The focus of this talk is not about my research in AI or Robotics but my own journey on trying to do research and understand intelligence in a rapidly evolving research landscape. I will trace my path from conducting early-stage research during graduate school, to working on practical solutions within a startup environment, and finally to my current role where I participate in more structured research at a major tech company. Through these varied experiences, I will provide different perspectives on research and talk about how my core beliefs on intelligence have changed and sometimes even been compromised. There are no lessons to be learned from my stories, but hopefully they will be entertaining.

In this 10-minute talk, followed by a scientific discussion, I will present my SNSF Starting Grant project, PREDICT, which investigates the neural and behavioural mechanisms underlying cognitive development from infancy through toddlerhood, childhood, and early adolescence. By bridging developmental neuroscience with advanced data analytics, PREDICT challenges traditional, often isolated methodologies and sets unique landmarks for understanding the interplay between early sleep markers, functional connectivity, and multisensory integration. Recognizing that understanding developmental trajectories requires multimodal approaches, PREDICT integrates cutting-edge methodologies - including 128-channel electroencephalography, actigraphy, eye-tracking, vestibular stimulation, and immersive VR - combined with cognitive testing, machine learning, and computational methods to: • Build predictive models and identify neural and behavioural markers of developmental trajectories. • Develop open-science protocols and pipelines for multimodal developmental data integration. Fostering an interdisciplinary, collaborative, and multimodal framework, PREDICT establishes benchmarks for the field, driving scientific, societal, and policy impacts by informing education, enhancing child health, and guiding early interventions, and delivering lasting benefits to researchers, practitioners, and policymakers.

The history of dress and fashion constitutes an essential yet frequently underappreciated dimension of art theory. While garments in visual art have long functioned as carriers of symbolism, identity, and social codes, dress analysis remains a marginal subject within mainstream art historical discourse. Despite the disciplinary challenges posed by its non-institutionalized status, scholars such as Rosita Levi Pisetzky—and many others—have produced foundational research that continues to shape our understanding of visual culture and material identity. This presentation draws on a multidisciplinary theoretical framework encompassing dress history, art history, social history, and semantics. It investigates the methodological potential of iconographic dress analysis in Early Modern artworks, with a particular focus on how clothing functions not merely as decoration but as a semiotic system embedded in the image. The aim is to explore how dress contributes to the construction of meaning within the pictorial space—revealing not only what is seen, but how and why it is seen. Moreover, what possible bridges can be built at the intersection of dress iconography and Artificial Intelligence in the study of Early Modern Art History?