MEDIA INTELLIGENCE: AI-DRIVEN INNOVATIONS AT THE CROSSROADS OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY

Abstract

The exponential growth of digital content across multimedia platforms has necessitated the emergence of advanced systems capable of understanding, analyzing, and acting upon complex media data. Media intelligence, as a rapidly evolving interdisciplinary field, leverages the convergence of artificial intelligence (AI), computer science, and information technology (IT) to transform raw media into actionable insights. This paper explores the state-of-the-art in AI-driven media intelligence, focusing on its theoretical foundations, technological architectures, practical applications, and emerging ethical challenges. Using a systematic literature review and thematic synthesis approach, the study examines key innovations in natural language processing, computer vision, multimodal learning, and predictive analytics. It also analyzes the integration of AI in domains such as journalism, public health, social media analysis, and content moderation. Particular emphasis is placed on the role of transformer-based models, real-time media pipelines, and hybrid neuro-symbolic frameworks in enabling intelligent media systems. The findings reveal that while AI significantly enhances the interpretive and predictive power of media analytics, it simultaneously introduces critical concerns around bias, transparency, surveillance, and digital governance. The paper concludes by outlining future research directions, including the potential of edge AI, quantum computing, and ethical-by-design frameworks, and calls for interdisciplinary collaboration to ensure that media intelligence systems are robust, fair, and aligned with societal values