 Dear Fellow Scholars, this is two-minute papers with Dr. Karojejol Nefaher. In early 2019, a learning-based technique appeared that could perform common natural language processing operations, for instance, answering questions, completing texts, reading comprehension, summarization, and more. This method was developed by scientists at OpenAI and they called it GPT2. A follow-up paper introduced a more capable version of this technique called GPT3, and among many incredible examples, it could generate website layouts from a written description. The key idea in both cases was that we would provide it an incomplete piece of text, and it would try to finish it. However, no one said that these neural networks have to only deal with text information, and sure enough, in this work, scientists at OpenAI introduced a new version of this method that tries to complete not text, but images. The problem statement is simple. We give it an incomplete image, and we ask the AI to fill in the missing pixels. That is, of course, an immensely difficult task, because these images made the picked any part of the world around us. It would have to know a great deal about our world to be able to continue the images, so how well did it do? Let's have a look. This is undoubtedly a cat. And look, see that white part that is just starting. The interesting part has been cut out of the image. What could that be? A piece of paper, or something else? Now, let's leave the dirty work to the machine and ask it to finish it. Wow, a piece of paper indeed, according to the AI, and it even has text on it. But the text has a heading section and a paragraph below it too. Truly excellent. You know what is even more excellent? Perhaps the best part. It also added the indirect illumination on the fur of the cat, meaning that it sees that a blue room surrounds it, and therefore some amount of the color bleeds onto the fur of the cat, making it blower. I am a light transport researcher by trade, so I spend the majority of my life calculating things like this, and I have to say that this looks quite good to me. Absolutely amazing attention to detail. But it had more ideas. What's this? The face of the cat has been finished quite well in fact, but the rest, I am not so sure. If you have an idea what this is supposed to be, please let me know in the comments. And here go the rest of the results. All quite good. And the true, real image has been concealed for the algorithm. This is the reference solution. Let's see the next one. Oh my, scientists at OpenAI pulled no punches here, this is also quite nasty. How many stripes should this continue with? Zero, maybe, in any case, this solution is not unreasonable. I appreciate the fact that it continued the shadows of the humans. Next one. Yes, more stripes, great, but likely, a few too many. We are the remainder of the solutions, and the true reference image again. Let's have a look at this water droplet example too. We humans know that since we see the remnants of some ripples over there too, there must be a splash, but does the AI know? Oh yes, yes it does. Amazing. And the true image. Now, what about these little creatures? The first continuation finishes them correctly and puts them on a twig. The second one involves a stone. The third is my favorite, hold on to your papers, and look at this. They stand in the water and we can even see their mirror images. Wow. The fourth is a branch, and finally, the true reference image. This is one of its best works I have seen so far. There are some more results and note that these are not cherry-picked, or in other words, there was no selection process for the results. Nothing was discarded. This came out from the AI as you see them. There is a link to these and to the paper in the video description, so make sure to have a look and let me know in the comments if you have found something interesting. So what about the size of the neural network for this technique? Well, it contains from 1.5 to about 7 billion parameters. Let's have a look together and find out what that means. These are the results from the GPT2 paper, the previous version of the text processor, on a challenging reading comprehension test as a function of the number of parameters. As you see, around 1.5 billion parameters, which is roughly similar to GPT2, it learned a great deal, but its understanding was nowhere near the level of human comprehension. However, as they grew the network, something incredible happened. Non-trivial capabilities started to appear as we approached 100 billion parameters. Look, it nearly matched the level of humans, and all this was measured on a nasty reading comprehension test. So this image GPT has the number of parameters that is closer to GPT2 than GPT3, so we can maybe speculate that the next version could be, potentially, another explosion in capabilities. I can't wait to have a look at that. What a time to be alive! This episode has been supported by weights and biases. In this post, they show you how to use their system to visualize which part of the image your neural network looks at before it concludes that it is a cat. You can even try an example in an interactive notebook through the link in the video description. It's and biases provides tools to track your experiments in your deep learning projects. Their system is designed to save you a ton of time and money, and it is actively used in projects at prestigious labs such as OpenAI, Toyota Research, GitHub, and more. And the best part is that if you have an open source, academic, or personal project, you can use their tools for free. It really is as good as it gets. Make sure to visit them through wnbe.com slash papers, or click the link in the video description to start tracking your experiments in 5 minutes. Our thanks to weights and biases for their long-standing 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.