Learning to See: Gloomy Sunday (2017)

This project is part of the Learning to See series.

Learning to See is a series of works that use machine learning algorithms to reflect on how our own cognitive biases shape how we ‘see’ and make sense of the world.

An artificial neural network looks out onto the world, and tries to make sense of what it is seeing. But it can only see through the filter of what it already knows.

Just like us.

Because we too, see things not as they are, but as we are.

The work is part of a broader line of inquiry about our cognitive biases, our inability to see the world from others’ point of view, and the resulting social and political polarization.

Using custom models trained on custom datasets representing the platonic natural elements: ocean & waves (representing water), clouds & sky (representing air), fire, flowers (representing earth, and life).

Music “Gloomy Sunday” by Diamanda Galas

30s Excerpts

Images

Of all the different things and scenery that NVidia could have chosen to demo their award winning incredible ‘Gaugan‘ (2019) with, their first choice, and cover image is a rocky seascape

NVIDIA GauGAN rocky-seascape demonstration, 2019.
NVIDIA GauGAN rocky-seascape demonstration, 2019.

not unlike my Learning to See: Gloomy Sunday (made in 2017 / went viral 2018) 😊

Rocky-seascape study from Learning to See: Gloomy Sunday, 2017.
Rocky-seascape study from Learning to See: Gloomy Sunday, 2017.

Series

More works in the Learning to See series.

Selected Exhibitions, Performances & Presentations

Only selected shows with project pages are listed below. For more complete exhibition and performance history please see Show History.

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Details

Medium
HD Video. Duration: 3:02. Technique: Custom software, Artificial Intelligence, Machine Learning, Deep Learning, Generative Adversarial Networks.