 Dear Fellow Scholars, this is two minute papers with Dr. Karajone Fahir. Today, we are going to play with a cellular automaton and fuse it together with the neural network. It will be quite an experience. But of course, first, what are these things anyway? You can imagine a cellular automaton as a small game where we have a bunch of cells and a set of simple rules that describe when a cell should be full and when it should be empty. What you see here is a popular example called Game of Life, which simulates a tiny world where each cell represents a live form. So, why is this so interesting? Well, this cellular automaton shows us that even a small set of simple rules can give rise to remarkably complex live forms such as gliders, spaceships, and even John von Neumann's Universal Constructor or in other words, self-replicating machines. Now, this gets more interesting, a later paper fused the cellular automaton with a neural network. It was tasked to grow and even better maintain a prescribed shape. Remember these two words, grow and maintain shape. And the question was if it can recover from undesirable states, can it perhaps regenerate when damaged? Well, here you will see all kinds of damage and then this happens. Nice! The best part is that this thing wasn't even trained to be able to perform this kind of regeneration. The objective for training was that it should be able to perform its task of growing and maintaining shape and it turns out some sort of regeneration is included in that. This sounds very promising and I wonder if we can apply this concept to something where healing is instrumental. Are there such applications in computer science? If so, what could those be? Oh yes, yes there are. For instance, think about texture synthesis. This is a topic that is subject to a great deal of research in computer graphics and those folks have this down to a science. So, what are we doing here? Texture synthesis typically means that we need lots of concrete or gravel road, skin, marble, create unique stripes for zebras, for instance for a computer game, or the post-production of a movie and we really don't want to draw miles and miles of these textures by hand. Instead, we give it to an algorithm to continue this small sample where the output should be a bigger version of this pattern with the same characteristics. So, how do we know if we have a good technique at hand? Well, first it must not be repetitive, checkmark, and it has to be free of seams. This part means that we should not be able to see any lines or artifacts that would quickly give the trick away. Now, get this, this new paper attempts to do the same with neural cellular automaton. What an insane idea! We like those around here, so let's give it a try. How? Well, first by trying to expand this simple checkerboard pattern. The algorithm is starting out from random noise and as it evolves, well, this is a disaster. We are looking for squares, but we have quadrilaterals. They are also misaligned, and they are also inconsistent. But luckily, we are not done yet, and now hold on to your papers and observe how the grid cells communicate with each other to improve the result. First, the misalignment is taken care of, then the quadrilaterals become squares, and then the consistency of displacement is improved. And the end result is, look. In this other example, we can not only see these beautiful bubbles grow out of nowhere, but the density of the bubbles remains roughly the same over the process. Look, as two of them get too close to each other, they coalesce or pop. Damage bubbles can also regrow. Very cool. Okay, it can do proper texture synthesis, but so can a ton of other handcrafted computer graphics algorithms. So, why is this interesting? Why bother with this? Well, first, you may think that the result of this technique is the same as other techniques, but it isn't. The output is not necessarily an image, but can be an animation too. Excellent. Here, it was also able to animate the checkerboard pattern, and even better, it can not only reproduce the weave pattern, but the animation part extends to this too. And now comes the even more interesting part. Let's ask why does this output an animation and not an image? The answer lies within these weaving patterns. We just need to carefully observe them. Let's see. Yes, again, we start out from the noise, where some woven patterns emerge, but then it almost looks like a person who started weaving them until it resembles the initial sample. Yes, that is the key. The neural network learned to create not an image, not an animation, but no less than a computer program to accomplish this kind of texture synthesis. How cool is that? So, aren't with all that knowledge, do you remember the regenerating iguana project? Let's try to destroy these textures too, and see if it can use these computer programs to recover and get us a seamless texture. First, we delete parts of the texture, then it fills in the gap with noise, and now let's run that program. Wow! Resilient, self-healing texture synthesis. How cool is that? And in every case, it starts out from a solution that is completely wrong, improves it to be just kind of wrong, and after further improvement, there you go. Fantastic! What a time to be alive! And note that this is a paper in the wonderful distale journal, which not only means that it is excellent, but also interactive. So, you can run many of these experiments yourself right in your web browser. The link is available in the video description. This episode has been supported by weights and biases. In this post, they show you how to debug and compare models by tracking predictions, hyperparameters, GPU usage, and more. If you work with learning algorithms on a regular basis, make sure to check out weights and biases. Their system is designed to help you organize your experiments, and it is so good it could shave off weeks or even months of work from your projects, and is completely free for all individuals, academics, and open source projects. This really is as good as it gets, and it is hardly a surprise that they are now used by over 200 companies and research institutions. Make sure to visit them through wnb.com slash papers, or just click the link in the video description, and you can get a free demo today. Our thanks to weights and biases for their longstanding support, and for helping us make better videos for you. Thanks for watching and for your generous support, and I'll see you next time.