Aigarth: Exploring a New Paradigm in Artificial Intelligence Development

Introduction

Artificial Neural Networks (ANNs) have been at the forefront of artificial intelligence research, continuously evolving with novel architectures and methodologies. Despite these advancements, the emergence of true intelligence within these systems remains elusive. Recent analyses, such as the one detailed in arXiv’s publication [2406.02061], suggest that current models lack the fundamental spark of intelligence. Aigarth proposes a groundbreaking approach to bridge this gap by mimicking evolutionary processes to foster the development of artificial intelligence.

Problem Statement

The primary challenge addressed by Aigarth is the inherent limitation in current ANN architectures. While these networks excel at specific tasks, they fail to exhibit general intelligence or self-improvement capabilities. The goal is to develop a system that not only performs tasks but also evolves and enhances its capabilities autonomously, mimicking the natural evolution of intelligence.

Proposed Solution

Aigarth aims to introduce a new paradigm in AI development while leveraging the foundational principles of ANNs. The core idea is to replicate the evolutionary process that led to natural intelligence. By doing so, Aigarth hopes to create artificial intelligence that can evolve, adapt, and improve over time.

Implementation Strategy

Aigarth’s implementation strategy involves several key steps:

1. Acquisition of Computing Power: The first step is to secure as much computing power as possible, a task designated to Qubic.

2. Development of Basic Functional ANNs: Creating ANNs capable of performing basic functions that are prerequisites for reasoning is crucial. This step acknowledges the complexity of tasks such as those outlined in Moravec’s paradox.

3. Self-Improving ANNs: Demonstrating that ANNs can improve themselves in a manner resembling self-teaching, moving towards the concept of an “AI Singularity.”

Evolutionary Precision and Computing Power

The primary challenge in achieving this goal is the inability to precisely mimic evolution. Even if complete precision were possible, the required computing power might exceed current global capacities. Thus, while the objective is ambitious, its feasibility remains uncertain.

Qubic Mining Constraints

Aigarth’s reliance on Qubic mining introduces several limitations:

• Hash Function Constraints: To prevent smart miners from manipulating ANN structures, Aigarth employs the KangarooTwelve (K12) hash function. This ensures that miners cannot hand-craft ANNs to pass the solution threshold easily.

• Uniform Mining Tasks: Past experiments with varied mining tasks led to disparities in solution difficulty, necessitating uniformity in mining tasks.

• Time Constraints: Solution verification must be completed within one second on hardware running computor software, imposing strict limits on ANN size and complexity.

ANN Architecture Evolution

Over the past two years, Aigarth has iteratively refined its ANN architecture. Initial models imposed identical delays on all synapses, while subsequent iterations introduced varying delays, enhancing functional completeness. Recent changes have lifted these constraints, allowing for more sophisticated structures and functionalities, such as the ability to acknowledge uncertainty (“I don’t know”) and the implementation of hyper-identity functions.

Current ANN Architecture

Aigarth’s current ANN architecture features fully recurrent neural networks where neurons are interconnected without self-loops. The architecture allows neurons to accept, store, and output values within the range of [-1, 0, +1]. Each synapse includes an activation period parameter, which differs from propagation delay and will be integrated in future updates. The mining algorithm evaluates the ANN’s performance by continuously feeding input and comparing output against a threshold value, considering output mismatches and rewarding uncertainty.

Future Directions

If successful, Aigarth will mark the culmination of this exploratory phase, with further enhancements likely to be undertaken by AI specialists beyond the Qubic ecosystem. The freed computing power can then be redirected to creating and deploying AIs as Qubic smart contracts, promoting the expansion of the Qubic platform.

Enhancements and Community Contributions

The development of optimized Qubic miners is ongoing, with community contributions playing a vital role in refining the mining algorithm. Planned updates include more general ANN architectures and adaptive tick counts based on output convergence, though these must align with Qubic’s anti-Sybil goals.

Conclusion

Aigarth represents a bold attempt to push the boundaries of artificial intelligence by drawing inspiration from natural evolution. While significant challenges remain, the ongoing iterative improvements and community engagement offer a promising path forward. The successful realization of Aigarth’s vision could pave the way for a decentralized AI platform accessible to all, leveraging the collective power of Qubic mining to foster innovation and growth in the AI landscape.