Creative Machine | Intentionality in Silico (Sold Christ)
A major exhibition exploring the twilight world of human/ma- chine creativity, including installations, video and computer art, artificial intelligence, robotics and apps by leading artists from Goldsmiths and international artists by invitation.
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Intentionality in Silico (Sold Christ)

Parashkev Nachev

A pervasive source of conceptual anxiety is the notion of intentional in-existence: that something can exist in a mind yet nowhere else. For the reductive theorist it seems an attractive hook on which to hang the distinctiveness of the mental, for it seems both fundamental and unparalleled in the non-human world. Though we had little use for the idea before the scholastics brought it back to defective life, a conceptual attack on it is too deflationary for the current intellectual market. So here instead I turn to empirical, guerilla warfare, by creating a machine that confabulates images that exist nowhere but in its “mind” yet have the reality of that most human of imagined unreals: the inexistent face. Employing deep-learning techniques I have created a new kind of machine, a facievore, that consumes human faces it can find on the internet and extracts canonical, archetypal representations, automatically shaped by “imhomogeneities” in the population, pictorial or categorial. It consumes both face surrogates (photographs, masks) and face representations (painting, sculpture), sometimes confabulating from the real, and sometimes from the human-imagined. Though seemingly part accidental, part reality-driven, its complexity pushes it into territory where the distinction between the stochastic and the deterministic becomes opaque.

Aside from smiling at the implausible reductiveness of dominant ideas in the philosophy of mind, this machine has another, positive aspect.

For it draws on a truth to which machine-learning more potentently than any other set of ideas will awake us: that the domains of the human and the physical-biological are one, must be one, and so to understand the biological we must humanise it.

This work supports a Wellcome Trust & Department of Health funded translational research project to develop a clinical system for detecting anomalies in brain scans with the aid of machine-learning (HICF-R9-501).