Version 1
: Received: 8 February 2024 / Approved: 9 February 2024 / Online: 9 February 2024 (13:27:18 CET)
How to cite:
A Mageed, D.I. Great Minds Think Alike Entropy Artificial Intelligence and Knowledge Transfer. Preprints2024, 2024020563. https://doi.org/10.20944/preprints202402.0563.v1
A Mageed, D.I. Great Minds Think Alike Entropy Artificial Intelligence and Knowledge Transfer. Preprints 2024, 2024020563. https://doi.org/10.20944/preprints202402.0563.v1
A Mageed, D.I. Great Minds Think Alike Entropy Artificial Intelligence and Knowledge Transfer. Preprints2024, 2024020563. https://doi.org/10.20944/preprints202402.0563.v1
APA Style
A Mageed, D.I. (2024). Great Minds Think Alike Entropy Artificial Intelligence and Knowledge Transfer. Preprints. https://doi.org/10.20944/preprints202402.0563.v1
Chicago/Turabian Style
A Mageed, D.I. 2024 "Great Minds Think Alike Entropy Artificial Intelligence and Knowledge Transfer" Preprints. https://doi.org/10.20944/preprints202402.0563.v1
Abstract
An exposition of entropy applications to highlight some potential entropic applications to artificial intelligence and knowledge transfer. Entropy is a basic idea in artificial intelligence that is applied to many different tasks, such as reinforcement learning, data compression, and decision-making. By quantifying uncertainty and information content, it helps artificial intelligence models provide predictions and decisions that are well-informed. Therefore, this paper shines on spotlighting the significance of entropy to attract the attention of artificial intelligence research community to entropy as a powerful tool to advance artificial intelligence.The significance of knowledge transfer (KT), particularly intergenerational knowledge transfer (IGT), in knowledge management is also covered in this paper. To quantify the complexity of knowledge distribution inside an organisation and assess the efficacy of knowledge transfer (KT) initiatives, the notion of knowledge entropy (KE) is presented. Additionally, the KT model—which is based on the conceptions of tacitness and information content—is introduced. It combines personalisation and codification methodologies. Future research directions are offered alongside a few difficult open problems.
Keywords
Artificial intelligence (AI); Knowledge transfer (KT); intergenerational knowledge transfer (IGT); knowledge entropy (KE); Machine Learning (ML)
Subject
Business, Economics and Management, Human Resources and Organizations
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
