Neural Networks are nowadays widely used to detect, classify and reconstruct emotions, mainly in order to map users behaviours and to affect them in effective ways. But what happens when we use Machine Learning to perform human feelings? And what if we use it to produce autonomous behaviours, rather then to affect consumers? Adversarial Feelings is an attempt to inform non-human intelligence with “emotional data sets”, in order to build an “algorithmic intimacy” through those intelligent devices. The goal is to observe subjective/affective dimension of intimacy from the outside, to speak about human emotions as perceived by non-human eyes. Transposing them into a new shape helps Lorem to embrace a new perspective, and to re-approach removed experiences.

The three chapters of Adversarial Feelings move from three textual datasets. In the first chapter, the network “reads” stories and poems about complex emotional situations. The texts are classified according to Plutchick’s Wheel of Emotions, providing the basis for the simulation of new emotional patterns. The second dataset is composed of dozens of science fiction books about AI and its interactions with human beings: the network seems to investigate its own nature. Some texts and interviews on aliens have been included in the dataset, suggesting a correlation between the non-human nature of algorithmic intelligence and our understanding of extraterrestrial life. The third dataset was created by Mirek Amendant, an American artist/linguist, author of the cult Zaum Gadget (1988). Here the narrative focuses on the concept of Spiritual Machines and the relations between Zaum, Babel, Primordial Noise and sacred texts.