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

Eric Dodds  00:13

Welcome back to The Data Stack. Show we are here today with Misha Laskin and 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 bass you in your co founder, working in sort of the depths of AI, doing research, building all sorts of fascinating things. You know, being, you know, part of the history is background of acquisition by Google and, you know, on the Deep Mind side, and some amazing stuff there. So I am humbled to have you on the show. Thank you so much for joining us.

Misha Laskin  01:05

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 you know, what was your, what was your high level journey? So initially, I actually did not start in AI, I started in theoretical physics. I have wanted to be a physicist since I was a kid. And the reason was, I just wanted to work on what I believe to be the most interesting and impactful scientific problems out there. And, you know, the one miscalibration that I think I made is that when I was reading back, and like all these really exciting things that happened in physics. They actually happened basically 100 years ago, and I sort of realized that I missed time. You know, you want to work on not just impactful scientific problems, but the impact of scientific problems of your time, and that’s how I made it in today. As I was working in physics, I saw the field of deep learning growing and all sorts of interesting things being invented. I actually would maybe get into AI by seeing AlphaGo 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 post doc in Berkeley, in this lab called Peter, Peter Bill’s Lab, which specializes in reinforcement learning and other areas in deep learning as well. And then I joined Deep Mind 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 kind of reinforcement learning efforts that were happening at Gemini at the time,

John Wessel  02:41

yeah, so many topics we could dive into. Amisha, so I’m gonna 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 working with AI data. What’s the topic you’re excited to dig into?

Misha Laskin  02:57

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 our data side that ensure that you know, you can predict where, where your AI’s will be successful? Because when you deploy, when you deploy it 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 being 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

Eric Dodds  03:50

Awesome. All right. Well, let’s dig in, because we have a ton to cover.