The first Cybathlon was successfully launched in 2016 as an international event in which people with disabilities or physical weakness use advanced assistive devices, including robotic technologies, to compete against each other. Sixty-six pilots assisted by 400 team members in 56 teams from 25 nations, participated in six different disciplines. One discipline is the Brain-Computer Interface Race, where an avatar in a computer game is controlled purely by brain waves. Can you do it as well?

Credits

Project: CYBATHLON / ETH Zurich, Switzerland
Inventor and initiator: Prof. Dr.-Ing. Robert Riener

BCI Game: BrainRunners, developed for the BCI Race of the Cybathlon 2016 in cooperation with ETH Zurich and Zurich University of the Arts (ZHdK), Switzerland

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

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.