Emergence of Self-Awareness in Artificial Systems: A Minimalist Three-Layer Approach to Artificial Consciousness

TL;DR

This paper introduces a minimalist three-layer model for artificial consciousness, demonstrating how self-awareness can emerge from layer interactions without explicit programming, offering new insights into AI consciousness.

Contribution

The paper presents a novel three-layer model that enables self-awareness emergence in artificial systems through minimal essential components, diverging from brain-replication approaches.

Findings

Self-awareness emerges from layer interactions and dynamic self-modeling.

The model demonstrates technical feasibility of minimal consciousness in AI.

Potential implications for understanding human consciousness and developing adaptable AI.

Abstract

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.