 Dear Fellow Scholars, this is two-minute papers with Dr. Karo Jona Ifehir. You will not believe the things you will see in this episode. I promise. Earlier we saw that OpenEIS GPT-3 and Codex AI techniques can be used to solve grade school level math brain teasers, then they were improved to be able to solve university level math questions. Note that this technique required additional help to do that. And a follow-up work could even take a crack at roughly a third of mathematical, Olympiad level math problems. And now let's see what the excellent scientists at deep-mind have been up to in the meantime. Check this out. This is Alpha Code. What is that? Well, in college, computer scientists learn how to program those computers. Now deep-mind decided to instead teach the computers how to program themselves. Wow, now here you see an absolute miracle in the making. Look, here is the description of the problem and here is the solution. Well, I hear you saying, Karoy, there is no solution here and you are indeed right, just give it a second. Yes, now hold on to your papers and marvel at how this AI is coding up the solution right now in front of our eyes. But that's nothing we can also ask what the neural network is looking at. Check this out, it is peeping at different important parts of the problem statement and proceeds to write the solution taking these into consideration. You see that it also looks at different parts of the previous code that it had written to make sure that the new additions are consistent with those. Wow, that is absolutely amazing. I almost feel like watching a human solve this problem. Well, it solved this problem correctly. Unbelievable. So, how good is it? Well, it can solve about 34% of the problems in this dataset. What does that mean? Is that good or bad? Now if we have been holding on to your paper so far, squeeze that paper because it means that it roughly matches the expertise level of the average human competitor. Let's stop there for a moment and think about that. An AI that understands an English description mixed in with mathematical notation and codes up the solution as well as the average human competitor at least on the tasks given in this dataset. Phew, wow. So what is this wizardry and what is the key? Let's pop the hood and have a look together. Oh yes. One of the keys here is that it generates a ton of candidate programs and is able to filter them down to just a few promising solutions. And it can do this quickly and accurately. This is huge. Why? Because this means that the AI is able to have a quick look at a computer program and tell with a pretty high accuracy whether this will solve the given task or not. It has an intuition of sorts if you will. Now interestingly it also uses 41 billion parameters. 41 billion is tiny compared to OpenEAS GPT3 which has 175 billion. This means that currently AlphaCode uses a more compact neural network and it is possible that the number of parameters can be increased here to further improve the results. If we look at DeepMind's track record improving on these ideas I have no doubt that however amazing these results seem now who have really seen nothing yet. And wait, there is more. This is where I completely lost my papers. In the case that you see here it even learned to invent algorithms. A simple one mind you, this is DFS, a search algorithm that is taught in first year undergrad computer science courses but that does not matter. What matters is that this is an AI that can finally invent new things. Wow. A Google engineer with also a world class competitive programmer was also asked to have a look at these solutions and he was quite happy with the results. He said to one of the solutions that quote, it looks like it could easily be written by a human very impressive. Now clearly it is not perfect, some criticisms were also voiced. For instance, sometimes it forgets about variables that remain unused. Even that is very human like I must say. But do not think of this paper as the end of something. No no, this is but a stepping stone towards something much greater. And I will be honest, I can't even imagine what we will have just a couple more papers down the line. What a time to be alive. So, what would you use this for? What do you think will happen a couple papers down the line? I'd love to know. Let me know in the comments below. And if you are excited to hear about potential follow up papers to this, make sure to subscribe and hit the bell icon. So definitely not want to miss it when it comes. This episode has been supported by Lambda GPU Cloud. If you are looking for inexpensive Cloud GPUs for AI, check out Lambda GPU Cloud. They 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. Because 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 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.