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neural network

Closed_Loop_JakeElwes_web

Closed Loop

In Closed Loop two artificial intelligence models converse with each other—one with words the other with images—in a never-ending feedback loop.

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Blade Runner—Autoencoded

Blade Runner—Autoencoded is a film made by training an autoencoder—a type of generative neural network—to recreate frames from the 1982 film Blade Runner. The Autoencoder learns to model all frames by trying to copy them through a very narrow information bottleneck, being optimized to create images that are as similar as possible to the original images.

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Hades

Light as a reference to the soul and consciousness glows in a gelatin cube, thus at the same time serving as a source of information.

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[{Ghost}]

[{Ghost}] is an artificial neural network, an artificial intelligence that inhabits two different art institutions. It will be shaped by online text information derived from both the Kunsthaus Graz and Ars Electronica Center but mainly through the participation of their human audience.

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Hybrid Art – cellF

There is a surprising similarity in the way neural networks and analogue synthesizers work: both receive signals and process them through components to generate data or sound. cellF combines these two systems.

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Machine Learning Porn

In Machine Learning Porn a neural network has been trained using an explicit content model for finding pornography in search engines. The network is then reverse engineered to generate new “pornography” from scratch: an AI daydreaming of sex.

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Learning to See: Hello, World!

A deep neural network opening its eyes for the first time, and trying to understand what it sees. Learning to See is an ongoing series of works that use state-of-the-art machine-learning algorithms as a means of reflecting on ourselves and how we make sense of the world.

Gene Kogan

Experts Tour: The Neural Aesthetic

Gene Kogan will introduce the field of machine learning and its existing and speculative implications to new media and art in general. He will discuss applications of neural networks and associated algorithms to producing images, sounds, and texts, showing examples of contemporary works using these abilities. Gene Kogan will also present two of his own works intersecting machine learning and generative art.