Abstract Concept

Research on abstract concept representation aims to understand how artificial systems can acquire, represent, and reason with abstract knowledge, mirroring human cognitive abilities. Current efforts focus on leveraging multimodal data, contrastive learning, and large language models (LLMs), often incorporating knowledge graphs and logic-based reasoning to improve consistency and generalization. These investigations are crucial for advancing artificial general intelligence (AGI) and improving the explainability and reliability of AI systems across various applications, including natural language processing and computer vision. The ultimate goal is to build AI systems that can not only process information but also understand and reason about complex, abstract concepts.