Yannis Ioannidis

Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina et al.

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature. Recommender systems and assistants play a prominent role in human-AI coevolution, as they permeate many facets of daily life and influence human choices on online platforms. The interaction between users and AI results in a potentially endless feedback loop, wherein users' choices generate data to train AI models, which, in turn, shape subsequent user preferences. This human-AI feedback loop has peculiar characteristics compared to traditional human-machine interaction and gives rise to complex and often ``unintended'' social outcomes. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science focused on the theoretical, empirical, and mathematical investigation of the human-AI feedback loop. In doing so, we: (i) outline the pros and cons of existing methodologies and highlight shortcomings and potential ways for capturing feedback loop mechanisms; (ii) propose a reflection at the intersection between complexity science, AI and society; (iii) provide real-world examples for different human-AI ecosystems; and (iv) illustrate challenges to the creation of such a field of study, conceptualising them at increasing levels of abstraction, i.e., technical, epistemological, legal and socio-political.

Ektor Vrettakis, Vassilis Kourtis, Akrivi Katifori et al.

Storytelling has the potential to revolutionize the way we engage with cultural heritage and has been widely recognized as an important direction for attracting and satisfying the audience of museums and other cultural heritage sites. This approach has been investigated in various research projects, but its adoption outside research remains limited due to the challenges inherent in its creation. In this work, we present the web-based Narralive Storyboard Editor and the Narralive Mobile Player app, developed with the objective to assist the creative process and promote research on different aspects of the application of mobile digital storytelling in cultural heritage settings. The tools have been applied and evaluated in a variety of contexts and sites, and the main findings of this process are presented and discussed, concluding in general findings about the authoring of digital storytelling experiences in cultural heritage.

Evgeny Kharlamov, Yannis Kotidis, Theofilos Mailis et al.

Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens. OBDA approach has a great potential to facilitate such tasks; however, it has a number of limitations in dealing with analytics that restrict its use in important industrial applications. Based on our experience with Siemens, we argue that in order to overcome those limitations OBDA should be extended and become analytics, source, and cost aware. In this work we propose such an extension. In particular, we propose an ontology, mapping, and query language for OBDA, where aggregate and other analytical functions are first class citizens. Moreover, we develop query optimisation techniques that allow to efficiently process analytical tasks over static and streaming data. We implement our approach in a system and evaluate our system with Siemens turbine data.