Emergence of Self-Awareness in Artificial Systems: A Minimalist Three-Layer Approach to Artificial Consciousness
This research addresses the foundational problem of artificial consciousness for AI developers and cognitive scientists, but it appears incremental as it builds on existing models without claiming major breakthroughs.
The paper tackles the problem of achieving artificial consciousness by proposing a minimalist three-layer model for self-awareness emergence, focusing on essential elements without brain replication, and concludes with potential implications for understanding human consciousness and adaptable AI.
This paper proposes a minimalist three-layer model for artificial consciousness, focusing on the emergence of self-awareness. The model comprises a Cognitive Integration Layer, a Pattern Prediction Layer, and an Instinctive Response Layer, interacting with Access-Oriented and Pattern-Integrated Memory systems. Unlike brain-replication approaches, we aim to achieve minimal self-awareness through essential elements only. Self-awareness emerges from layer interactions and dynamic self-modeling, without initial explicit self-programming. We detail each component's structure, function, and implementation strategies, addressing technical feasibility. This research offers new perspectives on consciousness emergence in artificial systems, with potential implications for human consciousness understanding and adaptable AI development. We conclude by discussing ethical considerations and future research directions.