Methods for improving artificial intelligence algorithms based on BIG DATA

Abstract

In the modern era of digital transformation, the integration of Big Data technologies and artificial intelligence (AI) systems has become one of the fundamental drivers of technological advancement. Efficient processing and analysis of massive datasets significantly enhance the accuracy, scalability, and adaptability of AI algorithms. This review article examines the role of Big Data platforms such as Hadoop, Spark, and Apache Flink in improving the performance of AI models. Special attention is paid to data collection, storage, and preprocessing stages that influence algorithmic efficiency. The study explores various machine learning and deep learning methods operating on Big Data foundations, including neural network scaling, data correlation computation, automated feature selection, and modeling techniques. Real-time information derived from large-scale data enables AI systems to dynamically adapt and generate predictive insights. Furthermore, the paper discusses ethical and technical challenges related to Big Data applications, such as data quality degradation, limited computational capacity, and privacy issues. The authors emphasize that the effective use of Big Data ecosystems represents a crucial factor for advancing AI capabilities, particularly in economics, healthcare, and industrial sectors.