Enhancing AI Through Statistical Methods for Improved Decision-Making
DOI:
https://doi.org/10.29304/jqcsm.2024.16.41800Keywords:
Artificial Intelligence, Machine Learning Algorithms, Bayesian Inference, Regression Analysis, AI-Statistical Hybrid ApproachesAbstract
This study examines the crucial relationship between Artificial Intelligence (AI) and statistics, which has grown in importance due to the information boom and computational advances. Modern data-intensive applications have led these domains to merge, despite their parallel history. This document explores AI's fundamentals, focusing on machine learning and deep learning, and how statistical methods help AI perform tasks like decision-making and pattern identification. We integrate AI with robust statistical modelling and prediction to improve AI transparency and effectiveness. This multidisciplinary approach emphasizes theoretical advances, practical applications, ethical considerations, and future problems at the interface of AI and statistics. We encourage AI and statistics communities to collaborate to promote innovation and responsible AI development and deployment. This collection of publications is a comprehensive resource for researchers and practitioners using AI and statistics to better decision-making and predictive analytics
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Copyright (c) 2024 Hadeel Kamil Habeeb , Zainb Hassan Radhy
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
