ADVANCES IN DEEP LEARNING ARCHITECTURES

Deep learning (DL) has redefined artificial intelligence by enabling machines to autonomously learn hierarchical representations from unstructured data, driving transformative progress in natural language processing (NLP), computer vision, and autonomous decision- making. This paper examines cutting-edge architectural innovations, including transformer models, graph neural networks (GNNs), self-supervised learning paradigms, and neuro- symbolic AI, which collectively address the limitations of traditional convolutional and recurrent networks. While these architectures achieve unprecedented accuracy in tasks like language translation and molecular discovery, challenges such as computational scalability, energy efficiency, and interpretability hinder their broader adoption. Case studies in healthcare, robotics, and climate modeling illustrate their societal impact, while emerging trends like sparse attention mechanisms and federated learning offer pathways to sustainable deployment. The synthesis of these advancements provides critical insights into the future of DL, emphasizing the need for ethical frameworks and interdisciplinary collaboration to advance artificial general intelligence (AGI).

Keywords: Deep Learning, Neural Networks, Transformers, Graph Neural Networks, Self- Supervised Learning, Artificial General Intelligence.