Michael Bernstein retweetledi
GitHub: github.com/GeneralUserMod…
For example: here’s @gandhikanishk following the Australia v. Zimbabwe match while monitoring a training run. All annotated with NAPsack!
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Michael Bernstein
754 posts
Michael Bernstein
@msbernst
@stanford Professor of Computer Science, @simile_ai co-founder, nationally bestselling author. I build interactive, social, and societal tech.
Stanford, CA Katılım Kasım 2007
1.9K Takip Edilen18.7K Takipçiler
Michael Bernstein retweetledi
Passive interaction data is super underrated.
Recruit users, observe what they're already doing (and willing to share!), and label those trajectories with a VLM! It's free lunch!!!!
We're releasing an open-source package (NAPsack) to do this, tested on 1.9M screenshots.
🧵
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Michael Bernstein retweetledi
We trained our models to solve problems with objective answers. But can we build models that solve problems where success is subjective, messy, and human? The latter is even more impactful imo.
Agree with Percy: simulation is the next frontier for AI.
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: simile.ai/blog/simulatio…
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Michael Bernstein retweetledi
Current AI is reactive. You prompt, it responds. True proactivity requires predicting what you'll do before you ask.
Our new work done by @oshaikh13 formalizes this as Next Action Prediction (NAP ): given a user's computer use, predict their next action.
We annotated 360K actions across 1 month of continuous computer use from 20 users and open-sourced a pipeline for private-infra labeling. LongNAP combines parametric + in-context learning to reason over long interaction traces.
This is one step closer to an assistant that proactively anticipates, not just reactively responds 🚀
Omar Shaikh@oshaikh13
What’s the point of a “helpful assistant” if you have to always tell it what to do next? In a new paper, we introduce a reasoning model that predicts what you’ll do next over long contexts (LongNAP 💤). We trained it on 1,800 hours of computer use from 20 users. 🧵
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Predict what the user will do next: a task that underlies a huge number of goals. Better assistants, better user models, the next generation of operating system metaphors...
Omar Shaikh@oshaikh13
What’s the point of a “helpful assistant” if you have to always tell it what to do next? In a new paper, we introduce a reasoning model that predicts what you’ll do next over long contexts (LongNAP 💤). We trained it on 1,800 hours of computer use from 20 users. 🧵
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Michael Bernstein retweetledi
What’s the point of a “helpful assistant” if you have to always tell it what to do next?
In a new paper, we introduce a reasoning model that predicts what you’ll do next over long contexts (LongNAP 💤).
We trained it on 1,800 hours of computer use from 20 users.
🧵
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Percy on AI simulation of human behavior:
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: simile.ai/blog/simulatio…
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Michael Bernstein retweetledi
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Michael Bernstein retweetledi
yes i tweet about simile a lot. yes im obsessed with the peeps. and yes you should work there.
wsj.com/cio-journal/ca…
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Nature thinking through the future of AI simulation of human behavior:
Simile@simile_ai
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: nature.com/articles/d4158…
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Michael Bernstein retweetledi
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:
nature.com/articles/d4158…
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Simile in WSJ!
Joon Sung Park@joon_s_pk
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! wsj.com/cio-journal/ca…
Español
Michael Bernstein retweetledi
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!
wsj.com/cio-journal/ca…
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Michael Bernstein retweetledi
Do you want to run your own social media feed ranking experiment? Check out our new paper in ACM Transactions on Social Computing! 🎉
link.growkudos.com/1f5qrfvgg00
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Michael Bernstein retweetledi
GUM/ @oshaikh13 @valteu1 @gandhikanishk @msbernst @Diyi_Yang (@Stanford) - GUM gradually builds a reliable "model of you" by observing computer activity, so assistants understand context and help proactively without constant re-briefing.
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Congrats to Farnaz Jahanbakhsh & Rayhan Rashed on winning the 🏆 UM Social Impact Engineering Award!🏆
Their #CHI26 work proposes a new paradigm for online moderation: transform content to preserve value while reducing harm — personalized to each user.
rayhan.io/diymod/
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Michael Bernstein retweetledi
スタンフォード大学の「AIエージェントとシミュレーション」の講義資料。
最近の話題だけでなくシミュレーションの歴史を遡り、認知学や社会学的な繋がりから評価手法まで触れているので読み物としてかなり面白い。並列にAIエージェントを大量に走らせる事で作る確率分布の応用先はかなり興味深い。
日本語
Michael Bernstein retweetledi
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! 🎈
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Michael Bernstein retweetledi
Congrats to @joon_s_pk, Michael Bernstein, Percy Liang, Elaina Yallen and the Simile team on their launch today! We’re thrilled to have partnered with this team since we co-led the seed as they define market research in the AI era.
Simile@simile_ai
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