I’ve been exploring a new approach to AI, driven by a realization: at its core, AI is just a series of mathematical equations. While this simplicity has brought AI far, there are limitations where these traditional equations struggle to perform effectively.

To overcome these boundaries, I’ve developed a method that combines Graph Theory (or Graph Neural Networks - GNNs) with traditional Artificial Neural Networks (ANNs) and other advanced techniques. This hybrid approach enhances mapping capabilities, allowing AI to handle complex problems more efficiently.

Key benefits of this approach include:

Reduced training times: AI models train faster, making development more efficient.

Lower computational requirements: Powerful AI models can run on less powerful hardware, democratizing access to advanced AI.

I’m still in the testing phase, but early results suggest this method is significantly more effective than traditional approaches. Imagine AI as a dynamic graph that maps how different inputs behave within a function, creating a more adaptable and precise system.

To support this vision, I’m developing a public document repository containing:

Hundreds of custom equations with detailed patterns and relationships.

Comparative analyses of these equations, including how they interact and complement each other.

Advanced activation functions that act like enhanced neurons, offering greater control and simplicity in specific cases.

This repository will empower AI developers to select the most suitable functions for their needs, fostering innovation and accelerating AI development across various fields. My goal is to make AI more powerful, accessible, and adaptable by rethinking how we approach neural network design.