Deep Learning in AI Systems: Advancements and Applications in Computer Vision

Abstract

Deep learning has revolutionized artificial intelligence (AI), particularly in the field of computer vision, enabling machines to perceive, interpret, and analyze visual data with unprecedented accuracy. This paper explores the latest advancements in deep learning techniques, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformers, which have significantly improved image recognition, object detection, and video analysis. The integration of deep learning with real-world applications, such as autonomous vehicles, medical imaging, and facial recognition, is also examined, highlighting its transformative impact on multiple industries. Moreover, the study delves into challenges such as data dependency, computational requirements, and ethical concerns regarding bias and privacy. As deep learning continues to evolve, emerging trends like self-supervised learning and multimodal AI are expected to redefine the capabilities of computer vision. By analyzing the convergence of theoretical advancements and practical implementations, this research provides insights into the future trajectory of AI-driven computer vision systems. References from recent scholarly literature support the discussion, ensuring a comprehensive and up-to-date analysis of the subject.