The Best AI Companies Have Unique Data Acquisition Strategies | Simile Co-founder & CEO

Jun Park: So, this was 2023. we had this idea that large language models are often used for simpler tasks like classification, simple generation but we thought that these models actually had a lot more potential. One of the early observations that we made was that these models are trained on so much of human behavioral data, sentiment data that were expressed on the web. So if you poke at them sort of at the right angle, you could actually extract a lot of realistic human behaviors out of them. I thought that was really interesting and it was also particularly interesting in that it was domain agnostic. So if you look at the literature in computer science for many decades we've always had the vision of creating agents that are meant to be generalizable that are meant to really be able to act like human in any environment. And my mind went to well maybe we have that opportunity here. So what we ended up doing was well if we were to fast forward many years into doing this what would be the most ambitious vision that we might have and that was creating entire lived experience of a town. So the idea here was we would make a game town and we would populate it with 25 NPCs. So non-playable characters except these characters would actually wake up in the morning, do their routines, go to work, have relationships and do all that. They would actually remember their interactions. They would actually plan their days. And some of the surprising things you end up seeing was the simulation itself was set the day before Valentine's Day and you actually see these agents come together, have parties like self-organized. So they would actually plan parties, they would decorate the cafe and so forth.

Jun Park: My fundamental thesis here is for AI companies of this generation you need to have an interesting data strategy that's going to be defensible and for us really the data collection challenge comes from two angles one is actually sourcing people sourcing people here is a little bit different than what other language model companies might consider to be their people or their population. We don't go after these expert programmers or expert scientists. We go after people like us like everyday people living their everyday life. That's what we care about is are they representative? Do we actually have the same representation of people as we do in the world that we live in? And then actually asking the right questions to these people. What are the experiments? What are the questions that actually get at the fundamental core nature of who they are? Some of the questions we actually ask at the start of our data collection at times is actually saying something like tell us the story of your life. Where did you grow up? What did you experience? What were some of the hardest problems that you had to tackle or decisions you had to make? Tell us a lot about these people.

Jun Park: It might be easy to look at simulation as a field and say well where are you going to get the reward? Because fundamentally all the things that you're trying to predict is happening in the future. It's going to be hard to validate. It is true. At the same time I actually think simulation has even better mechanism which is the world is our ground truth. We live in the ground truth world. So what we can do is every single day we can be generating tens of thousands of hypothesis. Each hypothesis is mapped onto an end statement. If this happens, we know whether we can validate the simulation to be right or wrong. And we're basically watching the world every day seeing which of those hypotheses are answerable at what time. And we can basically say a month goes by, we generated a million hypothesis, x percentage of them came true. This is the best way to learn about the world.

Jun Park: What I see today that's prominent in AI space is what I consider to be the CPU of intelligence unit. You have this one language model that's really large that's very smart that can do very complex reasoning tasks that's like CPU. What I see coming and what I think simulation as a field can offer is the GPU of intelligence unit. As I mentioned before, Simile does not care about creating really smart super intelligent machines. What we care about is creating models that are as smart as we — I fail at a lot of things. I want to make sure that the model that represents me fails the same way. But the beautiful part about people is individually we have so much diversity, so much different takes in our world that makes individuals so interesting. But also when they come together as a large collective, the emerging phenomena that we're able to draw out is some of the most wonderful thing that we can see in our world. Creating a society, creating an amazing process that actually allows us to make all these achievement — can we actually replicate that in simulation?