Gradient Descent Open Call
Gossamer Fog announces a new open call for our upcoming exhibition Gradient Descent. The exhibition will be solely curated by a custom built machine learning algorithm trained on a dataset of documentation images from all the previous artworks, exhibitions and projects that have been shown at Gossamer Fog since we opened in 2016.
The artwork submitted through the open call will be added to a new dataset to be scored by the A.I model based on its confidence of how well each work compares to the ‘average’ aesthetic style of Gossamer Fog, developed over its 8 year history. This is an experimental curatorial project and we do not expect perfect results, in fact we are excited to see what weird and unpredictable choices the model makes.
The machine learning model being used has been built independently by Gossamer Fog in collaboration with artist and creative technologist Jayson Haebich. It will be run locally on our own machine and the submitted images of artworks and subsequent dataset will not be uploaded, made available to any company or entity or shared publicly online. The dataset will not be used by us for any other purpose other than for the selection of this exhibition.
By submitting images to the open call, you agree to Gossamer Fog publishing the images (without name/title/info) as part of the documentation of the machine learning selection process - this may either be physically at the exhibition or online.
Gossamer Fog will offer a £100 artist fee for each artist selected, plus cover transport costs to the gallery.
Submission Deadline: 14th February
Results Announced: 18th February
Delivery of artworks: 19th-25th February
Exhibition opens: 1st March - 14th of April
This open call is open to UK, European and International artists depending on what form of artwork you are submitting. Please check the categories below for more details.
We will only accept pre-existing works from 2023 or earlier that are available to be shown physically at Gossamer Fog Gallery in London during the install and exhibition dates (19th February - 14th of April).
While we welcome submissions from artists who have previously shown with Gossamer Fog, we will not accept any artworks that have been shown with us before, as not to interfere with the dataset and process.
Unfortunately we are unable to accept sound only works as the machine learning model is built on images only.
Submit up to 3 images of artworks in each category, each submission to a category must be made via a separate email. Only one image per artwork is allowed.
Each submission via email must have the subject line GD - Category Name (i.e GD - Digital or GD - Sculpture etc). Please include your name, the title of the works, the year and where you are based in the body of the email.
Open to artists based in the UK & Europe. This can be any type of physical work that falls within the maximum dimensions of 30x30x30cm.
Open to artists based in the UK only. This can be any type of wall based work that falls within the maximum dimensions of 70x120x30cm.
Open to artists based in the UK only. This can be any type of 3D work that falls within the maximum dimensions of 100x100x100cm.
Open to international artists from around the World. You can submit Video, VR or Computational works that can be displayed on screens or VR headsets. Gossamer Fog will provide the equipment needed to exhibit these works.
We understand that some artworks cannot be captured in their entirety in a square format, so you are welcome to submit cropped images or close up details as our training dataset contains many images in this format anyway.
All images submitted must be:
- 700x700 pixels
- RGB colour mode
- .jpg files (make sure it is not .jpeg with an e)
- Titled: category_name_surname_001.jpg
Any files submitted that do not adhere to this format will be automatically deselected as the program will not process them - and we will not be formatting anyones entries ourselves.
Please send your submission to firstname.lastname@example.org by 14th February 2024 at 11:59pm GMT
If you have any questions please email us at email@example.com