No Priors Ep. 70 | With Cartesia Co-Founders Karan Goel & Albert Gu

Welcome back to No Priors. We're excited to talk to Karan Goel and Albert Gu, the co-founders of Kartesia, and authors behind such revolutionary models as S4 and Mamba. They're leading a rebellion against the dominant architecture of Transformers, so we're excited to talk to them about that and their company today. Welcome, Karan, Albert.

Thank you.

Nice to be here.

And can you tell us a little bit more about Kartesia, the product, what people can do with it today, some of the use cases?

Yeah, definitely. We launched Sonic. Sonic is a, uh, really fast text-to-speech engine, so, um, some of the places I think that we've, we've seen people be really excited about, uh, you know, using Sonic is where, like, they wanna do, uh, interactive low-latency, uh, voice generation. So I think the two places we've really kind of, um, had a lot of excitement is, one, in gaming, where, you know, folks are really just, um, interested in, uh, powering, um, you know, characters and roles and NPCs. The dream is to have a game where you have millions of players and they're, like, able to just interact, uh, with these, uh, with these models and- and- and get back responses on the fly, and I think that's sort of, uh, where we've seen a lot of excitement and- and uptake. And then the other end is voice agents, um, and- and being able to power them, and again, low latency there matters. Um, and you know, even with, uh, what we've done with Sonic, we're already kind of shaving off, like, 150 milliseconds off of, uh, you know, what they typically use. And so, you know, the roadmap is let's- let's, uh, get to the next 600 milliseconds and- and try to shave those off in the- over the course of the year. That's been the place where it's been pretty exciting.

Love to talk a little bit just about backgrounds and how you ended up starting Kartesia, and maybe you can start with the research journey, and like what kind of problems you were both working on.

Karan and I both came from the same PhD group, uh, at Stanford. I did a pretty long PhD and I worked on a bunch of problems, uh, but I ended up sort of working on a bunch of problems around, uh, sequence modeling. They came out of kind of these, uh, problems that I started working on actually at DeepMind during an internship, and then I started working on sequence modeling, uh, around the same time, actually, that Transformers got popular. Um, I actually, instead of working on them, I got really interested in these alternate kind of recurrent models, which I thought were really elegant for other reasons, and it kind of felt like fundamental in a sense. And so I- I was just really interested in them and I worked on them for a few years. A couple years ago, me and Karan worked together on this model called S4, which, uh, kind of got popular for showing that some form of recurrent model, called a state-space model, um, was really effective in some applications. And I've continuing to be pushing on that direction. Recently, uh, pr- proposed a model called Mamba, um, which was, uh, kind of brought these to language modeling, um, and showed really good results there, and so people have been really interested. Um, we've been using them for, um, applications and, uh, other sorts of domains and so on. So yeah, it's really exciting. Um, personally, I also, I just started as a professor at CMU this year. My research lab there is kind of working on the academic side of these questions, while, uh, at Kartesia we're kind of putting them into production.