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a16zInside AI Town: What AI Can Teach Us About Being Human
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CHAPTERS
- 0:00 – 3:59
Generative Agents Come to Life: Event Setup
Steph Smith introduces the a16z event on the Generative Agents paper. She explains how LLM-driven agents differ from rule-following Sims characters, and previews the observe, plan, reflect architecture.
- •The event featured lead author Joon Park and a16z's Martin Casado
- •Agents act on probabilistic reasoning and new information rather than rote rules
- •Examples include a father noticing his son and an agent skipping a party it accepted
- •Each agent has a seed identity and does three things: observe, plan, reflect
- •The framing questions are what the agents teach us about being human and what real-world uses exist
- 3:59 – 7:49
What Generative Agents Are and How the Paper Began
Joon Park gives an overview of generative agents as computational agents that use a language model plus an external memory and retrieval system. He compares this to an operating system around the LLM and recounts how the work grew out of the foundation models effort at Stanford.
- •Language models have seen so much human behavior that it can be extracted in context-specific ways
- •Behaviors that once had to be authored by hand can now be generated
- •Long-term memory is kept external to the model and relevant pieces are retrieved
- •The architecture is likened to an operating system around the LLM
- •The project grew from foundation model research, asking what fundamentally new thing these models enable
- 7:49 – 12:06
Why This Moment Resembles the Early Web
Martin Casado credits the origins of AI Town and its engineering. He then argues that this moment resembles the early internet, when silly-looking toys preceded the real native use cases.
- •Credit goes to Joon Park's work, Yoko's prototype and Ian's back end
- •The cute tile engine hides a hard scalable shared-state distributed system
- •Early web examples included the coffee pot camera and the big red button that did nothing
- •Enterprises often reject new technology while enthusiasts find the non-obvious use cases
- •Generative agents are presented as a native use case of this new medium
- 12:06 – 17:10
What Does Believability Mean for Human Agents?
Steph Smith asks what it means to be a believable human, and details the retrieval and reflection scoring decisions. Joon Park explains that believability worked like a Turing test, that it is hard to define, and that the next step may be accuracy.
- •Retrieval scores recency, importance and relevance, and reflection triggers at a summed importance threshold
- •Believability was adopted as the evaluation method after a month of not knowing how to evaluate
- •No prior literature defined believability, so the team built the definition from the ground up
- •Agents going to a bar at noon seemed unbelievable, but some audience members said they do that
- •Future work could aim for accuracy, matching distributions of human behavior
- 17:10 – 21:09
Origins of the Observe, Plan, Reflect Architecture
Joon Park explains how the architecture evolved from simply prompting a language model in the earlier Social Simulacra work. Multi-agent memory needs led to cognitive architectures inspired by Newell and Simon, with reflection as the new addition.
- •Social Simulacra populated social systems by prompting a language model
- •Longer-running simulations required agents to remember past interactions
- •Cognitive architectures from Allen Newell and Herbert Simon inspired perception, action and memory modules
- •LLMs now handle the micro-level processing inside those macro modules
- •Reflection, such as forming an opinion from seeing someone eat omelets repeatedly, was the new component
- 21:09 – 25:04
Why Build This? Treating Models Like Peers
Steph Smith asks why resources should go to agents interacting with each other. Martin Casado answers with his experience programming with models, which led him to see them as more like grad students than APIs, and urges people to engage with toy-like projects.
- •Steph Smith raises the judgment around human-AI and AI-AI interaction
- •Casado's code error got a comment back instead of a crash, showing these models differ from formal-language endpoints
- •He likens wrapping code around an LLM to wrapping an abacus around a supercomputer
- •A professor friend compared LLMs to grad students, who are treated as autonomous peers
- •Casado says those who skip things that look like toys will be passed by the wave
- 25:04 – 28:01
Simulation as a Tool for Social Science
Joon Park describes the applications he is most interested in: simulating human behavior to test policies and theories. He frames this as advancing social science, the quest to understand who we are.
- •Fundamentally new technology should enable something fundamentally new, namely simulating human behavior
- •Early technical advances often look like toys
- •Institutions like the Bank of England need to test policies before rollout
- •Social scientists could test theories through realistic simulations
- •The goal is to advance social science and empower communities and societies
- 28:01 – 30:43
Getting to Accuracy: Replication and Prediction
Steph Smith asks what it will take to move from creative uses to accuracy. Joon Park describes studies replicating social science results with LLMs and the problem of memorization, then explains how Social Simulacra recreated post-GPT-3 subreddits as a test.
- •LLMs used as study participants can replicate known results to some extent
- •It is unclear whether replication reflects behavior modeling or having read the paper, as with prospect theory
- •One fix is to test on new contexts the model hasn't seen
- •Social Simulacra recreated subreddits created after GPT-3's release
- •GPT-3 could simulate a COVID vaccination community despite not knowing about COVID
- 30:43 – 34:48
Ethics, Augmentation, and Regulation
Steph Smith asks about the ethical framework. Joon Park argues users should know they are talking to agents, that tools should augment rather than replace, and that getting it right early matters. Martin Casado gives a strongly anti-regulation view before leaving.
- •Joon Park says the project made clear that the agents are computational
- •He favors augmenting human abilities and asking whether replacement is worth its cost
- •Model-level techniques can keep behavior aligned with societal agreement
- •Park points to social media as a case where early decisions would have helped
- •Casado argues the regulatory machinery built in the social media era is hostile to AI and says to regulate the regulators
- 34:48 – 37:50
Q&A: Hard-Edge vs. Soft-Edge Agent Problems
In the audience Q&A, Joon Park answers a question on collaboration with two kinds of agent problems. Hard-edge problems have concrete right answers, while soft-edge problems are fuzzier, and he predicts early progress in the soft-edge ones.
- •Hard-edge problems have clear answers, such as classification or buying pizza
- •Soft-edge problems, like AI Town and Smallville simulations, are like drawing a portrait
- •Users tolerate imperfect simulation for fun or insight but not for errors like ordering the wrong pizza
- •Park predicts early agent progress in soft-edge spaces
- •AutoGPT and BabyAGI share a similar architectural philosophy and may need more work with users
- 37:50 – 41:16
Q&A: Context Window Size vs. Retrieval
Asked about much larger context sizes, Joon Park says that longer context is interesting but isn't the main bottleneck for agents. He bets on retrieval from external memory, since humans select only relevant information and large prompts hurt efficiency and effectiveness.
- •The largest context Park has seen in research is one million tokens
- •Humans choose only relevant information to make decisions, not their whole life experience
- •Larger contexts are expensive and can confuse models, which attend mostly to the start and end
- •Park bets on concise retrieved memories that fit into today's models
