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Brief

The alpine landscape seems to be an idyllic garden of unspoiled nature which is understood as a harmonious and balanced background. However in reality almost every kilometre of its landscape has been cultivated, urbanised or eroded by human activity. Even those bits of the landscape that on the first glance seem to be intact, become a reflection of the effect of climate change. Retreating glaciers are the most striking phenomenon of all; they are formations of geological time and memory that are melting in front of our eyes in a matter of years.

The stratum of the glacier is a repository of the microclimates, chemical compositions and traces of the past and present technological effects. An enormous amount of data is stored in particles and molecules of air, trapped in the layers of ice, where every layer contains information about past biological and climate activities. As an analog informational system, glacier remembers the past of the earth while slowly forgetting it due to the process of global warming.

The melting of a glacier reveals something that has been trapped inside of its memory layers - molecules of gas, organisms that come back to life, geological layers. Decoding these data reveals a bio-computational alpine panorama, composed of exponentially increasing carbon dioxide concentrations, unique diversity of contaminants and intense metabolic activity. It reveals a huge bio-computational living database that is slowly losing its structure.

If we portray glacier as an informational system we can oppose it to digital storage technologies which are currently producing enormous amounts of heat and form spatial typologies which are external to the landscape. Due to the global climate and energy crisis, conventional storage systems like digital data centres have become the core reason for Data Warming. New data storage and exchange systems such as DNA Data Storage, spatial patterns and encoders, as well as new, integral to the landscape typologies for data storage become vital. Synthetic landscape is evolving into a bio-computer capable of storing, transmitting and sharing data and knowledge.

Method

Synthetic Crystalleidrons is a design studio which establishes another kind of relationship with the emerging landscape of the glacier while looking for new typologies for data storage and new methods for data farming. We will approach the synthetic landscape as an ecology of knowledge, the Alpine glacier as an analog informational system, and the crystal as an informational entity. We will be exploring the potential of material systems formed in collaboration with living organisms and digital processes to store and exchange the data and knowledge. Through the set of physical and digital experiments we will aim to decode dataspaces by drawing and simulating the complex web of relationships it comprises. Physical exploration of crystal structure, micro algal and bacterial colonies will help us to develop synthetic material systems and typologies for data farming, as well as to define the strategies for the on-site implementation using snow and ice as a construction material. We will investigate machine learning and generative methodologies of computational design to create dynamic transformation processes of a landscape. We will explore the glacier surface with computer vision algorithms by extracting and analysing features and patterns of the landscape. We will also use remote sensing tools and databases to look back into the glacier's history and study how its ice cover changed over the years. With the help of the generative neural networks, we will simulate and generate different variations of the ice-melting-inspired structures which will help us to develop the site-specific design solutions. We will consider machine learning as an instrument of knowledge magnification that helps to perceive features, patterns, and correlations through vast spaces of data beyond human reach. The new type of glacier realm becomes an emergent property of a cybernetic process or conversation. The inhabitaion process is fully automatized with data analysis, machine learning, material programming, bio- fabrication and smartification protocols. Such automatization will aim to be ecological and sustainable, which implies the realisation of a complex cybernetic system.