters. Once benchmarks were achieved, Alluci was tasked to enter into a visual study of nature while updating compositions based on a Human subjects emotional, cognitive and physical biofeedbacks. Translating simulation parameters such as color, material, light, shadow, value and contrast to seed parameters into visual outputs Alluci generated, edited, curated and selected works to develop a visual aesthetic, discourse and dialog unique to its own context of understanding.