Modelling Commonsense

Modeling commonsense reasoning in artificial intelligence aims to equip machines with the everyday knowledge and understanding that humans effortlessly possess. Current research focuses on developing robust knowledge representations, such as multi-faceted concept embeddings and large-scale knowledge graphs, often incorporating symbolic knowledge distillation and fine-tuning of pre-trained language models. These efforts leverage both structured and unstructured data to improve performance on tasks like commonsense inference and question answering, ultimately seeking to enhance the generalizability and robustness of AI systems in real-world applications. The success of these endeavors will significantly impact the development of more human-like and adaptable AI agents.