ADA | Artificial Drawing & Design Aesthetics

The research unit in "Artificial Drawing and Design Aesthetics" is identified by the acronym "Ada," which stands for “Artificial Design Aesthetics” and also references the proper name of Ada Byron King, who, by conceptualising "software," anticipated how a form of "artificial intelligence" could influence numerous social domains of artistic, technical, and aesthetic production. This initiative stems from the traditional academic discipline of “Disegno,” which encompasses the theory and practice of projective representations (technical depictions and heuristic and communicative representations of design), considering also the aesthetic values involved and the inherent morphology of the represented objects. The unit focuses on "Artificial Drawing," that is, the implications of using artificial intelligence [AI] applications for the theoretical and practical aspects of “Drawing.” It considers as part of the “Drawing” domain various AI tools currently used in design practices for: a) recognising, reading, and classifying other corpora of images, and b) generating new images from the vast datasets derived from corpora of different expressive media. Thus, "Artificial Drawing" refers to the use of AI tools both in the morphological and morphometric study of objects and environments based on information patterns (a), and in projective representation (b). Specifically: a) AI tools – from Data Mining to Information Visualization (infographics) – that exceed the capabilities of human perception and computation in recognising and measuring informational patterns within data corpora. These are often applications produced through deep learning on vast syncretic datasets (verbal, visual, etc.). In this morphological and morphometric sense, early examples of "Artificial Drawing" from the past decade include pattern recognition systems, such as those increasingly used in medical imaging diagnostics, particularly in histopathology and radiology, providing visual expertise tools in the fields of art and design. b) AI tools that prove effective in automating various typical productive tasks in the field of projective representations: from concept design to rendering, from surveying to parametric modelling, in fields such as architecture, urban planning, and product and communication design. Some of the most recent applications of "Artificial Drawing" are developed using deep learning processes, trained with vast datasets: corpora of visual images, often verbally labelled and/or texts in natural language. These applications are designed to learn, a posteriori, to recognise informational patterns that would largely elude human computation and perception. They are also capable of generating new data in response to inputs formulated in some expressive medium (visual, acoustic, verbal, etc.), producing in response classifications of other image corpora, or generating novel images. In general, such applications produce new syntagmatic chains that align with the (human) meaning of the provided prompt. Since the transition from traditional Drawing techniques to Artificial Drawing tools, beyond reshaping drawing practices, fundamentally challenges traditional aesthetic, epistemological, media, and legal concepts – such as the notions of “image” (referring to patterns of informational items or syntagmatic chains no longer perceivable by human eyes or ears) produced by machines for other machines, "authorship," "style," "character," "manner," "form," "ductus," "asemic writing," "forgery," "counter-example," "counterfactual," etc., the impact of AI in the field of projective representation, and its historical-critical, aesthetic, and intersemiotic interpretation is significant.