Machine learning and deep learning architectures and trends: A review

Abstract

The arrival of machine learning (ML) together with deep learning (DL) has been revolutionizing many fields through advances in data-driven decision-making, automation, and predictive analytics. This has formed the keystone for the exploration of the most recent architectures and upcoming trends in said domains as to how they are significantly impacting other sectors. Recent ML designs, such as Transformers or graph neural networks (GNNs) in combination with neural differential equations, have found remarkable performance in tasks such as natural language processing (NLP) or recommendation systems and molecular modeling. The birth of big language models (LLMs)-from GPT-4 to BERT-has furthered the understanding and production of human languages to degrees where chatbots, translation, and content generation are advanced. At the same time, DL structures have evolved with the advent of state-of-the-art advancements, e.g., convolutional neural networks (CNNs) and generative adversarial networks (GANs), that play an essential role in areas such as image and video processing, autonomous driving, and synthetic data generation. This work focuses on how these structures may be combined with the advanced technologies of the Internet of Things with that of blockchain and quantum computing to enhance security, efficiency, and scalability for intelligent systems. Increasing trends show a concern with artificial intelligence (AI) and explainable AI (XAI) to deal with crucial problems of transparency, fairness, and accountability. The study also examines how federated learning is likely to influence privacy-driven data analysis and the surge in edge AI, which involves pushing computing closer to the source of data, reducing latency and improving real-time decision-making. This research will identify the importance of ML and DL, which is crucially important in showing the shape of technology and society in the future.

Topics & Concepts