 Dear Fellow Scholars, this is two-minute pepper sweet caro e johnaifahir. This paper was written by scientists at DeepMind and it is about teaching an AI to look at a 3D scene and decompose it into its individual elements in a meaningful manner. This is typically one of those tasks that is easy to do for humans and is immensely difficult for machines. As this decomposition thing still sounds a little nebulous, let me explain what it means. Here you see an example scene and the segmentation of this scene that the AI came up with, which shows what it thinks where the boundaries of the individual objects are. However, we are not stopping there because it is also able to rip out these objects from the scene one by one. So why is this such a big deal? Well, because of three things. One, it is a generative model, meaning that it is able to reorganize these scenes and create new content that actually makes sense. Two, it can prove that it truly has an understanding of 3D scenes by demonstrating that it can deal with occlusions. For instance, if we ask it to rip out the blue cylinder from this scene, it is able to reconstruct parts of it that weren't even visible in the original scene. Same with the blue sphere here. Amazing, isn't it? And three, this one is a bombshell, it is an unsupervised learning technique. Now, our more seasoned fellow scholars fell out of the chair hearing this, but just in case, this means that this algorithm is able to learn on its own and we have to feed it a ton of training data, but this training data is not labeled. It just looks at the videos with no additional information and from watching all this content, it finds out on its own about the concept of these individual objects. The main motivation to create such an algorithm was to have an AI look at some gameplay of the StarCraft II strategy game and be able to recognize all individual units and the background without any additional supervision. I really hope this also means that DeepMind is working on a version of their StarCraft II AI that is able to learn more similarly to how a human does, which is looking at the pixels of the game. If you look at the details, this will seem almost unfathomably difficult, but would, of course, make me unreasonably happy. What a time to be alive. If you check out the paper in the video description, you will find how all this is possible through a creative combination of an attention network and a variational autoencoder. This episode has been supported by Backblaze. Backblaze is an unlimited online backup solution for only six dollars a month and I have been using it for years to make sure my personal data, family pictures and the materials required to create this series are safe. You can try it free of charge for 15 days and if you don't like it, you can immediately cancel it without losing anything. Make sure to sign up for Backblaze today through the link in the video description and this way you not only keep your personal data safe, but you also help supporting this series. Thanks for watching and for your generous support and I'll see you next time.