One intriguing characteristic of the human mind is its ability to be creative, that is the ability to generate an output that is not explicitly learned; to evolve memory through synthesis and noise, arriving at ideas and solutions that seem to come out of nowhere, that appear to be completely new.

Creativity, like consciousness or intelligence, is a fundamentally social and perceptual phenomenon. There is no list of ingredients, secret formula, or divine synthesis that gives rise to any of these qualities. A person, an animal, or a machine, is creative if we deem them to be so, if our culture has shaped us to believe it. Nonetheless, the structure of the biological brain and the process by which it evolved lends it the capacity for these qualities and the perception of them.

This project is concerned with the possibility of creating creativity. It proposes that by constructing electronic structures using processes and materials inspired by biology we can enable a creative capacity in machines.

Through utilization of principal component analysis and neural network techniques I have developed an autonomous face categorizing and generating software program. Through exposure to facial images over time the network develops a sense of what faces are, what components they are composed of, what makes them similar and different. It is then able to extrapolate from experience to create new facial images which embody its knowledge.

Like a living creature, each instantiation of the code is unique; though each may experience similar information, they apprehend it and remember it in their own anomalous way. Following from this, they interpret new and ambiguous stimuli divergently. This project examines the core possibility of how it is that human beings come up with ideas which are new to them, and how this capability can be translated to the realm of machines.