The article discusses a new way to teach computers to learn with only a few labeled examples, which is important for machine learning when labels are hard to get. The researchers focus on using graphs to connect unlabeled examples, assuming similar nodes have similar labels. They introduce a method to learn the graph from problem instances in a domain, leading to better performance on new instances. This approach provides strong guarantees for both online and distributional learning, and can be applied to a wide range of problems beyond just semi-supervised learning.