Multimodal AI: The future of integrated intelligence

Subramanian, Peraschi Selvan (2025) Multimodal AI: The future of integrated intelligence. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 1552-1559. ISSN 2582-8266

This article explores the transformative potential of multimodal artificial intelligence systems, which integrate diverse data types including text, images, video, and audio into unified computational models. By seamlessly combining multiple sensory modalities, these advanced frameworks enable more nuanced perception, interpretation, and response capabilities that parallel human cognitive processes. The architectural foundations of multimodal AI, including cross-modal learning techniques, modular architectures, and representation learning strategies, establish robust platforms for sophisticated data integration. Technological breakthroughs such as contrastive learning, dilated attention mechanisms, and multimodal transformers have addressed critical efficiency and performance barriers. The impact of these innovations extends across healthcare, autonomous systems, creative industries, and education, enabling unprecedented applications from disease progression prediction to enhanced artistic expression. As multimodal AI continues to mature, it promises to redefine the boundaries of human-computer interaction and establish new paradigms for artificial intelligence that more holistically engage with complex real-world environments.