Analysis of AI Algorithm Development: From Machine Learning to Deep Learning - Jompon Pitaksantayothin; Hariyadi Fajar Nugroho (Artificial Intelligence Systems and Its Applications, 2025)
The development of Artificial Intelligence (AI) is currently very rapid, but there is still much confusion regarding the differences and evolution of the main algorithms, namely machine learning (ML) and deep learning (DL). This study aims to analyze the development of AI algorithms conceptually and technically from conventional ML to DL, and to provide a structured understanding of the paradigm shift in AI development. The method used is a systematic literature study of 10 recent scientific articles discussing aspects of ML and DL algorithms. The results of the analysis show that ML relies on manual feature extraction with the advantages of computational efficiency and interpretability, while DL is able to process large and complex data automatically with better performance, although it requires high computing resources and faces interpretability challenges. The discussion also identifies the main challenges that AI still faces as well as innovation opportunities to overcome these limitations. In conclusion, a deep understanding of the evolution of AI algorithms is essential as a foundation for the development of more adaptive, effective, and transparent AI technology in the future.