NORAA [machinic doodles]

Abstract

How does machine learning contribute to our understanding of how ideas are communicated through drawing? Specifically, how can networks capable of exhibiting dynamic temporal behaviour for time sequences be used for the generation of line (vector) drawings? Can machine-learning algorithms reveal something about the way we draw? Can we better understand the way we encode ideas into drawings from these algorithms? While simple pen strokes may not resemble reality as captured by more sophisticated visual representations, they do tell us something about how people represent and reconstruct the world around them. The ability to immediately recognise, depict objects and even emotions from a few marks, strokes and lines, is something that humans learn as children. Machinic Doodles is interested in the semantics of lines, the patterns that emerge in how people around the world draw - what governs the rule of geometry that makes us draw from one point to another in a specific order? The order, speedpace and expression of a line, its constructed and semantic associations are of primary interest, generated figures are simply the means and the record of the interaction, not the final motivation. The installation is essentially a game of human-robot Pictionary: you draw, the machine takes a guess, and then draws something back in response. The project demonstrates how a drawing game based on a recurrent-neural-network, combined with real-time human drawing interaction, can be used to generate a sequence of human- machine doodle drawings. As the number of classification models is greater than the generational models (i.e. ability to identify is higher than drawing ability), the work inherently explores this gap in the machine's knowledge, as well as creative possibilities afforded by misinterpretations of the machine. Drawings are not just for guessing, but analysed for spatial and temporal characteristics to inform drawing generation.