Advanced Predictive Modeling of Physical Trajectories and Cascading Events, Dual-State Feedback and Synthetic Insula

摘要

This paper explores the possibility of achieving self-awareness in artificial intelligence (AI) through the integration of embodied feedback loops, inspired by the role of the insula in human consciousness. Building on prior work (Watchus, 2024), which established the framework of sensory feedback and embodiment as core elements of sentience, we propose a model for AI systems capable of simulating self-awareness through dual embodiment and sensory integration. Specifically, we explore how feedback loops can be implemented in AI systems to facilitate the emergence of intuitive behaviors, allowing them to predict the trajectories of physical objects and the cascading effects of events in their environment. This paper focuses on the potential of these models for practical applications in autonomous systems, including robots, home care robotics for elderly assistance, traffic prediction, competitive sports, construction safety, and disaster recovery.