From neural architecture search in 2021 to Thesis

You Actually, I find so many commonalities between research and gaming. Maybe I am weird in that sense. I don't know. Because both of them have reward system in it, and both of them have objective functions in it. Like in the end of the day, you need to achieve something. And in order to do that, you need to follow certain paths. And then you need to make certain decisions. And then it's either bad or good. And then you only know the more you go. I don't know, maybe it's a very simplified way of looking at it. But then also that would bring me to the part that I admire the most for the audience. people don't know, I've been looking for Sergi and Nujy, I didn't know they existed, but I've been looking for them for a very long time because I have a genuine interest in making science and engineering problem too. And there has been so many attempts and at the moment all the big labs, especially OpenAI is increasing their resources and focus on the track, you know better than I do and like. Eric Schmidt has been talking about it for a very long time and then he's been supporting certain group of people and we have really amazing talent. Demis Hassabis, he's super super into it. Like even before I knew he existed, like what 20 plus years and this is his, as a child he wrote, wanna win Nobel Prize. And indeed what made him win the Nobel Prize was exactly that. So how about your parts? Like what motivated you among all the other problems on the planet that you picked this one. And at the same time, it feels like we're a little bit counterintuitive because it is right now all in the big institutions, like all with the big budget teams, big places, but I'm not going to steal the light from you. But yet you said, okay, we go all in to this. We bet the farm on it. And then you have an amazing stellar success too. Okay, I let you answer that. What was the real reason, motivation behind it?

Yeah, so this, I back in 2021, I started to get very interested in neural architecture search and meta learning, which are these subfields in, you know, deep learning where you essentially can try to find neural networks that solve a certain task. So, you know, a classical example of this is MNIST, like HEN. hand-digit recognition or CIFAR-10 where you're trying to identify objects. And fundamentally what I found is in my classes at Stanford, we just basically build these ⁓ neural networks handcrafted. We'd spend most of the quarter trying to devise an architecture to solve that problem. And so for the audience, going from or constructing a neural network is

Mm.

just about getting the training data in a supervised setting. It is getting a pair of data, or a pair of X and Y. So X is your input to the model and Y is the thing that you're predicting. And so for instance, if you have like a dog and a cat image, you want to predict what it actually is. It's like one or zero binary. And there are lots of sort of permutations of the types of models that you can use for that architecture. So most common, course, would be like a convolutional neural network for that, but there are so many variations. And in the literature, are, thousands of papers that come out every month ⁓ trying to advance the architectures themselves. So there was this entire field of neural architecture search that actually used a sort of proxy for the reward, namely validation loss. So what this actually is, is