The Evolution and Architecture of Multimodal AI Systems
DOI:
https://doi.org/10.32628/CSEIT251112108Keywords:
Artificial Intelligence, Cross-Modal Integration, Distributed Computing, Neural Architecture, System PerformanceAbstract
This technical article explores the evolution, architecture, and implementation challenges of multimodal AI systems, which represent a significant advancement in artificial intelligence. The article explores how these systems integrate multiple input modalities to achieve comprehensive understanding and analysis capabilities, mirroring human cognitive processes. Through detailed analysis of system architectures, performance metrics, and implementation strategies, we investigate the current state of multimodal AI across various applications, from virtual assistants to healthcare analytics. The article covers core technical components, data synchronization challenges, resource optimization techniques, and future directions in the field, providing insights into both theoretical frameworks and practical implementations.
Downloads
References
Nikolaos Rodis, et al., "Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions," IEEE Access ( Volume: 12). [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10689601
Shezheng Song, et al., "How to Bridge the Gap between Modalities: A Comprehensive Survey on Multi-modal Large Language Model," Journal Of Latex Class Files, Vol. 14, No. 8, August 2023. [Online]. Available: https://arxiv.org/pdf/2311.07594
Yiqiao Jin, et al., "MM-SOC: Benchmarking Multimodal Large Language Models in Social Media Platforms," arXiv:2402.14154v3 [cs.CV], Feb. 2024. [Online]. Available: https://arxiv.org/pdf/2402.14154v3
Sarbaree Mishra, et al., "Cross modal AI model training to increase scope and build more comprehensive and robust models," Journal of AI-Assisted Scientific Discovery, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/246/234
Francesca Castaldo, et al., "Multi-modal and multi-model interrogation of large-scale functional brain networks," NeuroImage, Volume 277, 15 August 2023, 120236. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1053811923003877
Muhammad Farooq, "An Adaptive System Architecture for Multimodal Intelligent Transportation Systems," arXiv:2402.08817v1 [cs.CL], Feb. 2024. [Online]. Available: https://arxiv.org/pdf/2402.08817v1
Felix Krones, et al., "Review of multimodal machine learning approaches in healthcare," Information Fusion Volume 114, February 2025, 102690. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1566253524004688
Shakti N. Wadekar, et al., "The Evolution of Multimodal Model Architectures," arXiv:2405.17927v1 [cs.AI], May 2024. [Online]. Available: https://arxiv.org/pdf/2405.17927v1
Xingguang Peng, "Multimodal Optimization Enhanced Cooperative Coevolution for Large-Scale Optimization," IEEE Transactions on Cybernetics ( Volume: 49, Issue: 9, September 2019). [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8405748
Wen Gao, et al., "Parallel Task Scheduling in Autonomous Robotic Systems: An Event-Driven Multimodal Prediction Approach," ICPP '24: Proceedings of the 53rd International Conference on Parallel Processing, 2024. [Online]. Available: https://dl.acm.org/doi/abs/10.1145/3673038.3673147
Debashri Roy, "Going beyond RF: A survey on how AI-enabled multimodal beamforming will shape the NextG standard," Computer Networks, Volume 228, June 2023, 109729. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S1389128623001743
Wei Chen , et al., "New Ideas and Trends in Deep Multimodal Content Understanding: A Review," Neurocomputing, Volume 426, 22 February 2021, Pages 195-215. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0925231220315939
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.