The bot Cicero can collaborate, scheme and build trust with humans. What does this mean for the next frontier of AI? With Noam Brown, Research Scientist at Meta

**Noam Brown** (0:07)
We were trying to think of what would be the hardest game to make an AI for. We landed on diplomacy.
The idea that you could have an AI that negotiates in natural language with humans and strategizes with them, it really just felt like science fiction. I'm really glad that we aimed high at that point. I was a little afraid to do that, to be honest. It's a high-risk thing to aim for, but all research is high-risk, high-reward, or at least it should be.
We maybe get two orders of magnitude more scaling, and then we have a big problem. Do you train a $100 billion model? This is why I'm interested in the reasoning direction.

**Sarah Guo** (0:41)
This is the No Priors Podcast. I'm Sarah Guo.
We invest in, advise, and help start technology companies.

**Elad Gil** (0:48)
In this podcast, we're talking with the leading founders and researchers in AI about the biggest questions.

**Sarah Guo** (1:01)
This week on the podcast, we're welcoming Noam Brown. Noam's a research scientist on the Meta Fundamental AI Research Team. Noam co-created the first AIs to defeat top humans in two different types of poker. He also recently did an important project called Cicero. In this podcast, we'll dig into how this AI works, what makes for great AI research and engineering, and how AI games tie into AGI.
Noam is considered one of the smartest engineers and researchers in AI. His work has deep implications for how humanity and AI co-evolve.
The new bot Cicero can lie, can scheme, it can read a human's intentions and build trust. Cicero demonstrates these skills by performing better than the average human at a classic game called diplomacy. Noam, welcome to No Priors.

**Noam Brown** (1:41)
Noam, thank you for having me.

**Elad Gil** (1:42)
Yeah, thanks a lot for joining. So, you know, I think in the world today, when a lot of people think about AI, they think about it as basically you put a couple of words into a prompt and then you get out an image, or you have ChatGPT summarize James Burnham's professional managerial class for you in a rhyming essay in the voice of a cat or something. And I think you've pushed in really interesting directions that are very different in some ways from what a lot of people have focused on. And you've been more focused on game theoretic actors interacting with humans and with each other.
And in parallel, you're kind of known as, as Sarah mentioned, as sort of one of these true 10x engineers and researchers pushing the boundaries on the AI. And so I'm sort of curious, like what first sparked your interest in games and researching AI to defeat games like poker and diplomacy?

**Noam Brown** (2:22)
Well, I think, you know, my journey is a bit non-traditional. I mean, I started out in finance actually. So towards the end of my undergrad career and also right after undergrad, I worked in algorithmic trading for a couple of years. And I kind of realized that while it's fun and it's exciting, it's kind of like a game. You know, you got a score at the end of the day, which is how much money you made or lost. It's not really the most fulfilling thing that I want to do with my life. And so I decided that I wanted to do research and it wasn't really clear to me in what area. I was originally planning to do economics actually. And so I went to the Federal Reserve. I worked there for two years. Honestly, I wanted to figure out how to structure financial markets better to encourage more pro-social behavior.
And so in the process, I became interested in game theory and I thought I wanted to pursue a PhD in economics, focused on game theory.
And two things happened. So first of all, I became a bit jaded with the pace of progress in economics because if you come up with an idea, you have to get it passed through legislation and it's a very long process. And computer science is much more exciting in that way because you can just build something. You don't really need permission to do it. And then the other thing I figured out was that a lot of the most exciting work in game theory was actually happening in computer science. It wasn't happening in economics.
And so I applied for grad schools with the intention of studying algorithmic game theory in a computer science department.
And when I got to grad school, there was conveniently a professor that was looking for somebody to do research on AI for poker.
And I thought this was like the perfect intersection of everything that I wanted to do. I was interested in game theory. I was interested in making something, interested in AI. I had played poker when I was in high school and college and never for high stakes, but always just kind of interested in the strategy of the game. I actually tried to make a poker bot when I was an undergrad and it did terribly, but it was a lot of fun. And so to be able to do that for research in grad school, I thought this was like the perfect thing for me to work on. And also I felt like there was an opportunity here because it felt doable. And I kind of recognized that if you succeed in making an AI that can play poker, you're going to learn really valuable things along the way. And that could have major implications for the future.