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I think it’s pretty clear that simulation is the next frontier for AI. The most impressive feats of AI to date are when we have a clear environment + reward, whether it be beating Le Sedol at Go, winning an IMO gold medal, or writing entire apps from scratch. In these cases, the RL algorithm can try different actions, and observe the well-defined consequences in the safety of a docker container. But what about messy real-world situations involving people? The rewards are unclear, the stakes are high, and you can’t experiment in the real world. But these situations are precisely where the next big opportunity in AI is. To crack this, we need to *simulate* society (“put society into a docker container”). Concretely, this means building a model that can predict what will happen in any given situation (real or hypothetical). If we can do this, we are only limited by our imagination: predict the future, optimize for better outcomes, answer hypothetical (“what if”) questions. Ultimately, this goes beyond making better decisions, but it’s about giving us a better understanding of ourselves and the world. Simulation is the whole enchilada. And this is exactly the research that @simile_ai is working on. Read more here:

Our work on generative agents showed that it's possible to accurately simulate human behavior by capturing rich information about real people. @joon_s_pk spoke with @Nature about how our work builds on this research, and how enterprises can now use simulations to test decisions before they hit the real world:

Exciting to see @WSJ cover what we’re building at @simile_ai. It’s great to see the technology we developed in the lab making real world impact alongside foundational institutions like CVS and Gallup. Nothing like frontier research meeting real PMF!

Simile is increasing decision-making capacity and decreasing research time. We are proud to work with partners like @CVSHealth! 🤝 Projects that took months are now taking hours, and studies are closer resembling human behavior than prior self-reported research - all with the goal of providing the best products and services for customers at companies like CVS Health, where the customer is centered in every decision.   Read more in the CVS Health whitepaper below.

Happy Valentine’s Day from Team Simile! Fun fact: In our founders’ 2023 paper, @joon_s_pk and team introduced the concept of generative agents to the world by simulating a town of 25 agents… one of who was planning a Valentine’s Day party. The agents autonomously spread invitations to the party over the next two days, made new acquaintances, and asked each other out on dates to the party. This town of agents, Smallville, along with all the flirty festivities, was instrumental in creating the field of AI-based simulation. Love is in the air! 🎈

We’re thrilled to lead @simile_ai's $100M Series A! By building digital twins of real people grounded in real interviews and real data, we believe they’re creating the foundational infrastructure for decision-making in an AI-native world.

Generative agents. Foundation models. Some of the core ideas shaping modern AI trace back to Joon Sung Park, Percy Liang, and Michael Bernstein. Now they’re building Simile - digital twins of people as enterprise infrastructure for decision-making. Proud to lead their $100M Series A alongside an exceptional group of partners & angels. Big ambition. Incredible team. Link: 

AI that predicts earnings call questions is in the works. @simile_ai CEO @joon_s_pk says his startup correctly forecast eight out of 10 analyst questions on a recent simulated call. The company has raised $100 million to scale its human-behavior prediction tools

Congrats on the launch @simile_ai ! (and I am excited to be involved as a small angel.) Simile is working on a really interesting, imo under-explored dimension of LLMs. Usually, the LLMs you talk to have a single, specific, crafted personality. But in principle, the native, primordial form of a pretrained LLM is that it is a simulation engine trained over the text of a highly diverse population of people on the internet. Why not lean into that statistical power: Why simulate one "person" when you could try to simulate a population? How do you build such a simulator? How do you manage its entropy? How faithful is it? How can it be useful? What emergent properties might arise of similes in loops? Imo these are very interesting, promising and under-explored topics and the team here is great. All the best!