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This website hosts a short continually evolving film that attempts to visualize the song, “The Ship” from the Brian Eno album of the same name.

In explanation of the work, Eno comments:

“Humankind seems to teeter between hubris and paranoia: the hubris of our ever-growing power contrasts with the paranoia that we're permanently and increasingly under threat. At the zenith we realise we have to come down again... we know that we have more than we deserve or can defend, so we become nervous. Somebody, something is going to take it all from us: that is the dread of the wealthy. Paranoia leads to defensiveness, and we all end up in the trenches facing each other across the mud."

Taking this statement as a starting point and utilizing a bespoke artificial intelligence programme developed by the Dentsu Lab Tokyo, this film explores various historical photographic images and real time news feeds to compose a collective photographic memory of humankind.

Developed especially for this project, the artificial intelligence programme employs machine learning techniques to interpret its own ”memories” of the past, associating them with ongoing current events and presenting them in a unique generative film.

The Ship - A Generative Film is an exploration of the music and themes from the latest Eno album ‘The Ship’ and forms a journey through modern history to explore the relationship between events present and past. The viewer is invited to view this film and begin an internal discussion about how historical meaning is produced.

Does the machine intelligence produce a point of view independent of its makers or its viewers? Or are we - human and machine - ultimately co-creating new and unexpected meanings?

Source of images


As a way to actively engage with the music video in a more active manner, the viewer is capable to interact using the mouse.

There are 2 ways to interact.


The images used in this project are referenced from the following resources. News images from current events are used as input for the machine intelligence, and based on that, this project constructs the video utilizing past archive images which the machine intelligence considers relevant.