Many intelligent systems are optimized for the average user. With personal cognitive systems, we envision AI systems that are highly personalized and that continuously adapt to a specific individual, reflecting an individual’s abilities, preferences, and emotions. In the context of the ERC project, "AI-Twins of Human Experience," we are investigating how to move beyond generic, universal AI tools to personalized digital cognitive systems that continuously learn from users’ experiences, interactions, and reactions. Our goal is to lay the foundation for digital systems that facilitate human-AI partnerships, amplifying cognition, creativity, and well-being. Physiology-driven learning and adaptation are key to realizing this vision.
Accurate prediction of future trajectories in unstructured environments is essential for safe autonomous vehicle operation, especially when pedestrians, cyclists, and motor vehicles interact in complex and unpredictable ways. Many existing prediction models predict based on a single class or rely on multiple separate graphs for multi-class prediction. This work introduces a lightweight trajectory prediction framework that represents all agents within a single Comprehensive Heterogeneous Graph, enriched with a directional interaction mask that highlights behaviourally important neighbours based on relative distance, velocity, and motion direction. A spatial-temporal design combining a Spatial-Temporal Graph Convolutional Network with a micro Temporal Convolutional Network allows efficient modeling of motion dynamics, while a Gaussian mixture model decoder enables probabilistic training and deterministic maximum likelihood prediction for simulation. Experiments on the Stanford Drone Dataset show improved accuracy over recent multi-class baselines, and qualitative visualizations confirm stable, socially coherent, and scene-consistent predictions suitable for real-time autonomous systems.
Digital Twins (DTs) have emerged as a critical technology for enhancing the management and optimization of buildings, enabling real-time monitoring, predictive maintenance, and energy efficiency through the integration of Internet of Things (IoT) sensors and advanced data analytics. In the context of smart buildings, in particular, DTs are essential in bringing the best architectural practices for real-time processing of sensor data, building information models, and supporting efficient operation, energy management, and occupant comfort. This paper critically examines recent advancements in applications of sensor-driven DTs for smart buildings, and highlights the main aspects of their technical solutions, including types of sensors, use-case problems, visualization approaches, as well as continuously present challenges in these applications.
Fully decentralised AI challenges the long-standing assumption that intelligence must reside in a centralised infrastructure. By allowing data to remain at its source and enabling models to evolve through peer-to-peer interactions, decentralised learning opens the door to AI systems that are privacy-preserving, sovereign, and deeply embedded in their operational environments. However, removing the center also reshapes the problem space. How does collective intelligence arise from the structure of the underlying communication graph? What guarantees of convergence and robustness can we provide in the presence of unreliable links, heterogeneous data, or even adversarial behavior? And how can meaningful knowledge emerge when local signals are noisy or imperfect? This keynote examines decentralisation not merely as a technical design choice, but as a conceptual shift. By embracing network structure, resilience, and collaboration as first-class elements, we can move toward AI systems that behave less like monolithic engines and more like adaptive, distributed ecosystems.
