Various pontetials is as a subset of Approximation series.

By the mid 2010s it became evident that the vast majority of digital images in the era of big data would not even be the kind that is supposed to manipulate human attention, but the ones that exist as data points in obscure databases and are meant to be analysed by algorithms rather than humans: images taken by machines for other machines. ‍And so has emerged a complex and real link between contemporary ecological conditions of the planet and our computational ability to sense, process and map these conditions. With machine intelligence, big data architectures and giant corporate agents being at the forefront of this relationship. In simple words, everything can be registered as a data point, we learn about the world through a multitude of computational approximations and models based on them.

In addition to being subjected to attention economy any art that is posted or created online has also become subjected to algorithmic pattern processing as well. Which makes these existing and potential algorithms a new audience, a new type of interpreter that is undefined and speculative, a black box with mathematical eyes. With the series of works Earth Potentials, Mars Potentials, 67P Potentials, Hydrothermal Potentials, Earthwares and so on I attempted to create works that would be addressing these new synthetic viewers. Of course it’s important to note that the “artificial intelligence” we are having on hand at the moment is an assemblage of human indexing labor, limited databases of human-created history of images and words, conditioned by programming languages, hardware and all the limitations that come with that. This unprecedented scale of mapping is an unprecedented scale of image-making, and image-making is an act of “seeing as, drawing as” (Janet Vertesi).  

The image itself becomes a matrix of pixels, to be statistically analysed and transformed into relevant information. As my practice developed I increasingly started to make works that were re-using images made for a variety of these massive datasets like photographs of Mars surface taken by robotic rovers, photographs of laboratory organisms taken by various scientific institutions, medical scans and protein models, photographs taken by automatic trail cameras in nature reserves around the world, and so on. I also tried to imagine how these arrangements of pixels are meant to trigger not only human viewers, but also various algorithms based on machine vision principles and potentially so-called General Artificial Intelligence, if not now then definitely at some point in the future. “The point of using AI in scientific research is that it sees patterns that humans cannot. But deciding whether the pattern that it sees (or the pattern that people see in what it sees) is real or an illusion may or may not be falsifiable, especially when it concerns complex phenomena that can’t be experimentally tested.” — Benjamin Bratton and Blaise Agüera y Arcas, 2022 Mapping.

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