Course introduction

0:05 I hope you made it to the right class. This class is on self-improving AI agents. Anyone made it to the right class here? Yes. Okay. How many of you are here and doing Masters at Stanford? PhD? Okay. PhD and then undergrad? Okay. So Masters is the big crowd here. It's a really good balance. So we'll introduce ourselves. So I'm Ankushya. I have been working in large language models for a while and I'm an Adjunct Professor at Stanford and I also in research at a startup called Reflection AI.

0:48 Hi, I'm Azalia Mehusaini. I'm a I'm an Assistant Professor in the CS department and, uh, Ankushya and I met each other when we were at Google Google Brain. Google Brain? Yeah, back in the day and then Google DeepMind. And I also worked on Cod, Adantranphic and Gemini at Google DeepMind. And we are so excited to teach this course again. This is the second time we're teaching. Of course, we have made some updates, both to the lectures and also the way we are, uh, kind of conducting everything during the course.

0:48 Uh, and we were just so amazed by the amount of interest, uh, for people to join this course. So we are excited to have you here. Uh, the course website is at cs329a.stanford.edu. We have updated the website in terms of all the lecture materials and and the papers that you'll be reading as well as, what's the homework schedule to expect, the project proposal and project structure as well. So in today's lecture, we'll start with an overview of large language models.

1:36 Uh, just, uh, how the scaling trends have evolved over the last, uh, five years. And then we'll focus more on what we will cover in this class. And then from there, uh, in the second half of this, uh, this lecture, we'll cover the course logistics and that part, you should pay a lot of attention to, because that influences your grades. So let's take a look at an overview of the trends for large language models.