 Dear Fellow Scholars, this is two-minute papers with Kato Ejolna Efehir. Have a look and marvel at this learning-based assembler robot that is able to put together simple contraptions. Since this is a neural network-based learning method, it needs to be trained to be able to do this. So, how is it trained? Normally, to train such an algorithm, we would have to show it a lot of pairs of the same contraption and tell it that this is what it looks like when it's disassembled and what you see here is the same thing assembled. If we did this, this method would be called supervised learning. This would be very time-consuming and potentially expensive as it would require the presence of a human as well. A more convenient way would be to go for unsupervised learning where we just chuck a lot of things on the table and say, well, robot, you figure it out. However, this would be very inefficient if at all possible because we would have to provide it many, many contraptions that wouldn't fit on the table. But this paper went for none of these solutions as they opted for a really smart, self-supervised technique. So what does that mean? Well, first, we give the robot an assembled contraption and ask it to disassemble it. And therein lies the really cool idea because disassembling it is easier and by rewinding the process, it also gets to know how to assemble it later. And the training process takes place by assembling, disassembling, and doing it over and over again several hundred times per object. Isn't this amazing? Love it. However, what is the point of all this? Instead, we could just add explicit instructions to a non-learning based robot to assemble the objects. Why not just do that? And the answer lies in one of the most important aspects within machine learning generalization. If we program a robot to be able to assemble one thing, it will be able to do exactly that, assemble one thing. And whenever we have a new contraption on our hands, we need to reprogram it. However, with this technique, after the learning process took place, we will be able to give it a new previously unseen object and it will have a chance to assemble it. This requires intelligence to perform. So how good is it at generalization? Well, get this, the paper reports that when showing it new objects, it was able to successfully assemble new previously unseen contraptions 86% of the time. Incredible. So, what about the limitations? This technique works on a 2D planar surface, for instance, this table, and while it is able to insert most of these parts vertically, it does not deal well with more complex assemblies that require inserting screws and pegs in a 45 degree angle. As we always say, two more papers down the line and this will likely be improved significantly. If you have ever bought a new bed or a cupboard and said, well, it just looks like a block. How hard can it be to assemble? Wait, does this thing have more than 100 screws and pegs? I wonder why. And then, 4.5 hours later, you'll find out yourself. I hope, techniques like these will help us save time by enabling us to buy many of these contraptions preassembled and it can be used for much, much more. What a time to be alive. This episode has been supported by Lambda. If you're a researcher or a startup looking for cheap GPU compute to run these algorithms, check out Lambda GPU Cloud. I've talked about Lambda's GPU workstations in other videos and I'm happy to tell you that they're offering GPU cloud services as well. The Lambda GPU cloud can train ImageNet to 93% accuracy for less than $19. Lambda's web-based IDE lets you easily access your instance right in your browser. And finally, hold onto your papers because the Lambda GPU cloud costs less than half of AWS and Azure. Make sure to go to LambdaLabs.com slash papers and sign up for one of their amazing GPU instances today. Thanks to Lambda for helping us make better videos for you. Thanks for watching and for your generous support and I'll see you next time.