Advancements in Artificial Intelligence for Real-World Problem Solving: Foundations, Methods, and Applications

Abstract: This paper surveys recent advancements in artificial intelligence (AI) that have directly improved the capability of systems to solve real-world problems. We review progress in foundation models and multimodal systems, generative models (diffusion and transformer families), human-in-the-loop alignment (RLHF), and privacy/resilience techniques for deploying AI at the edge (federated/TinyML). Building on the literature, we identify important gaps in robustness, evaluation, and societal alignment, then propose a methodology combining multimodal pretraining, task-specific fine-tuning with human feedback, and privacy-preserving edge deployments to address practical tasks in healthcare triage, environmental monitoring, and robotics. Experimental designs, datasets, metrics, and ethical safeguards are provided to enable reproducible, responsible research.