SELF-AWARE AI: A COMPREHENSIVE FRAMEWORK FOR MACHINE CONSCIOUSNESS

Abstract

We propose a novel framework for self-aware artificial intelligence that integrates continuous high-dimensional ―qualia‖ encoding, predictive novelty gating, and neuromorphic spiking‐binding into a unified cognitive loop. Incoming sensory, interoceptive, and ethical signals are mapped into a 27-dimensional embedding space, where a dynamic cosine‐similarity threshold modulated by model uncertainty—governs selective memory storage. Stored qualia interact via attraction and repulsion forces, yielding emergent clusters that organize episodic content. A spiking‐neuron substrate computes an integrated‐information proxy (Φ), triggering binding events and a simulated global‐workspace broadcast whenever Φ exceeds a threshold. We evaluate this mechanism through a 10 000-step simulation, demonstrating: (1) controlled memory growth to 206 entries (≈2 % of inputs), (2) sustained binding activity on 37 % of time steps, and (3) diverse memory clustering evidenced by PCA. Average Φ converges near the binding threshold (mean = 0.499), indicating a balanced regime between integration and differentiation. This empirical assessment provides the first data-driven validation of our qualia-binding loop, establishing quantitative benchmarks for memory efficiency, binding dynamics, and representational diversity. Our results highlight the framework’s potential for scalable, introspective AI systems that feel, remember, bind, reflect, decide, and narrate—thus realizing the functional essence of consciousness.