Video and sound installation using Machine Learning, 2018
A deep dive into, and controlled exploration of the inner world of a neural network trained on everything, the world, the universe; trained on art, life, love, faith, ritual, god.
‘Deep Meditations’ is a continuation and merger of both the ‘Learning to see‘ and ‘Learning to Listen‘ series of works; using state-of-the-art Machine Learning algorithms as a means of reflecting on ourselves and how we make meaning. It is intended as both a piece for introspection and self-reflection, as a mirror to ourselves, our own mind and how we make sense of the world; but also as a window into the mind of the machine, as it tries to make meaning in its own computational way.
Two artificial neural networks work together – connected deep in the so called hidden layers – to generate images and sounds which are somehow harmoniously linked. The piece is a slow journey through the imagination of a machine which has been trained on *everything*. Literally ‘everything’. Images tagged ‘everything‘ were scraped from the popular photo-sharing website Flickr. Along with images tagged with world, universe, space, mountains, oceans, flowers etc.. As well as more abstract concepts like art, life, love, faith, ritual, god.
Given such a diverse dataset however, the neural networks are not given any labels about anything that they are exposed to. They are not provided the semantic information to be able to distinguish between different categories of images or sounds, between small or large; microscopic or galactic; organic or human-made. Without any of this semantic context, the network dissects and learns purely on aesthetics, and all of its knowledge is fused into a melting pot of surface characteristics. Swarms of bacteria become clouds of nebula; oceanic waves become mountains; flowers become sunsets; blood cells become technical illustrations etc.
The multiple channels of video represent related – but slightly varied – journeys, happening simultaneously. These journeys affect each other, but they have different characteristics. Some are slower and steadier, focusing on more long term goals, while others are quicker, more exploratory, curious, seeking shorter term rewards. Other channels yet represent the same journeys, but at different points of the networks training history. Shedding light on how slightly more exposure to certain types of stimulus can cause the network to reinterpret the same inputs as visually and compositionally related, but with semantically vastly varying results. All of the journeys are still somehow in sync, branching and spiralling around each other.
Memo Akten is a computational artist from Istanbul, Turkey working primarily in moving image, light and sound; producing work spanning disciplines such as video, installation, performance, online works and dance.
His work explores the collisions between nature, science, technology, ethics, ritual, tradition and religion. He studies and works with complex systems, behaviour and algorithms, and combining critical and conceptual approaches with investigations into form, movement and sound he creates data dramatizations of natural and anthropogenic processes.
Alongside his practice, he is currently working towards a PhD at Goldsmiths University of London in artificial intelligence and expressive human-machine interaction, exploring collaborative co-creativity between humans and machines.
Akten received the Prix Ars Electronica Golden Nica in 2013 for his collaboration with Quayola, ‘Forms’. Exhibitions and performances include the Grand Palais, Paris; Victoria & Albert Museum, London; Royal Opera House, London; Garage Center for Contemporary Culture, Moscow; Holon Design Museum, Israel and the EYE Film Institute, Amsterdam.