ADA is an AI application and an artwork that uses machine learning techniques to produce aesthetic evaluations of pictures taken by its users. The users in turn are prompted to evaluate ADAs claims and preferences, thereby helping the AI to develop new aesthetic preferences and an understanding of aesthetic categories.
Machine learning algorithms have gained a foothold as agents producing aesthetic assessments. They influence our everyday aesthetic choices: what music we listen, what TV-series we follow etc. But how does artificial intelligence learn aesthetics? ADA encourages users to engage in aesthetic dialogue with an AI, raising questions about the differences between human and machine perception.
The ADA Android app enables its user to take pictures for ADA to process and allows the user in turn to respond to ADAs evaluations. The user can install the app on their own device. User interaction with the ADA app consists of three main stages: 1) the user takes a picture, 2) ADA responds to the picture by giving a textual aesthetic critique, which contains keywords the user can interact with, 3) by selecting one of the keywords, the user is able to tell ADA whether they agree with it, suggest new qualities and indicate which parts of the picture support these new evaluations, and give their own aesthetic judgements of the picture. In addition to the textual responses, ADA can also optionally express its views orally by leveraging the device’s text-to-speech capabilities.
The ADA backend running on a remote server receives the images sent by the client apps, processes the images to produce aesthetic evaluations, and keeps track of the user interaction on a client by client basis. The image processing is based on a variety of machine vision and machine learning techniques that aim to extract image descriptors, that are then ascribed aesthetic value by ADA. The descriptor extraction mechanisms are shared across the clients and updated offline once enough training data is collected. The aesthetic valuations in contrast are maintained on a client by client basis and evolve online as the user interacts with and slowly personalizes their own ADA.
Technical description: https://brainsonart.wordpress.com/ada-description/