From Sims to Sapiens: Crafting Reality with Code

0:00They actually wanted to build general computational agents, sort of the way general agents are supposed to be, but they didn't have the techniques to do it. They basically didn't have the large things more. And like the work that you've done is one of those 100%. There's like a spark of genius. What does it mean to accurately reflect your own behavior? But the set of companies that come out of it are always part of this enthusiast era, right? Like you couldn't have predicted Yahoo, you couldn't have predicted Amazon, like you knew something was going to happen. Can we run simulation so we can learn more about ourselves?

0:35A few weeks ago, the A16Z Infrastructure team ran an event in the San Francisco office, the topic, Generative Agents. These are autonomous characters designed to simulate human behavior, derived from a recent but game changing paper called Generative Agents, Interactive Simulacra of Human Behavior. Developers from all around the city came to hear the lead author, June Park, Speak Alongside A16Z General Partner, or Team Casado. And in this panel, they discuss how this paper and the advancements in large language models have opened a new window, expanding the dynamism of simulation, which instead of binary logic, we're using proper ballistic thinking and the ability to incorporate new information.

1:21So what does that really mean? Well, instead of your character in Sims following very specific, wrote rules. With gendered of agents, a father may go outside because he notices his son, another may take their breakfast off the stove because they notice it's burning, and another may even opt into a Valentine's Day party invite, and then elect not to show up. All very human behaviors. Now the architecture described in the paper is of course intentionally designed by June and team, and it's a combination of a seed identity for every agent and then functions that cause each one to do three discrete things, to observe, to plan, and to reflect.

2:01And these architecture decisions ultimately generate unexpectedly spirited conversations just like this. Hey Lucky, it's so great to see you. How have you been? I've been dying to hear about your space adventure. Hey Kira, I've been fantastic. My space adventure was out of this world. I can't wait to share all the details with you. Or even this. I've been trying to find my way. It's been a chaotic journey to say the least. Embrace the chaos dear Kurt. For within its turbulence lies hidden truth. Seek the depths of the unknown and unravel the mysteries that burden your soul. And here's the thing.

2:43They don't just interact with each other. Again, they wake up, they cook, some paint while others write, they hold opinions of one another, and most importantly, they remember and they have higher level reflections based on the past. Pretty amazing, but you think? So as these generative agents become a lot closer to nuanced human behavior, what can we learn about being human from these surprisingly realistic simulations? And what is the calculus of that believability? Are there real world applications on the horizon? And what is truly net new here? Listen in as we discuss all that more, including the origin of the very paper that June wrote.

3:22I hope you enjoy. As a reminder, the content here is for informational purposes only. Should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the company's discussed in this podcast. For more details including a link to our investments, please see A16Z .com slash Discoachers.

3:56So how many people in this room have actually read the Generative Agents paper that June wrote? It's a lot of people. Pretty much everyone. So, June, even though so many people have read it, why don't you just give a quick overview of what it is, but also maybe the backstory that people haven't made me heard of? So, gender of the pageants is these general computational agents that can similarly believe of a human behavior. Fundamentally leverages something like a large -linked model. Under the assumption that a link model has encoded or has seen so much about a human behavior from its training data, from the Wikipedia, social web and so forth.

4:34So if you are able to poke at the right angle, you can actually extract a lot of those human behaviors in a very context -specific manner. The opportunity here is that in the past we had to manually author a lot of these behaviors, but now we can simply generate them with a language model. So, generate the agents' leverages that create these computational systems. Ultimately, one sort of technical improvement that we're trying to make in addition to a large large model, it's basically giving it some form of memory and retrieval system. So you may have all used, obviously, Chatech, PT, and so forth.

5:07It is heavily context -limited. And even if that limitation were to go away in the future, processing a lot of really long -term context window is really inefficient and also ineffective when you're trying to prompt these models for a really narrowly defined behavioral aspects. So main philosophy here is we're going to give the long -term memory for these agents, that's external to the language model. And then we treat the contextually relevant information from the long -term memory, whether it's planning, action sequences, or reflections to create these computational agents. I philosophically, to some extent, I think this is akin to creating the operating system around the large -linked model.

5:48In the way of sort of your prompting large -linked model, to me, there's a lot like how we used to use computers back in the day when we had to wire up the back end every time we run a new program. And what has really made complex behavior with this computational tools possible was introduction of this larger architecture that surrounds the core fundamental techniques. So that's what generative agent is about. And you mentioned sort of the background why we got into all this. So I started my PhD at a sort of midway through 2020. That was just around when in chapter three was about to come out.

6:23And that year, we, a bunch of basically authors at Stanford, were working on this paper called Foundation Model, the opportunities and risks of foundation models. What we were seeing was these new form of machine learning models that seemed fundamentally different than the things that we had experienced in the past, in that we didn't have to find tune or specifically train models for a very narrow purposes, but we can train general model, almost like a stem cell in bio and leverage that to create a lot of downstream behaviors. After writing that paper, sort of my team, especially myself and my advisors, what we really want to answer is there seems to be a new opportunity, but exactly what is it?

7:05I think the early days of GPT -3, a lot of the tasks that we were doing were things like classification and generation, which was really cool to see that these models can conduct these tasks, but also something that we already knew how to do for many decades. and our gender philosophy there was be able to do something that's fundamentally different. So that's how we got into this. Our answer to that basically was, I think we might be able to create human ligations that can populate this virtual world. One thing maybe you can just elaborate, you said it's perhaps one of the most exciting times in race and history and maybe you can just speak to exactly what you mean there and how it relates to simulation and some of this new technology that we're seeing with LLMs.

7:47Sure. So first, very quick credit, or credit stume. As far as AI time clearly June is like the grandfather of AI town. And like we wouldn't be here without your work. So really appreciate you coming here. AI town itself actually came from a personal project from Yoko. A true story is it was actually a personal project. And I was like, hey, maybe more people would be interested in it. And I kind of coerced her into bringing it forward to everybody else. And so now when it actually comes to the code, the vast majority of the work on the code was actually done by Ian. It's kind of funny. You see this funny little tile set up here, and it kind of belies the fact that it's actually really hard to build a scalable, shared state distributed system that you needed to multiplayer game.

8:29It's just a hard technical problem, right? And anybody that's kind of built our systems knows that. And so it's funny because people go and they say, oh, here's this cute, see a little tile engine with these characters running around, but actually the back end is built to be something that can scale. And that requires people to focus on this and so Ian has done a tremendous job and the comics team continues to work on that. Okay, so why is this so exciting? So, okay, so because I'm old, I actually saw the advent of the web. And this feels very similar to that in the following ways, which is when you have a very destructive technology like this, whatever touches it becomes magic.

9:03I was actually having a conversation just before this. Does anybody here know it like the first video on the internet was? Yes, it was a coffee pot. I was like, this dude, I think it was in Cambridge. It was a grad student and he was like, oh, listen, I want to know when my coffee is empty. He put a camera and because it was very new, I was like, oh my god, there's a coffee pot on the internet. And so everybody wanted to look at the coffee pot, right? And do people remember the big red button? One of the first apps was this big web page which was the red button on it and you know what it did? Nothing.

9:31Like you press it and it did nothing, but people thought it was amazing because it was on the internet. And everybody would go press the button and they'd leave great comments about this button. And there's many examples of like, It was this crazy disruptive technology, and the apps seemed really stupid, and there's a bunch of enthusiasts, and you know what the enterprise thought about this? Like the actual business folks? Like I remember when Eric Schmidt fucking banned the browser. Like he was like, this is Eric Schmidt, the CTO of Sun is like, you can't have a browser because people are gonna work, right?

9:59So the same thing always happens. It's like the enthusiasts are like, this is really cool, and they use it for fringe stuff, and then like the enterprise doesn't understand it, and like Italy, they ban it, or they don't use it. but the set of companies that come out of it are always part of this enthusiast era, right? Like you couldn't have predicted Yahoo, you couldn't have predicted Amazon, like you knew something was gonna happen. And so what happens at this time is, there's a bunch of stuff that like is silly, like the coffee pot with silly, the red button with silly, but you never know like that spark of life where it's gonna come from.

10:33And it's always kinda like this non -obvious use case and it kinda seems like a 20 minute takes off, right? So you're always looking for those non -obvious use cases. And it almost never looks like the old one. Those are old enough. Do you remember like desktop is a service? I'm going to go to the cloud. I might have my Windows desktop. Like, who wants that? Nobody wants that, right? Instead of clearly we're going to rewrite the application and SAS, right? So we're in this period now where everybody's experimenting. And then I'm personally, literally from just a personal interest standpoint, but all of us are interested.

11:03Like, what are the use cases that will take advantage of this new medium that are native. And the work that you've done is one of those 100%. There's a spark of genius, which is like, when you work with these things, you know this is a new way to think about it. It's a new use case. It's can create entirely new apps. And that's what the future is built from. And so that's why I think so interesting broadly, because it's like the early internet, but very specifically in this use case, because I think the work that you've done really is a great example of something totally new. I can agree more.

11:32And I think one interesting aspect that if you explore this project, you just start to question what it means to be human. Like if we're trying to create these agents that are, quote, believable, what is believable in terms of being a human, and as part of the project, you have this coded technically, right? You made architecture decisions, you made decisions in terms of your retrieval function. Quick interruption, just to give you some color on what some of these decisions were. The retrieval function, for example, is based on scores across recency, importance, and relevance. So, for example, on a scale of 1 to 10, freshier teeth might get an important score of 1, versus a break -up might get a 10.

12:14Meanwhile, reflection is only triggered after a certain number of important events, quantified by summing the important scores until a certain threshold is met. In this case, I believe it was 150. This clever architecture results in emergent behavior. Like Agent Sharing invites with one another, or even having that information circle all the way back to the original planner. And I'm sharing these details to showcase how thoughtful you really need to be if you're designing architecture that reasonably approximates humans. Maybe you could just speak to what you've learned through those decisions technically about what it means to be a believable human.

12:50Right. So this is an interesting one. So we actually had made the general of agents and there was about a month period when we knew we had to evaluate these agents somehow and we didn't know how. And basically the cause of we stumbled upon is this idea of believability. It basically is sort of like a touring test, right? That when you look at them, do they look believable? Do they behave in ways that we can sort of see ourselves behaving? And that ended up becoming our evaluation method. It is interesting question though, in terms of like, what does it mean to be believable human? And we often look to prior literature and research to get inspiration for how to define this.

13:30And what we found was there's no prior literature on this. We used a concept believability to talk about this concept, but we were never in a position where we can meaningfully evaluate something like believability because we didn't have agents like this. So to some extent we were building up the definition ground up. And I think what came out to be the case is for us, these agents plan, react, act in a believable manner. Do they believe of reflection the way we would evaluate the current past? And I think what we've learned over the past few months, one of the more fun and interesting findings is even that, I don't think it's quite perfect definition in that a lot of sort of audience came back to us to basically say, well, one of the error cases that we've be noted, what some of these agents would go to a bar at noon or something like that.

14:18And we said that was not believable. Like, who did that? And people would come back to us and say, I did that.

14:26And if you can sort of expand from that story, I think that there's a lot of cases where even my parents like at me and go like, I cannot believe what you've done. Like, why would you do that? And vice versa. So I think even amongst the people who know each other well, having this sort of sensitive believe abilities really difficult. And I think that's sort of fundamentally under lies what it means to be human. I get not exactly predictable. And in social science we call that complexity, the human behavior sort of complex. So to some extent we can build intuition for how people might behave, but to really predict it is very difficult task.

15:04Now I do think this actually does lead to sort of future work in the space there, this idea of believability. So in this paper, we use this incomplete definition of what it means to be believable. Not perfect, but at least on that evaluation we've done well. I think if you were to build on that idea a little bit further, then you could actually start to ask, beyond believability, can you create agents that are accurately human? And I think given how difficult it was to actually evaluate what it means to be believable, I think this accuracy actually has a lot of interesting questions around it.

15:37What doesn't mean to accurately reflect your behavior, it could be that if we can match distribution of human behavior, let's say in this context they have this kind of probability of behaving this way, right? Let's say it's 10 p .m. what are the chances that I or you sleep or would be awake? What are the chances that I'd be working that I might not be working? I think ultimately getting to that degree of accuracy in this simulation might be sort of the next step to these kinds of simulation based work. If we can do that, I think the application space is that actress bit on lot will be interesting and I think it would also be different and we can go likely neon even I think there's a lot of application that we can do it right now but I think the future work that's where I think where we are headed in this direction.

16:24So I want to talk about those future applications but maybe we could just speak super quickly to in the paper you have observation, planning and reflection. And that mostly encapsulates the way that the agents are engaging with each other when they take an action they go through those three steps. I assume that wasn't your first crack at the solution that coming up with this human believable agent. And so how did you get there and to do learn anything about the importance about any of those three steps or all three of them entirely? Really the first way we actually went about doing this was simply by prompting a language model.

17:00So this line of work, a gender of the agents is actually the second in this line of work that we published. The first work in this line was called social simulator. And the idea there was to popfully a social computing system. Imagine you're a social designer, you need to know what might happen when there's tens of thousands of people in your system. Can we simulate those people in their behavior? So that project was home, so we just in the lecture, we did it simply by prompting a language model. That worked. But what we found was if we want to properly the spaces over a longer period of time, so we can do, for instance, long -distance study or gameplay that's going to last forever, then for those kind of instances, simply prompting these models wouldn't work.

17:43And this insight actually first came when we realized that we needed to have multi -agent interaction, because the agents actually would need to remember that I saw some audience here before, I should remember them, I met Martin, Stapp, Yoko, and so forth in the past few weeks or a few months, when I talked to them, I need to remember those interactions. So that's when we realized that we actually cannot simply prompt these models, but we actually need a higher -level architecture. So when we, when about doing that, I think really the main inspiration to be God actually was from prior work. So people like Alan Newell and Herbert sign language, you might recognize these names, those are sort of quote -unquote the founders of AI in the 60s and 70s.

18:25And they are the people who used to build what we call cognitive architectures. And those architectures were very reminiscent of sort of the general intelligence architecture in that it has some perception module, some action module, and there is some long -term and short -term memory. And really the goal back then was ambitious, right? They actually wanted to build general computational agents, sort of the way general agents are supposed to be, but they didn't have the techniques to do it. They basically didn't have the large -linked model. And the way we saw it was now the time to merge those two worlds, where we now have large -linked model, they can do a lot of micro -processing of this colony modules.

19:05And we can actually now bring back this macro modules, or architecture, like calling it the architecture. So we took inspiration from that particular texture hand planning in place and it had long term ensures a revering place. So we were inspired by that. One thing that I think was a little bit new though, I think is this idea of reflection that we humans, for instance, if you eat an omelette three times in a row, or if you see somebody else eat an omelette three times in a row, you likely create an opinion about the person. Maybe that person likes to eat omelette in the morning. And that's very human thing to do.

19:39And there's a good reason why we do that. We do that because it's efficient. It allows us to have higher -level inferences about the world and form opinions about those around us and about ourselves. And that's something that in the past, we couldn't really imagine formulating with a computational system, but with Laura's language model because everything is in natural language, we had that opportunity. So we added that one last component called reflection. and that's sort of how we lend it on the architecture that you see in the paper right now. Let's move on to how this can all be used and we'll get to the specific applications.

20:15But, Martin, I feel like you'll have a great answer to this. Why even do this? I feel like it's very obvious for a lot of people to understand why we would have human to human interaction. We're doing that right now. There's increasing capacity to understand human to AI or human to computer interaction, character AI is a company where there's still a lot of judgment there. I think there's even more judgment when it comes to AI to AI. Why should they use our resources to have these computers hang out and talk and burn toast and go to the bar at 2pm? So, yeah, Martin, what do you think? What's the case for us advancing in this field?

20:51No judgment for me, by the way. You can use these for whatever you want. So, I mean, I want to go back to what I said before, which is like, Any time you have a new modality, it's just not obvious what's the right way to think about it. And for me, the big aha in the last few months is just programming using models. If you've spent a long time programming, and they've been programming for 30 plus years, right? I'm never going to good programmer, but a program. And when you start programming with these models, you're like, oh, I've got an API, and I'm just going to use the API, and then I'm going to treat it like it's like the endpoint to an API.

21:22And you say some stuff, and then you get some response back. You kind of treat it like this function that you call, right? It's just like any programmer would do. But then when you're working with it more, you're like, oh, these kind of are like these life forms. And like my first ahal was because I'm shit at JavaScript. I like missed some quote somewhere. And rather than sending up the text string, I wanted to send it. I sent it some code. And instead of like, barking like you would normally have and breaking, like, you know, C++, you'd core dump or whatever, it commented on my code. I was like, oh my goodness, right?

21:57And so like all of a sudden, like, whoa, this is totally different. Like I'm not dealing with this finite state machine, formal language, think of the other end of an API. And like there's this thing and like it'll comment. And more than I program with these things, the more I'm like, what's kind of like wrapping an abacus around a supercomputer, right? It's like it's smarter than the code. It could probably write the code better than I can write anyways. like, why am I doing this weird blood -letting ritual of writing a shit JavaScript over this kind of superhuman thing? I mean, this is kind of what you end up with.

22:31And so it's very clear we're gonna interact with these things in a different way. And in fact, I was talking with that professor in Michigan recently and we're talking about this object. He's like, you know what? You know how I think about LLMs? He's like, I think about them like grad students. He's like, they speak English. They're pretty smart. I don't use a formal language. they solve like these really complex problems, et cetera. And like, having worked with a lot of grad students, having been a grad student myself, like you don't treat these things with code, right? And so the reason to do this is I actually think AI Town is kind of what this is gonna end up being.

23:07It's like you need to give them the resources that they need to be pretty autonomous and to grow, and we're gonna treat them more like peers, and they're gonna talk to each other too, and it's more like grad students. And so for me, this is just an example of, we got to change the way we think, and listen clearly, like, I'm up here and I'm telling these great stories because they're kind of funny. I don't believe this stuff in the limit, but I think there are really interesting ways to change how you think about it and all of this stuff, right? Like, I'm not trying to be categorical here. So like, there is a new way that we're going to interact with these models.

23:35It is much more natural language, they are much more powerful. And so I do think this is why we should all be doing this type of stuff. Because if you don't engage in these kind of things that look like toys, this way will pass you by. That on 100 % convinced. Totally. And as both of you have spoken to, this is fundamentally new technology. And so June, something you said to me when we first spoke is when you have fundamentally new technology, you must do something fundamentally new with it. And so maybe you can speak to that in terms of what you're seeing that can be done today, but also where you look ahead and you think, oh wow, that's a really excellent use case that we couldn't do without this new technology.