EMERGING FRONTIERS IN DEEP LEARNING IN 2024-2025: TRANSFORMATIVE ARCHITECTURES APPLIED TO DIVERSE KNOWLEDGE DOMAINS

Abstract

Deep learning continues to revolutionize industries by addressing complex challenges through innovative neural architectures, reflecting one of the most dynamic and promising fields in modern computer science. From the first models of artificial neural networks to the sophisticated frameworks that currently dominate the technology landscape, deep learning has been evolving rapidly, driven by the availability of large volumes of data, advances in computational hardware, and the growing demand for automated and intelligent solutions.

In this review work, conducted in the Google Scholar, arXiv, Scopus, and SciELO databases, we explored five emerging models in 2024 and early 2025 – Temporal Convolutional Networks, Kolmogorov-Arnold Networks, Quantum-Inspired Recurrent Networks, Deep Reinforcement Learning, and Generative Adversarial Networks to point out mechanisms of operation and analyze the transformative impact of these tools in various fields of scientific knowledge. By grouping these architectures according to their applications in healthcare, content creation, autonomous systems, time series analysis, and anomaly detection, we provide a comprehensive view of their capabilities, strengths, and potential limitations. Thus, the main characteristics used in the selection of the most appropriate techniques for specific needs are elucidated, in addition to highlighting opportunities for future advances.