The Data Stack Show - The PRQL: From Theoretical Physics to AI: Misha Laskin on AGI, Superhuman Intelligence, and Autonomous Coding

Episode Date: February 17, 2025

The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Welcome to the Data Stack Show prequel. This is a short bonus episode where we preview the upcoming show. You'll get to meet our guests and hear about the topics we're going to cover. If they're interesting to you, you can catch the full-length show when it drops on Wednesday. Welcome back to the Data Stack Show. We are here today with Misha Laskin. Misha, I don't know

if we could have had a guest who is better suited to talk about AI because you have this amazing Basu and your co-founder working in sort of the depths of AI, doing research, building all sorts of fascinating things, you know, being fascinating things, being part of the history's background of acquisition by Google, and on the DeepMind side, and some amazing stuff there. So I am humbled to have you on the show. Thank you so much for joining us. Yeah, thanks a lot, Eric.

It's great to be here. Okay. Give us just a brief background on yourself, like the quick overview. How did you get into AI? And then what was your high-level journey? Initially, I actually did not start in AI. I started in theoretical physics. I wanted to be a physicist since I was a kid. The reason was I just wanted to work on what I believed to be the most interesting and

accurate scientific problems out there. And the one miscalibration I think I made is that when I was reading back on all these really exciting things that happen in physics, they actually happened basically 100 years ago. And I sort of realized that I missed time. You want to work on not just impactful scientific problems, but the impactful scientific problems of your time. And that's how I made it into AI. As I was working in physics, I saw the field of deep learning growing and all sorts of interesting things being invented. I actually, what made me get into AI is seeing AlphaVote happen, which was this system that

was trained autonomously to beat the world champion at the game of Go. And I decided I needed to get into AI then. So after that, I ended up doing a postdoc in Berkeley in this lab called Peter Adil's lab, which specializes in reinforcement learning and other areas of deep learning as well. And then I joined DeepMind and worked there

for a couple of years where I met my co-founder as we were working on Gemini and leading a lot of the reinforcement learning efforts that were happening at Gemini at the time. Yeah, so many topics we could dive into, Misha. So I'm going to have to take the data topic. So I'm really excited to talk about how data teams look the same and how they look a little bit different when they're working with AI data. What's a topic you're excited to dig into? I think on the data side, there are many things I'm really interested in, but something I'm really interested in is how do you set up kind of evaluations on a data side that ensure that you can predict where your AIs will be successful.

Because when you deploy AIs to a customer, it's sort of, you know, you don't know exactly what the customer's talents are. And so you need to set up evals that allow you to kind of predict what's going to happen. And I think that's part of a big part of what a data team does, is setting up evaluations. And it's maybe one of the least, maybe it's one of the last things that a lot of people think about, and think about AI,

because we think about language models and reinforcement learning and so forth. But actually the first thing that any team needs to get right in any AI project is setting up clear evaluations that matter. And so on the data side, that's something I'm really interested in. Awesome. All right, well let's dig in,

because we have a ton to cover. Yeah, let's do it. All right, that's something I'm really interested in. Awesome. All right, well, let's dig in, because we have a ton to cover. Yeah, let's do it. All right, that's a wrap for the prequel. The full-length episode will drop Wednesday morning. Subscribe now so you don't miss it.