NM-SRN v2.0 AGI Level 1 running on ESP32 - 3rd May 2025

Abstract This paper presents a significant breakthrough: the successful execution of the Neural-Matrix Synaptic Resonance Network (NM-SRN) v2.0, demonstrating AGI Level 1 capabilities, on an ESP32 microcontroller platform via the Wokwi simulator. This achievement highlights the potential of NM-SRN as a transparent, explainable, and inherently 'Safer by Design' Artificial General Intelligence architecture. We provide visual evidence from the simulation, showcasing core NM-SRN functionalities operating within the constraints of edge hardware, including resonance-based propagation and Fast Forward Learning (FFL) enabled by default through its Turing complete design. This work, part of the open-source 'ThinkSpace' PROJECT, validates the practical viability of running advanced, adaptable AI models in accessible, real-world environments. Source code is omitted due to intellectual property considerations.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-06-17
DOI
https://doi.org/10.5281/zenodo.15680457
Primary Topic
Statistical Methods in Epidemiology
Type
article
Field-Weighted Citation Impact
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article

NM-SRN v2.0 AGI Level 1 running on ESP32 - 3rd May 2025

Zenodo (CERN European Organization for Nuclear Research)
Statistical Methods in Epidemiology
article

NM-SRN v2.0 AGI Level 1 running on ESP32 - 3rd May 2025

article en

Abstract

Abstract This paper presents a significant breakthrough: the successful execution of the Neural-Matrix Synaptic Resonance Network (NM-SRN) v2.0, demonstrating AGI Level 1 capabilities, on an ESP32 microcontroller platform via the Wokwi simulator. This achievement highlights the potential of NM-SRN as a transparent, explainable, and inherently 'Safer by Design' Artificial General Intelligence architecture. We provide visual evidence from the simulation, showcasing core NM-SRN functionalities operating within the constraints of edge hardware, including resonance-based propagation and Fast Forward Learning (FFL) enabled by default through its Turing complete design. This work, part of the open-source 'ThinkSpace' PROJECT, validates the practical viability of running advanced, adaptable AI models in accessible, real-world environments. Source code is omitted due to intellectual property considerations.

Zenodo (CERN European Organization for Nuclear Research)
Biokine (Israel) (IL), Neural Signals (United States) (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 52%
Statistical Methods in Epidemiology
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NM-SRN v2.0 AGI Level 1 running on ESP32 - 3rd May 2025 · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS