Developing a Speculative Encoding Algorithm with Hyperdimensional Computing and Neuro-Symbolic AI

Foreword

In the constantly changing world of artificial intelligence, the fusion of different paradigms often leads to groundbreaking advancements. One such promising intersection I explored is Hyperdimensional Computing (HDC) and Neuro-Symbolic AI.

This comprehensive guide dives deep into the development of a speculative encoding algorithm that harnesses the strengths of both HDC and Neuro-Symbolic AI, utilizing a Convolutional Neural Network (CNN) trained on the MNIST dataset. We’ll explore the intricate mathematical foundations, meticulously dissect the code implementation, examine a variety of potential use cases, and highlight the multifaceted benefits of this integrated approach.

Introduction

Artificial Intelligence (AI) has seen remarkable progress through various computational paradigms. Among these, Hyperdimensional Computing (HDC), also known as Vector Symbolic Architectures (VSA), stands out for its ability to represent and manipulate information in high-dimensional spaces. On the other hand, Neuro-Symbolic AI combines the learning prowess of neural networks with the reasoning strengths of symbolic systems.