Conscious Learning Machines (Introductory) Paper

Abstract

This paper introduces a novel new approach to machine learning, termed Conscious Learning (CL) Machines, which aims to more accurately model the human brain’s hierarchical structure and function. The proposed Conscious Learning (CL) Machines utilize a revolutionary b-neuron cluster design, incorporating principles from the Internet of Things (IoT), to create a more efficient, effective, and potentially transformative Machine Learning system. This paper discusses the design of the b-neuron cluster, its significant advantages over current Deep Learning practices, and the exciting potential implications for performance, discovery, and applications in cutting-edge fields like generative AI and robotics.

Introduction

Deep Learning has revolutionized the field of machine learning, enabling unprecedented advancements in areas such as image recognition, natural language processing, and speech recognition. However, current Deep Learning models, which typically use a single type of neuron(perceptron) and heavily centralized processing, may not fully capture the complexity and diversity of the human brain. In contrast, the proposed Conscious Learning(CL) Machines utilize a b-neuron cluster design, where each digital neuron in the cluster mimics a different type of biological neuron.