Semantic Gap

The "semantic gap" refers to the mismatch between human understanding and machine representation of information, hindering effective communication between humans and AI systems across various domains. Current research focuses on bridging this gap using techniques like contrastive learning, attention mechanisms within transformer-based architectures (e.g., U-Nets, graph neural networks), and knowledge graph integration to improve the alignment between different modalities (text, images, numerical data). Addressing the semantic gap is crucial for advancing AI capabilities in diverse applications, including natural language processing, computer vision, and material science, ultimately leading to more robust and human-centered AI systems.