Predictive Artificial Intelligence: A Theoretical Analytical Study of Foundations and Concepts
DOI:
https://doi.org/10.21814/h2d.6990Keywords:
Artificial Intelligence, Prediction, Data Analysis, Models, Foundations, ConceptsAbstract
This research addresses the topic of predictive artificial intelligence from a theoretical analytical perspective, aiming to clarify the foundations and concepts upon which this field is built. It focuses on the fact that artificial intelligence fundamentally relies on machine learning and deep learning techniques, which extract patterns from vast datasets and utilize them to construct models capable of forecasting the future with varying degrees of accuracy. The research also discusses the philosophical dimension of prediction, the limits of machines in surpassing human intellect, in addition to highlighting the importance of probability and uncertainty in predictive outcomes. It refers to challenges associated with data bias, ethical dimensions, and the risks of excessive reliance on machines in decision-making. The research concludes that predictive artificial intelligence represents a powerful tool for understanding the future, yet it remains governed by theoretical foundations and ethical considerations that limit its accuracy and necessitate the integration of human roles with technological ones.
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