Mukadderat / Predestination

"Mukadderat / Predestination" explores our perception of future prediction by offering new possibilities into the conventional understanding of time.

 

Mukadderat / Predestination has been exhibited in DIFC Art Nights, March 2022. Dubai.

Abstract

"Mukadderat / Predestination" explores our perception of future prediction by offering new possibilities into the conventional understanding of time. The work is emerging from the juxtaposition of artificial intelligence, randomness and predictability of the future.

Data visualization and morphological transformation translate the data into meaningful questions that enable visitors to experience the work. Sertac Tasdelen's work questions the idea of not knowing what the future holds and the future's uncertainty and a predetermined course of events.

 

Free will might be an illusion, and our destiny might be waiting for us patiently. We all perceive ourselves as separate entities, but we might be all part of collective and endless consciousness. The work's title refers to the predetermined future, and all we perceive is an illusion of time, which has been a highly debated topic since the dawn of humanity, between the religious leaders, scientists, and philosophers.

Back in his Istanbul studio, Sertac Tasdelen and his team filtered the true data — billions of living memory — through sophisticated analytics, transforming the data set into pigments and light. What emerged, the artist says, was a completely harmonious image, an “dancing sediments of future prediction”

 

"With help from artificial intelligence, we embarked on a journey through Turkish coffee memoirs. Neuro-scientifically, the Faladdin is the source of memory, dreaming about the future," he says.

Process

The data preparation process was conducted by the Faladdin AI Team using its dataset of Turkish coffee cup images. The work requires that the image data matches with precise specifications; such as limited resolution and averaged pixel values. By draining the power of a generative adversarial networks (GAN) architecture; enhanced for 1024x1024 pixel output by using additional convolutional layers (Conv2D), we have achieved to create self-generating images of coffee cups which were then utilized for higher resolution, layer by layer, to emphasize the details of fed data. This approach enabled us to use the thumbnails of every user of the Faladdin as feature vectors to interpolate between generation processes.