The article presents a new, faster way to calculate the relationship between random variables called distance covariance and distance correlation. By using a more efficient algorithm, the researchers were able to reduce the computational complexity from O($n^2$) to O(n log n), making the calculations quicker and more practical for a wider range of applications. They also found that their new formula for estimating squared distance covariance is a U-statistic, which has nice properties for analyzing data. Overall, this work will make distance correlation more accessible and useful in various fields.