Abstract The process of learning and knowledge transfer is governed by highly organized systems, both in biological intelligence and artificial models. This paper explores the parallels and interactions between the human brain's architecture, specifically neural circuits and pathways, and the computational structure of artificial neural networks (ANNs). In human learnography, brain regions such as the prefrontal cortex, hippocampus and cerebellum collaborate through synaptic plasticity and motor science to build memory and skillsets. Similarly, ANNs utilize the layers of interconnected nodes to simulate these mechanisms, enabling machines to learn from data, recognize patterns, and make informed decisions. By examining the learning parts and structures in both systems, this study highlights how biological insights inspire artificial intelligence and how ANNs, in turn, reflect core principles of brain-based knowledge transfer. The convergence of these learning paradigms offers a dee...

Human learnography and machine learnography follow distinct, but interconnected paths in knowledge transfer. While human learnography develops brainpage maps and modules through motor science and task-based learning , machine learnography processes data using artificial neural networks and AI models. Neural Networks & Deep Learning: Future of Artificial Intelligence In this article, we delve into their similarities, differences, and how brain-computer interfaces and AI-powered academic learning could revolutionize the future of knowledge transfer. While human brain uses motor science, brainpage development and task-oriented learning, machine intelligence relies on artificial neural networks, deep learning and reinforcement learning. Highlights: Learnography Relying on Structured and Adaptive Learning Mechanisms Understanding Human Learnography Understanding Machine Learnography Parallels Between Human Learnography and Machine Learnography Key Differences Between Human and Machine ...