What Artificial Intelligence Lacks to Think Like a Human: A UDMM-Based Perspective

Executive Summary This report examines the fundamental limitations of Artificial Intelligence (AI) in emulating human thought, utilizing the Unified Dynamic Model of the Mind (UDMM) as a comprehensive cognitive framework. While AI has achieved remarkable feats in specific domains, its current paradigms fundamentally diverge from the UDMM's integrated, dynamic, and embodied understanding of human cognition. The analysis indicates that AI's shortcomings are not merely about missing features but about a fundamentally different, less integrated, and less dynamic architecture of understanding and interaction with reality. Key deficiencies in AI include the absence of true purposeful intent, a lack of embodied emotion and affective cognition, incomplete internal models of reality, challenges in continuous self-modelling and temporality, and discrepancies in robust predictive processing and error correction. These gaps highlight that AI's statistical pattern matching and disembodied operations prevent it from achieving the holistic, adaptive, and value-driven intelligence characteristic of the human mind. Addressing these deficiencies requires a paradigm shift in AI development, drawing directly from UDMM principles to foster more cognitively aligned, emotionally intelligent, and self-aware systems, while also navigating profound ethical considerations.