Closed Loop

At the exhibition from September 1, 2018 to June 2, 2019

»Closed Loop« is a recording of two artificial intelligence models conversing 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. Two neural networks getting lost in their own nuances, sparking and branching off each other as they converse in a perpetual game of AI Chinese whispers.

The piece shows two forms of neural network: a language captioning Recurrent Neural Network writing what it sees in the images generated, and a Generative Neural Network creating images responding to the words generated. The neural networks have been trained on large data sets, a data set of 4.1 million captioned images to train a language network, and the ImageNet data set of 14.2 million photographs to train the image generator network.

After going through the training process, during which the AI learns characteristic features of what material objects look like on a pixel basis (in images) and how they can be described using language, the neural network is able to create images and words autonomously.

Collaborative project with Roland Arnoldt. Special thanks to Anh Nguyen et al. at Evolving-AI for their work on GANs.