If you would like to know how exactly it works here is part one

Adam Allison — Fri, 06 Dec 2024 13:30:43 +0000

Right now as I'm writing this I'm on the bus so mind my spelling and Grammer.

First what happens is you look at the line you want to make. You will have a library full of equations and patterns and you will choose what patients or equation seems to fit well or has keywords that matches the line you want to match. After that you will get the equations and modify there variables to match sections in the line you are trying to match. You repeat this step modifying and translating the graph.

After this you can take the error the line you made in relation to the line you want to match. Now find the line of best for the error. Then you add those two together to get the new equation.

Some questions you may have are:

How is this more effective at training. Answer: Well with this instead of having to basically guess how many neurons you would need in your nn; you will just be able to map it. This will also help against over fitting or overcomplicating a network. It can also help you run bigger networks on less powerful devices.
How will I know what equation to use? Answer: in the library it will have multiple sections including images each aqation can give you and more on how they can modify. This way you can easily find equations you need.
Wouldn't it be better to just use simpler equations? Answer: in allot of cases no, each neuron in this method will have more control individually and can most cases simplify networks and make it easier for non so devs to learn.
How would this work on 2 inputs network or more? Answer: this is still in development but will be explained in my next post.

New AI idea

Adam Allison — Thu, 05 Dec 2024 22:27:05 +0000

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.

DEV Community: Adam Allison

If you would like to know how exactly it works here is part one

New AI idea