Machine Hallucinations – ISS Dreams transforms a vast data set of 1.2 million images captured from the International Space Stations (ISS), along with additional satellite images of Earth’s topology, into a dynamic data painting. Each version of the machine’s dream sequences is derived from a different generative adversarial network (GAN) latent walk, exploring artificial intelligence’s capacity to reach its own subconscious and offering an avant- garde form of cartographic aesthetics.
About RAS : Machine Hallucinations
Machine Hallucinations is an ongoing project of data aesthetics based on collective visual memories of space, nature, and urban environments. Since the inception of the project in 2016, our studio has been utilizing machine intelligence as a collaborator to human consciousness, specifically DCGAN, PGAN and StyleGAN algorithms trained on these vast datasets to unfold unrecognized layers of our external realities. We collect data from digital archives and publicly available resources and process them with machine learning classification models such as CNN’s, Variational Autoencoders and deep ranking, to filter out people, noise and irrelevant data points. The sorted image datasets are then clustered into thematic categories to better understand the semantic context of the data universe.
This expanding data universe not only represents the interpolation of data as synthesis, but also becomes a latent cosmos in which hallucinative potential is the main currency of artistic creativity. In order to capture these hallucinations from a multi-dimensional space, we use NVIDIA’s StyleGAN2 which generates a model for the machine to process the archive and the model is trained on subsets of the sorted images, creating embeddings in 4096 dimensions.
As a masterfully curated multi-channel experience, Machine Hallucinations brings a self-regenerating element of surprise to the audience and offers a new form of sensational autonomy via cybernetic serendipity.
Los Angeles, California, USA
Team Members & Collaborators
Ho Man Leung
Raman K. Mustafa