Emerging trends in hybrid information systems modeling in artificial intelligence

This chapter provides a comprehensive overview of emerging trends and challenges in developing hybrid information systems for artificial intelligence (AI) applications. Hybrid AI combines multiple techniques like machine learning, rule-based systems, and symbolic AI to create more robust and versatile solutions. The chapter elucidates the growing adoption of ensemble learning to improve model performance by aggregating diverse base model predictions. Combining neural networks with rulebased systems for enhanced decision-making capabilities is also explored. Additionally, the fusion of reinforcement learning and expert systems to enable agents that leverage both exploration-driven learning and domain knowledge is analyzed, along with associated techniques and challenges. For natural language processing, the chapter examines hybrid models capable of deeper language understanding and generation. Within computer vision, augmenting deep learning with traditional techniques for improved efficiency and transparency is discussed. Furthermore, the importance of explainable AI to interpret hybrid system outputs is emphasized, along with emerging techniques. Finally, the chapter addresses benchmarking complex hybrid models, including specialized evaluation metrics beyond accuracy and tailored benchmark datasets. Overall, it provides indispensable insights into the state of the art, open challenges, and interdisciplinary research shaping the future of hybrid AI.