For those that have trouble understanding what is being talked about here. A little background, data is often multiplied to higher dimensions in ai in order to make decisions and determine important information. This article is saying high dimensions and complicated machine learning models should only be used for tasks that converge on a specific point, such as feature selection (selecting the most relevant column or feature in a table of data). This is because as dimensions increase, even though the space increases, the distance between the data points that can be placed in that space decreases, this forces complicated ai models to converge on a specific point or opinion and overfit their answers to what they were trained to do (as opposed to thinking out of the box and coming up with new ideas). The author cautions that using very high dimensions to calculate data leads to overfitting and recommends a managed approach of using a lower dimension to train neural networks/machine learning models