Matheus Montanari

The algorithmic landscape project investigates different dimensions of the city landscape, taking into account the algorithmic layer that constitutes it. More than a backdrop where the action happens, the landscape is discussed as something in the order of action, contemplating spatio-temporal dimensions that encapsulate a series of physical, cultural, technological and aesthetic elements. We understand that with an increasing algorithmic logic permeating the world, especially Artificial Intelligence, we have to take into account these elements as agents and constituents of the landscape.

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These types of systems have specific ways of operating, mainly on data-based prediction. For this project, we subvert two of them, which operate with collaborative filtering for content suggestion: Spotify and Google Maps.

 The project unfolds in three stages.

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The first part is a performance that culminates in an algorithmic walk in two different cities: Paris, France, and Caxias do Sul, Brazil. This action produces more than 10,000 images that are used as a data set for the work.

During the performance, I make an algorithmic derive guided by a song suggestion system. I walk on the streets listening to the songs that the platform suggests to me based on the profile it created upon my data. If the algorithm suggests a song that I liked, I turn in the next street right. If the algorithm suggests a song I don't like, I turn the next street left. In this way, I make an unusual route and mapping of the city, while I record everything in photos and the geo-located track of the GPS application.

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The second part consists of the analysis of these images by a machine learning software created for this work. This software does a reverse image search on the data set and finds the most similar locations in the two different cities.

With these selections, we created a collection of images that approach two geographically distant places from an algorithmic point of view.

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Then, the images are printed with a specific technique on acrylic sheets, which maintains the pigment semi-humid. When the different acrylic sheets meet, the images combine themselves, bringing out the image of what exists between them, and revealing the algorithmic landscape.

These images are then scanned and transformed into a video frame.