Zoe

BizOps & GTM @simile_ai

[Michael Bernstein @msbernst]

I describe AI behavioral simulation as a “what-if” machine. I wrote about what a What-If Machine is, and how it works, at @simile_ai:

[Simile @simile_ai]

Anyone can simulate the future. But the simulation only matters if it’s trustworthy.

At Simile, we train two types of models: simulation models and confidence models. Our first research blog post explores the origin of our proprietary confidence model, which predicts the accuracy of our population simulations and, in turn, makes them actionable.

https://t.co/qz0yPZzY55

[Simile @simile_ai]

Simile has been selected amongst this year's IA40, the top private companies shaping the future of applied AI.

Proud to be building alongside this group.

[Joon Sung Park @joon_s_pk]

Five months ago, we launched @simile_ai with the belief that simulation would become a new way for the world to make decisions.

Today, that belief feels less like a thesis and more like the beginning of a new category.

Incredibly proud of my team. We are just getting started.

[Simile @simile_ai]

Today we’re announcing our Series B.

We’ve raised $200M at a $2B valuation from Greenoaks with participation from Index Ventures, Hanabi, A*, Bain Capital Ventures, CVS Health Ventures, and Definition.

Our mission is to simulate all eight billion people on earth, accurately.

[Zoe @zoeeyang]

@simile_ai Let's push the boundaries of impossible!! 🤩

[Percy Liang @percyliang]

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:
https://t.co/eBMW2beHdT

[Mike Volpi @mavolpi]

The @simile_ai train keeps rolling. Great story on the @WSJ cc @joon_s_pk https://t.co/GUJExz6snn

[Simile @simile_ai]

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.

[Simile @simile_ai]

h1 roadmap earth, h2 roadmap mars

[Simile @simile_ai]

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! 🎈

[Joon Sung Park @joon_s_pk]

Simulate the world many times, perturb assumptions, and see what’s robust and real

[Bloomberg TV @BloombergTV]

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 https://t.co/ZInigbefyR

[Zoe @zoeeyang]

Hehe come chat with me on our website!!

https://t.co/C2hGHRypPh

[andrew pignanelli @ndrewpignanelli]

the @simile_ai site is so cute check out this easter egg on submit

[Zoe @zoeeyang]

Come help us build the future!!

[Simile @simile_ai]

At Simile, we have built the first AI simulation of society, populated by agents based on real humans.

The future is too important to be left to chance. Join us.

[Andrej Karpathy @karpathy]

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!

[Mike Volpi @mavolpi]

Looking good, @joon_s_pk !! https://t.co/If2uJAQo5B

[Michael Bernstein @msbernst]

Simile on Bloomberg:

https://t.co/sSgrlcgFoz

[Zoe @zoeeyang]

@manavr123 So excited to build future together !!

[Zoe @zoeeyang]

@mihikapoor HUGE DAY 🚀🚀🚀