In Closed Loop two artificial intelligence models converse with each other—one with words the other with images—in a never-ending feedback loop. The words of one describe the images of the other, which then seeks to describe the words with a fresh image. The neural networks become lost in their own nuances, sparking and branching off each other as they converse.

Credits

www.jakeelwes.com
Special thanks to Anh Nguyen et al. at Evolving-AI for their work on GANs

The resulting sequence is very dreamlike, drifting in and out of recognition between static scenes that the model remembers well, to fleeting sequences—usually with a lot of movement—that the model barely comprehends.

The film Blade Runner is adapted from Philip K. Dicks novel Do Androids Dream of Electric Sheep?. Set in a post-apocalyptic dystopian future, Rick Deckard is a bounty hunter who makes a living hunting down and killing replicants, artificial humans that are so well engineered that they are physically indistinguishable from human beings.

By reinterpreting Blade Runner with the autoencoder’s memory of the film, Blade Runner—Autoencoded seeks to emphasize the ambiguous boundary in the film between replicant and human, or in the case of the reconstructed film, between our memory of the film and the neural networks. By examining this imperfect reconstruction, the gaze of a disembodied machine, it becomes easier to acknowledge the flaws in our own internal representation of the world and easier to imagine the potential of other, substantially different systems that have their own internal representations.

Credits

Carried out on the Msci Creative Computing course at the Department of Computing, Goldsmiths, University of London under the supervision of Mick Grierson.

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. While the light glows, people’s assumptions about the world are synthesized in an artificial neural network (ANN) and modified into a machine discourse. Mold (life) slowly grows over the fluorescent gelatin, until the light is extinguished and the metaphysical discussion ends.

Credits

Supported and produced by Us(c)hi Reiter— servus.at
Translation: Aileen Derieg
FIFO programming: Oliver Frommel
Supported by Kunstuniversität Linz

Thanks to Free/Libre Open Source Software, http://fsfe.org/

Partly funded by the Bundeskanzleramt Kunst & Kultur as part of the servus.at annual program 2017 and by Linz Kultur

[{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.

There will be a web app that allows dialogue with the public, while [{Ghost}] will also be connected to the media façade of the Kunsthaus Graz and the Ars Electronica Center. These façades create a projection in public space of the current status and developments in the A.I. in the form of visual patterns and brief info texts.

A collaboration between Ars Electronica Center and Kunsthaus Graz

Project team: Tristan Schulze (artist and designer), Elisabeth Schlögl (assistant curator, Kunsthaus Graz), Barbara Steiner (director, Kunsthaus Graz)

cellF combines these two systems. The “brain” of this new creation consists of a biological neural network grown in a petri dish, which controls analogue modular synthesizers in real time. The living part of this completely autonomous and analogue instrument is composed of nerve cells. These were taken from Guy Ben-Ary’s fibroblasts (cells in connective tissue), which were programmed back into stem cells. Guy Ben-Ary then artificially further developed these stem cells into neural stem cells, which can become differentiated into nerve cells under certain conditions in the laboratory and form a neural network – Ben-Ary’s “external brain.”

The activity of this brain can be influenced by the input from other, human musicians and made audible through the analogue synthesizer. Human and instrument become a unit – a “cybernetic rock star” from the petri dish.

The project will be presented during the Ars Electronica Festival in the POSTCITY.

Credits

guybenary.com/work/cellf

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.

Credits

www.jakeelwes.com
Special thanks to Gabriel Goh for inspiration.

Originally inspired by the neural networks of our own brain, deep learning artificial-intelligence algorithms have been around for decades, but they are recently seeing a huge rise in popularity. This is often attributed to recent increases in computing power and the availability of extensive training data. However, progress is undeniably fueled by the multi-billion-dollar investments from the purveyors of mass surveillance: Internet companies whose business models rely on targeted, psychographic advertising, and government organizations and their War on Terror. Their aim is the automation of understanding big data, understanding text, images and sounds. But what does it mean to “understand”? What does it mean to “learn” or to “see”?

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. The picture we see in our conscious minds is not a direct representation of the outside world, or of what our senses deliver, but of a simulated world, reconstructed according to our expectations and prior beliefs. The work is part of a broader line of inquiry about self-affirming cognitive biases, our inability to see the world from others’ point of view, and the resulting social polarization.

SAT Sept. 9, 2017

SAT Sept. 9, 2017, 3:00 PM-4:30 PM

Infos

Meeting Point: POSTCITY WE GUIDE YOU Meeting Point
Duration: 90 minutes
Language: English
Price: € 16 / € 12 reduced