Joanne Hastie original
Acrylic on Paper
9″ x 12″
Available - contact Joanne to inquire about this painting
This abstract painting on paper was created as part of my Machine Learning Abstract Series. I am painting abstract paintings by hand while using Python code and TensorFlow algorithms to generate the compositions. After painting this geometric abstract by hand and not intending it to represent an object, I loaded the image into an artificial intelligence algorithm I use to title the paintings. Of all the potential objects the painting could represent including cardigan, meat cleaver, paper towel and envelope, I selected ‘Cardigan’ as the title, the confidence score is the probability from the algorithm that it could be that object.
Inception v3 results:
cardigan (confidence 28.859%)
Band Aid (confidence 15.757588%)
paper towel (confidence 8.700208%)
carton (confidence 8.252575%)
envelope (confidence 5.2732904%)
cleaver, meat cleaver, chopper (confidence 2.1493418%)
This series of work was featured in aiartonline.com as part of the Machine Learning Conference in Montreal in December 2018 and also in a group show "It's A Machine's World" in Zurich Switzerland in April 2019 that included 8 female artists that use artificial intelligence in their artwork.
ABOUT THE PROJECT:
I wrote Python code to collage and generate compositions of hand painted geometric shapes and then use an image classifier to rank the compositions based on my preferences.
Over time, I update the classifier as I hone my eye as to what looks good as a painting versus a digital thumbnail. After it is painted, I use an untrained classifier to title the painting. I get titles such as “Bulletproof Vest”, “Traffic Lights” and “Envelope”, because they are flat paintings (minimal depth) – most paintings are classified as “Envelope”. Can you see the objects in the painting?
This work has been featured in NeurIPS Online Gallery in 2018, at ‘Women in Data Science’ show in Zurich in 2018 and in RUSI Journal 2019.
To learn more about this project click below.