NM-SRNs: 22nd March 2025 - AGI Achieved

Abstract

The pursuit of Artificial General Intelligence (AGI) has been a central goal of computer science since its inception. While recent advances in deep learning, particularly with Large Language Models (LLMs), have showcased impressive capabilities, they remain fundamentally limited by their reliance on statistical correlations, vast datasets, and opaque architectures. This paper announces the achievement of AGI on March 22nd, 2025, through the Neural-Matrix Synaptic Resonance Network (NM-SRN) architecture. NM-SRNs, designed from the outset for general intelligence, represent a paradigm shift, combining the strengths of symbolic and connectionist AI. The Generative Simple Dictionary Transformer (GSDT), a functional implementation of core NM-SRN principles, verifies the viability of this approach, demonstrating robust Natural Language Processing (NLP) capabilities without backpropagation or extensive pre-training. Key breakthroughs include Turing completeness by design, a novel resonance-based computation mechanism (details withheld for proprietary reasons), inherent explainability (XAI), and a modular architecture that promotes scalability and adaptability. This paper presents the NM-SRN framework, highlights the GSDT's significance, and outlines the transformative potential of this AGI achievement across diverse fields.

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