The article discusses a problem in unsupervised learning called subspace recovery, where we try to find a specific subspace in a set of points. The researchers developed an algorithm that can efficiently find the subspace when it contains a certain fraction of the points. This algorithm is both easy to compute and works well even with some outliers present. They also showed that it is difficult to find the subspace when there are too many outliers, suggesting their algorithm strikes a good balance between efficiency and robustness. The problem has connections to other areas like small set expansion and matroid theory.