Canadian Abstracts #2
Date: 2020
Duration: 07:12 min
Medium: Algorithmic Generated Video.
Size: 1024px x 1024px
Canadian Abstracts #2 is a continuation of the machine learning series I’ve been working on over the last few years. Pretty excited to have been able to get the GAN network to produce higher resolution video.
Canadian Abstracts is an exploration of computational creativity focused on the relationships with nature and our environment. Canadian Abstracts uses a Generative Adversarial Network (GAN) which is a type of Artificial Intelligence. This algorithm is trained on ~10,000 artworks by all members of the Group of Seven. Through the visual exploration of these new Canadian Landscapes, does our own understanding and view of Canada, it’s wilderness and our environment match that of the algorithms? Are these A.I. landscapes familiar to you? Could these landscapes really exist? Or might these images only be the dreams of a technological system that is trying to understand our physical world?
Canadian Abstracts #3
Date: 2020
Duration: 03:55 min
Medium: Algorithmic Generated Video.
Size: 1024px x 1024px
Canadian Abstracts #3 is an exploration of computational creativity focused on the relationships with nature and our environment. Canadian Abstracts #2 uses a Generative Adversarial Network (GAN) which is a type of Artificial Intelligence. This algorithm is trained on ~5000 pieces of work by Lawren Harris. Once trained, the A.I. is able to generate and output its own images to express its understanding of the Canadian wilderness. Then by allowing the algorithm to move through latent space, these output images become animated as it travels between data points within its imagination. Through the visual exploration of these new Canadian Landscapes, does our own understanding and view of Canada, it’s wilderness and our environment match that of the algorithms? Are these A.I. landscapes familiar to you? Could these landscapes really exist? Or might these images only be the dreams of a technological system that is trying to understand our physical world?
Jordan Shaw is an internationally exhibited artist and creative technologist raised and currently based in Toronto, Canada. He received his MFA from OCAD University's Digital Futures program exhibiting his thesis project, during Vector Festival at InterAccess. Before that, he completed his undergraduate degree at Carleton University and Algonquin College, where his final installation was exhibited at ACM SIGGRAPH.
The manifestation of Jordan's work seeks to visualize the hidden interactions between people and technology, data collection, generative and computational visuals and the digital systems trying to understand the physical world. These technical systems are not always physically tangible to the human senses. Jordan's work intends to creatively express the invisibility of modern-day techno-culture into a tangible and concrete experience that exemplifies the connection between participants and digital systems. He's exhibited at festivals including ACM SIGGRAPH, Vivid Sydney, Toronto Design Offsite Festival, Vector Festival, Nuit Blanche, OPC's Winter Light Exhibition and now IDKF.