Symbol of change for AI development

IMAGE: A novel algorithm translates symbolic knowledge into vector spaces to combine deductive reasoning with machine learning. A mathematical framework that bridges the gap between high-level human-readable knowledge and statistical data has been developed by a KAUST team and is expected to improve machine learning. Humans rely on patterns, labels and order to make sense of the world. We categorize, classify and make links between related things and ideas, creating symbols that we can use to share information. Artificial intelligence, on the other hand, is trained most effectively using raw numerical data.