 Dear Fellow Scholars, this is Two Minute Papers with Dr. Karo Zonai-Fehir. Today we are going to explore what happens if we unleash an AI to read the internet and then ask it some silly questions and we'll get some really amazing answers. But how? Well, in the last few years, open AI set out to train an AI named GPT-3 that could finish your sentences. Then they made image GPT and they could even finish your images. Yes, not kidding. It could identify that the cat here likely holds a piece of paper and finish the picture accordingly and even understood that if we have a droplet here and we see just a portion of the ripels then this means a splash must be filled in. So, in summary GPT-3 is trained to finish your sentences or even your images. However, scientists at OpenAI identified that it really isn't great at following instructions. Here is an excellent example. Look, we ask it to explain the moon landing to a six-year-old and it gets very confused. It seems to be in text completion mode instead of trying to follow our instructions. Meanwhile, this is instruct GPT near new method which can not only finish our sentences, but also follow our instructions. And would you look at that? It does that successfully. Good job, a little AI. But this was a really simple example, but of course we are experienced fellow scholars here, so let's try to find out what this AI is really capable of in three really cool examples. Remember in the Hitchhiker's Guide to Galaxy where people could ask an all-knowing machine the most important questions of things that trouble humanity. Yes, we are going to do exactly that. Round one, so they are all-knowing AI. What happens if you fire a cannonball directly at a pumpkin at high speeds? Well, what do you see that? According to GPT-3, pumpkins are strong magnets. I don't know which internet forum told the AI that? Not good. And now hold on to your papers and let's see the new techniques answer together. Wow! This is so much more informative. Let's break it down together. It starts out by hedging, noting that it is hard to say because there are too many unpredictable factors involved. And knowing as it might seem, it is correct to say all this. Good start. Then, at least some of the factors that might decide the fate of that pumpkin like the size of the cannonball, distance and velocity. Yes, we are getting there, but please give me something concrete. Yes, there we go. It says that, quote, some of the more likely possible outcomes include breaking or knocking the pumpkin to the ground, cracking the pumpkin, or completely obliterating it. Excellent. A little smarty pants AI at our disposal. Amazing. I love it. Round 2, Code Samarization. What deep mines Alpha Code is capable of reading a competition level programming problem and coding up a correct solution right in front of our eyes. That is all well and good, but if we give GPT-3 a piece of code and ask it what it does, well the answer is not only not very informative, but it's also incorrect. No instruct GPT gives a much more insightful answer, which shows a bit of understanding of what this code does. That is amazing. Note that it is not completely right, but it is directionally correct. Partial credit for the AI. Round 3, write me a poem. About what? Well, about a wise frog. With GPT-3 we get the usual confusion. By round 3 we really see that it was really not meant to do this. And with instruct GPT, let's see... Hmm. An all-knowing frog who is the master of this guys, a great teacher, and quite possibly the bringer of peace and tranquility to humanity. All written by the AI. This is fantastic. I love it. What a time to be alive. Now we only looked at three examples here, but what about the rest? Where you're not for a second, open AI scientists ran a detailed user study and found out that people preferred instruct GPT solutions way more often than the previous techniques on a larger set of questions. That is a huge difference. Absolutely amazing. Once again, incredible progress. Just one more paper down the line. So, is it perfect? No. Of course not. Let's highlight one of its limitations. If the question contains false premises, it accepts the premise as being real and goes with it. This leads to making things up. Yes, really. Check this out. Why aren't birds real? GPT3 says something. I am not sure what this one is about. This almost sounds like gibberish. While instruct GPT accepts the fact that birds aren't real and even helps us craft an argument for that. This is a limitation and I must say a quite remarkable one. An AI that makes things up. Food for thought. And remember at the start of this episode we looked at this moon landing example. Did you notice the issue there? Please let me know in the comments below. So, what is the issue here? Well, beyond not being all that informative, it was asked to describe the moon landing in a few sentences. This is not a few sentences. This is one sentence. If we give it constraints like that, it tries to adhere to them, but is often not too great at that. And of course, both of these shortcomings show us the way for an even better follow-up paper, which based on previous progress in AI research could appear maybe not even in years, but much quicker than that. If you are interested in such a follow-up work, make sure to subscribe and hit the bell icon to not miss it when it appears on two-minute papers. So, what would you use this for? What do you expect to happen? A couple more papers down the line? Please let me know in the comments below. I'd love to hear your thoughts. This episode has been supported by Lambda GPU Cloud. If you're looking for inexpensive Cloud GPUs for AI, check out Lambda GPU Cloud. They've recently launched Quadro RTX 6000, RTX 8000, and V100 instances, and hold onto your papers because Lambda GPU Cloud can cost less than half of AWS and Azure. Plus, they are the only Cloud service with 48GB RTX 8000. Join researchers at organizations like Apple, MIT, and Caltech in using Lambda Cloud instances, workstations or servers. Make sure to go to lambdaleps.com, slash papers to sign up for one of their amazing GPU instances today. Our thanks to Lambda 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.