I used videos recorded from trains windows, with landscapes that moves from right to left and trained a Machine Learning (ML) algorithm with it. First, it learns how to predict the next frame of the videos, by analyzing examples. Then it produces a frame from a first picture, then another frame from the one just generated, etc. The output becomes the input of the next calculation step. So, excepting the first one that I chose, all the other frames were generated by the algorithm. The results are low resolution, blurry, and not realistic most of the time. But it resonates with the feeling I have when I travel in a train. It means that the algorithm learned the patterns needed to create this feeling. Unlike classical computer generated content, these patterns are not chosen or written by a software engineer. In this video, nobody made explicit that the foreground should move faster than the background: thanks to Machine Learning, the algorithm figured that itself. The algorithm can find patterns that a software engineer may haven’t noticed, and is able to reproduce them in a way that would be difficult or impossible to code.