Current Challenge

Current research addresses significant challenges in applying artificial intelligence (AI) across diverse fields, focusing on improving model performance, explainability, and real-world applicability. Key areas include enhancing the efficiency and robustness of AI models, particularly large language models (LLMs) and deep learning architectures like transformers and convolutional neural networks, for tasks such as image inpainting, dynamic question answering, and healthcare applications. These advancements aim to address limitations in areas like data scarcity, bias mitigation, and the need for more transparent and reliable AI systems, ultimately impacting various sectors from healthcare and manufacturing to public safety and environmental science.