Deep Unlearning

Just the other day we entered a new world. With the so-called ›tabula-rasa‹ way of learning demonstrated by DeepMind’s AlphaGo Zero, human knowledge of the Western category now finally is one among many, and certainties will come to an end. Through the deployment of such true machinic creativity, both new knowledge and its application in time are likely to produce effects so complex and alien that we may never fully understand them.

In order to prepare for a world of unhuman knowledge, in a playful-yet-serious inversion we propose a process of Deep Unlearning for humans. An effort to self-alienate in order to gain a tiny measure of access to the ways of the not-us.

An open-ended series of playful algorithmic exercises will be developed, phrased in an almost Fluxus-like fashion, executed and documented over the course of the residency and beyond.

They will take on different interfacial configurations, ranging from human-nature/nature-human, to human-human and ultimately human-machine/machine-human.

Their instructions will be sharable in many media, forming the basis for solitary exercises or workshops with multiple natural or unnatural participants across all scales. Results can be purely experiential, but also (if the exercise involves collecting data) may in turn form the basis of another training process.

We consider the audience of this effort to be ourselves as a species, an adaptation to the future.