The Evolution and Architecture of Multimodal AI Systems

Authors

  • Bhabani Sankar Nayak University of Illinois at Urbana-Champaign (UIUC), USA Author

DOI:

https://doi.org/10.32628/CSEIT251112108

Keywords:

Artificial Intelligence, Cross-Modal Integration, Distributed Computing, Neural Architecture, System Performance

Abstract

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.

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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

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Published

20-01-2025

Issue

Section

Research Articles

How to Cite

The Evolution and Architecture of Multimodal AI Systems. (2025). International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 1007-1017. https://doi.org/10.32628/CSEIT251112108