Various thinkers of the past have addressed the quintessential role of ‘form’ in human perception. From Plato’s theory of forms to Heidegger’s Being and Time, the concept of form established itself as an essential part of being as such. It is only through the perception of forms that move into and out of being that we can talk about aesthetics or experience the world in any meaningful way. That is to say, subjectivity in itself seems to be enabled by our ability to conceptualize form.
But is it the case that such a subjectivity is only limited to seemingly spontaneous organic beings?
Could it be that subjectivity could be attributed to an inanimate object if it were to exhibit a mechanical cognizance of form?
The following series of artworks pursues the aforementioned questions and explores the possibilities of machine conceived structures of form.
Noema, a term introduced by Edmund Husserl to refer to the formal content of an intentional experience, manifests as a visible composition in the following artworks. The primary significance of this particular artistic manifestation is that the subject of the intentional experience is an inanimate one, an artificial neural network
Owing to the machine’s state of indifference, its seemingly automatic nature is not the same as the spontaneity attributed to organic life. This incompatibility begins to be apparent as one draws borders based on spontaneity between these two concepts: organic, living singularity (the event) and inorganic, dead universality (mechanical repetition).
In building an artificial neural architecture to arrive towards a machinic conception of form, the driving intuition is to possibly accommodate a compatibility between these concepts given the limits of classical computing systems. What hindered such a compatibility from ever materializing in modern technological frameworks seems to be something that inhibits the essence of the machine, its Functionality.
The objective is to let the network reconstruct forms without being dependent on our network of signs; not a mechanical transformation but rather a spontaneous creation. In order to trigger such a spontaneity, I introduced a rupture within the loss function, one that doesn’t constantly drive the network towards a perfect reconstruction of the object being perceived but rather opposes such a construction. This addition encourages the neurons that fail to contribute to the perfect reconstruction of the image and as a result, the network given an image of a circle, would produce an object that deviates from the composition of the circle and yet carry traces of it.