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A recent experiment conducted by Stanford and Google researchers aimed to apply the latest advances in machine learning models to produce “generative agents” that can respond realistically to the circumstances presented to them. The goal was to create agents that could produce “believable simulacra of human behavior” in a virtual town filled with artificial intelligence. The researchers were curious about what would happen if they set these agents loose in a virtual environment.
Although the paper describing the experiment has not been peer-reviewed or accepted for publication, the researchers were able to produce some interesting results.
The agents in the virtual town were observed to brush their teeth and be very friendly to one another, which was unexciting but still a success for the researchers.
This experiment has potential implications for the development of artificial intelligence, particularly in creating more realistic and believable simulations of human behavior.
The use of generative agents could lead to the creation of more sophisticated AI models that can be used for a variety of applications, from gaming to training simulations.
Moreover, the ability to create believable simulacra of human behavior has significant implications for the development of chatbots and other conversational AI.
By training generative agents to respond realistically to human inputs, it is possible to create chatbots that are more convincing and capable of carrying on a more natural conversation with users.
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The researchers at Stanford and Google conducted an experiment to test the latest advances in machine learning models. Their paper, which is not yet peer-reviewed or published, describes the use of “generative agents” that take in their circumstances and respond with a realistic action.
They wanted to create “believable simulacra of human behavior,” and the experiment was conducted by filling a virtual town with AI agents and observing their interactions.
While the experiment produced some cute imagery and descriptions of reflection, conversation, and interaction, it is important to note that the agents are not as advanced as they might appear to be.
The virtual town is more like an improv troupe role-playing on a MUD than any kind of Skynet-like scenario. The graphics are simply a visual representation of what is essentially a bunch of conversations between multiple instances of ChatGPT.
The agents don’t actually walk or move around; all the interactions occur through a complex and hidden text layer that synthesizes and organizes the information related to each agent.
The experiment involved 25 agents, each prompted with similarly formatted information that caused it to play the role of a person in the virtual town. For instance, one agent was set up to act as John Lin.
The experiment yielded interesting results and demonstrated the potential of machine learning models to create believable simulations of human behavior. However, it is important to remember that this is just one small step in a larger effort to develop more advanced AI technology.
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John Lin is a pharmacy shopkeeper at the Willow Market and Pharmacy who loves to help people. He is always looking for ways to make the process of getting medication easier for his customers; John Lin is living with his wife, Mei Lin, who is a college professor, and son, Eddy Lin, who is a student studying music theory; John Lin loves his family very much; John Lin has known the old couple next-door, Sam Moore and Jennifer Moore, for a few years; John Lin thinks Sam Moore is a kind and nice man…
The experiment described in the article involves creating “generative agents” using machine learning models that can respond to their environment with a realistic action.
The experiment involves 25 instances of ChatGPT, where each instance plays the role of a person in a fictional town. The objective is to produce believable simulacra of human behavior.
The agents are given information about their circumstances, such as the time of day, and are prompted to come up with their next actions accordingly.
For example, if John agent is told that it’s 8 AM and he just woke up, he will brush his teeth, kiss his wife, get dressed, and then go to the kitchen. Similarly, his son Eddy, who is represented by a different ChatGPT instance, will also wake up and follow a similar routine.
The agents are not in the same virtual space or interact with each other directly. Instead, they receive prompts from the experimental framework that informs them about their environment and what other agents are doing.
When John decides to move to the kitchen, the framework informs him that his son Eddy is already there, as Eddy has also moved to the kitchen based on an estimate of how long various actions take.
The agents’ interactions and movements are not predetermined, but rather a result of the machine learning models’ responses to the information they receive.
The experiment aims to produce a realistic simulation of human behavior by allowing the agents to interact with their environment and each other in a believable way.
John: Good morning Eddy. Did you sleep well?
Eddy: Good morning dad. Yeah, I slept great.
John: That’s good. What are you working on today?
Eddy: I’m working on a new music composition for my class. It’s due this week, so I’m trying to get it finished. But I’m having so much fun with it!
John: That sounds great!
The article describes an interesting experiment that involves the interaction between multiple instances of ChatGPT.
The experiment creates a fictional town and family, and then prompts separate instances of ChatGPT with similar formatted information to play the role of people in that town.
Each instance of ChatGPT receives information about the current time and the circumstances of the person they represent. They are then asked to come up with their next actions based on this information.
For example, one instance of ChatGPT is prompted with information that it is 8 AM and John has just woken up.
It then asks itself what John would do next, given the time and circumstances. ChatGPT generates an answer based on what it thinks a real human would do in that situation, for instance, brushing his teeth and kissing his wife before getting dressed and heading to the kitchen.
Meanwhile, another instance of ChatGPT, representing John’s son, Eddy, is also prompted with its own information and generates its own actions based on this.
The experiment creates an overarching structure that allows the actions of the separate instances of ChatGPT to interact with each other. When John moves to the kitchen, for example, and Eddy has also decided to move to the kitchen at an overlapping time in the experiment-level “day,” the experimental framework informs John that Eddy is there.
The framework also provides information such as the table no one is sitting at, and the stove being on. This information allows the instances of ChatGPT to interact with each other in a simulated environment.
The article points out that the experiment is more like an improv troupe role-playing on a MUD than any kind of proto-Skynet. This is because the graphics are just a visual representation of what is essentially a bunch of conversations between multiple instances of ChatGPT.
All the interactions happen through a complex and hidden text layer that synthesizes and organizes the information pertaining to each agent.
The experiment continues throughout the day, and ChatGPT generates the characters’ actions minute by minute, such as buying groceries, walking in the park, and going to work.
The users can also write in events and circumstances, like a dripping faucet or a desire to plan a party, and the agents respond appropriately, since any text, for them, is reality.
All of this is performed by laboriously prompting all these instances of ChatGPT with all the minutiae of the agent’s immediate circumstances. Here’s a prompt for John when he runs into Eddy later:
It is February 13, 2023, 4:56 pm.
John Lin’s status: John is back home early from work.
Observation: John saw Eddy taking a short walk around his workplace.
Summary of relevant context from John’s memory:
Eddy Lin is John’s Lin’s son. Eddy Lin has been working on a music composition for his class. Eddy Lin likes to walk around the garden when he is thinking about or listening to music.
John is asking Eddy about his music composition project. What would he say to Eddy?
[Answer:] Hey Eddy, how’s the music composition project for your class coming along?
The article discusses an experimental framework that simulates a fictional town where characters interact with each other in a virtual environment. The simulation is accomplished by asking separate chatbots what they would do if they were in a particular situation.
The chatbots simulate characters’ daily routines, such as waking up, brushing their teeth, going to the kitchen, and buying groceries, with the framework reminding them of important details or synthesizing them into more portable pieces.
The framework also maintains a long-term memory for each character to store essential information, and the rest can be forgotten.
One potential issue with this approach is that the chatbots could quickly forget important details, so the framework sits on top of the simulation and reminds them of important things.
For example, the framework might extract the “reflection” that “Eddie and Fran are friends because I saw them together at the park” from a situation in the park where Eddie and Fran were having a conversation while sitting on a bench.
This reflection is then stored in the agent’s long-term memory, which is a collection of information stored outside the ChatGPT conversation.
The article notes that this approach is an early attempt to create generative agents, although it falls short of true generative agents.
Dwarf Fortress, another simulation game, takes a similar approach but requires every possibility to be hand-coded. This method is not scalable, making the experimental framework a more compelling alternative.
Although ChatGPT was not designed to imitate fictional characters or speculate on mundane details, with proper handling, it can simulate characters and maintain continuity in a virtual diorama without breaking.
This has potentially huge implications for simulations of human interactions, wherever those may be relevant — of course in games and virtual environments they’re important, but this approach is still monstrously impractical for that.
What matters though is not that it is something everyone can use or play with (though it will be soon, I have no doubt), but that the system works at all. We have seen that in AI: If it can do something poorly, the fact that it can do it at all generally means it’s only a matter of time before it does it well.
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