In the urban sphere, AI is often associated with the concept of smart cities and thus the use of technology and the enormous computing power of machines to increase the quality of life for citizens, creating greater efficiency in resources and services, but there are further applications. The sprawl of information technologies is changing relationships between people and space, contributing to the mutation of iconographic production and the way content is conceived and communicated. Digital management and communication processes focus on images. The set of images constitutes a highly evocative language; it can be immediately comprehensible or require decoding that refers to specific contexts and cultures. Their importance is evident in human-targeted communication, but they also constitute a data transmission vehicle for empirical knowledge generation by algorithms like those used for artificial image creation from other images. Text-to-image AI generators are trained through the analysis of hundreds of millions of images and their related textual descriptions, which allows the system to learn the relationship between text and visual elements. Through this process, the network is also able to infer other information about reality. Images can be the representation of actual objects, as well as a subjective product of imagination or sensory processing. So too are the images created by Italo Calvino’s Invisible Cities, which bear witness to mental and non-geographical spaces. Cities that cannot be seen can be constructed from their poetical descriptions. What textual variables affect the realization of an image? How do the datasets that different text-to-image tools draw on affect image realization? Can these tools be trained from the user’s realization of an image? The paper collects the first outcomes of a research project comparing the results produced by the main image-generation tools from text descriptions extracted from Calvino’s book.
Proposal for a Data Visualization and Assessment System to Rebalance Landscape Quality
The landscape can be considered a complex system described as a non-linear entity, organized accord-ing to the connections between the different elements that characterize its state. The latter cannot be determined a priori but emerges from the multiple interactions between assets and relationships that are no longer found when the phenomenon is traced back to the individual components. Territorial development is closely connected to social, economic, cultural and symbolic issues that determine the transformative practices of space and the territorial palimpsest. If not carefully managed, these forces can lead to the dissolution of the landscape and environmental values that have been stratified in a specific land. This paper proposes the construction of a numerical spatial model that describes the territorial settlement patterns, based on methodologies and techniques typical of AI, to develop tools for re-balancing the man-landscape relationship.
Artificial Intelligency, Big Data and Cultural Heritage
In recent decades, the cultural heritage sector has benefited from solutions offered by ICT for the conservation, management, enhancement and communication of cultural heritage; today this specific sector benefits from the infinite potential of application of AI. The proposed research identifies the three main lines of research that operate in the cultural heritage exploiting the synergy between machine learning, big data and AI, starting from the analysis of the state of the art and a subsequent first taxonomic approximation of artificial intelligence systems. The analysis of some case studies developed in the field of recovery and restoration of cultural heritage, monitoring and prevention of damage, data acquisition and analysis of the same, confirm the real potential of AI: trigger knowledge from knowledge.