Michael Bernstein

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Michael Bernstein

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 Edilen19K Takipçiler
Michael Bernstein
Michael Bernstein@msbernst·
How do we know, rigorously, if AI simulation is accurate? This is how. If you care about people, methodology, and measurement, and want to join the charge, let us know!
Sanjay Kairam@skairam

I’m one month into @SimileAI, and I’m even more convinced that evals for simulating human behavior is one of the most interesting problems in AI. That's why we’re growing our small-but-mighty Evals team by hiring multiple Evals -- MTS roles in SF + NYC. jobs.ashbyhq.com/simile/33d7507…

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Mihika Kapoor
Mihika Kapoor@mihikapoor·
I’m hiring a video game designer @simile_ai The first thing we ever built was a top-down pixel-art town called Smallville. It looked like a game, but the characters were the world's first ai agents living their own lives. Now we're building simulated worlds full of people who act like people, and we want a website that feels like one too. if you obsess over games, sprites, systems, worldbuilding, behavior, and making things feel alive, DMs are open :)
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Joon Sung Park
Joon Sung Park@joon_s_pk·
Simulation and AGI were the twin pillars of advanced civilizations in all my favorite sci-fi. When we created Smallville and agents in 2023, the romantic in me couldn’t resist devoting my career to it -- now with an incredible team @simile_ai. Fun conversation @sonyatweetybird!
Sonya Huang 🐥@sonyatweetybird

The advanced civilizations of sci-fi legend (Banks, Asimov, etc) have some form of simulation to guide society. @joon_s_pk is taking a crack at building that simulator with @simile_ai. As Joon's cofounder @percyliang puts it: great science starts with a great measurement. From Smallville in 2023 to today, Simile is trying to build the Hubble Telescope equivalent for simulating human behavior. 00:00 Introduction 01:49 Building Generative Agents 02:29 Valentines Day Emergence 03:33 From GPT 3 To Agents 05:03 Social Computing Problem 06:19 Social Simulacra Subreddits 07:57 Models Getting Good Enough 08:57 Humans Are Not Rational 10:04 Turning Research Into Simuli 11:55 Validation And Accuracy Proof 12:43 Customer Workflow CVS Example 16:11 Why Collect Real Data 17:51 Behavioral Signals And RCTs 21:52 Use Cases And Second Order Effects 26:31 Evaluating Convergence Divergence 31:58 Big Societal Simulations Ahead 36:08 Future Of Simulation

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Michael Bernstein
Michael Bernstein@msbernst·
Natasha is an absolutely stellar designer, elevating everything she touches. Simulation is a blank page for interaction: how do you support people in authoring everything from simple queries to complex simulations, and then make sense of the result?
Natasha Tenggoro@natashatenggoro

hey! I recently left Figma and joined @simile_ai as founding designer. I’m stoked to help shape future paradigms for how people interact with simulations. also….we're hiring in NYC & SF 👀 if this sounds like your kind of problem, come build with us!

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Natasha Tenggoro
Natasha Tenggoro@natashatenggoro·
hey! I recently left Figma and joined @simile_ai as founding designer. I’m stoked to help shape future paradigms for how people interact with simulations. also….we're hiring in NYC & SF 👀 if this sounds like your kind of problem, come build with us!
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Mihika Kapoor
Mihika Kapoor@mihikapoor·
There’s a lot of existentialism about the role of design in an AI-forward world. And yet there is no one better suited than @natashatenggoro to epitomize what a powerhouse design can be in this new era. Incredibly honored to welcome her to the team 🤩 Since joining Simile, Natasha has uplifted the entire company - crafting a design system that instantly upleveled what customers will experience, building an abundance of prototypes that define the future, not to mention shipping PRs to the core codebase nearly every day. I’ve always believed that the best work comes from drawing outside the lines. Simile uniquely gives folks the opportunity to do exactly that: building novel products that make cutting-edge research useful and accessible. If you’re interested in drawing outside the lines at the intersection of product and research, reach out 🙂
Natasha Tenggoro@natashatenggoro

hey! I recently left Figma and joined @simile_ai as founding designer. I’m stoked to help shape future paradigms for how people interact with simulations. also….we're hiring in NYC & SF 👀 if this sounds like your kind of problem, come build with us!

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Paul Novosad
Paul Novosad@paulnovosad·
An Econ PhD student at the 20th ranked program who is working on stuff they are passionate about will have a better job market than one at MIT who's been doing nothing but phd-app-maxxing since undergrad. People get confused by this because they don't observe *how* successful people came about their insane knowledge bases. It wasn't by relentlessly grinding away at stuff because they had to. They look at Scott Kominers and say "if i grind and learn as much math as he did, i will be successful." You can't! *You* can't learn as much math as Kominers because he gets energized by configuration results for type ii lattices. You will burn out if you try to do it this way. You cannot, through grind alone, learn more about the economics of cities than Glaeser, or about how to maximize a value function than Acemoglu. Research careers are long. Most people give up and stop working on research (graph is share of elite PhD graduates with at least one publication in year X after graduation). If you're starting a PhD, you're presumably doing it to have a successful 40-year research career. The number one factor in whether that happens is not which program you get into, it's whether you find a research angle that energizes you enough to push through the endless barriers an academic career throws in your path. This is why a lot of the received wisdom around PhD applications is wrong. If you're 100% consumed by the predoc rat race already, it's going to be a long, hard road ahead. Obv you still have to do admissions, you should study a lot for the GRE, sigh it seems like taking real analysis is probably worth it. But spending time on the things that energize you about economics is a no-brainer, whether it's policy, or blogging, or whatever, you gotta do the things that light your fire and make you want to be on this road.
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James Landay
James Landay@landay·
Honored to lead this merged institute with a new vision and excited to have @drfeifei and John Hennessy advise me.
Stanford HAI@StanfordHAI

Big news! @Stanford is merging @StanfordHAI & Stanford Data Science into a single institute, led by @landay. Continuing under the HAI name, the institute seeks to advance AI & data science for discovery, transform education, and shape AI’s societal impact: news.stanford.edu/stories/2026/0…

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Nilou Salehi
Nilou Salehi@nilou_salehi·
It was standing room only at the kick-off for our research series on continual learning. Thank you to @NikzadAfshin (@across_ai ) @sarahookr (@adaption_ai) and @mralbertchun (AI Circle) for hosting! @oshaikh13 shared his research on human grounding in continual learning. It was so cool to be reminded of the old Apple Knowledge Navigator and how close we are to it and yet how far we still are :) how much easier some questions have gotten and how some remain so hard. Omar, you reminded me of my PhD defense where at some point I annoyed Maneesh so much he said: you can't keep saying "depends on the user context" in response to every question 😅 youtu.be/umJsITGzXd0?si… Stay tuned for the next meetup next month and check out Omar's research with @msbernst and @Diyi_Yang : •⁠ ⁠Creating General User Models from Computer Use (arxiv.org/abs/2505.10831): an architecture for a model that learns about you by observing any interaction with your computer, building confidence-weighted propositions about preferences and intent. •⁠ ⁠Learning Next Action Predictors from Human-Computer Interaction (arxiv.org/abs/2603.05923): predicting a user's next action from their full multimodal interaction history (screenshots, clicks, sensor data) rather than just typed prompts.
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Serina Chang
Serina Chang@serinachang5·
🎉 Thrilled to have two papers accepted to ACL 2026 main! 1. Graph-based models match LLMs on close-ended human simulation tasks with far less compute & greater transparency 2. (oral) How to allocate human samples towards fine-tuning vs post-hoc rectification in simulation
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Dora Zhao
Dora Zhao@dorazhao9·
Excited to be sharing three papers at #CHI2026! 1⃣ Value Alignment of Social Media Ranking Algorithms 2⃣ Mapping the Spiral of Silence: Surveying Unspoken Opinions in Online Communities 3⃣ Whose Knowledge Counts? Co-Designing Community-Centered AI Auditing Tools with Educators in Hawai`i
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Omar Shaikh
Omar Shaikh@oshaikh13·
This is one of my fav figures in our paper. You can: 1. Identify a user's objective by observing general interaction with their computer. 2. Use it to construct a "just in time" rubric. 3. Sample bunch from model and SCALE TEST TIME COMPUTE ON LITERALLY ANY OPEN-ENDED TASK?!?
Michelle Lam@michelle123lam

Once you have JIT objectives, you can embed them into various LLM architectures via existing generators and evaluators. Evaluations on N=205 participant-provided inputs show that JIT objectives produce user-preferred outputs, whether generating experts, tools, or feedback.

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Robb Willer
Robb Willer@RobbWiller·
Very grateful to receive a Guggenheim Fellowship. I’ve been so lucky to have such incredible students, collaborators, and mentors whose coattails I’ve hitched myself to for many years. Above all, I feel very lucky that I get to work on topics I care deeply about with collaborators who are also my friends.✊❤️#guggfellows2026
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Michael Bernstein
Michael Bernstein@msbernst·
This paper was a fascinating experience where, when we first submitted it, reviewers refused to believe that we could create contentious social media content with LLMs. This time around, they saw it as the main point of novelty. As time passed, our work got _more_ novel?
Dora Zhao@dorazhao9

2. Mapping the Spiral of Silence: Surveying Unspoken Opinions in Online Communities w/ @Diyi_Yang @msbernst We introduce a human–AI pipeline to measure the spiral of silence across political subreddits, revealing how community design shapes when people choose to stay silent online. Preprint: arxiv.org/abs/2502.00952

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Michelle Lam
Michelle Lam@michelle123lam·
Most of what I actually need help with, I never think to tell a model. But why is it on me to remember? Our new paper asks: what if AI could proactively specialize to individuals and the tasks they’re carrying out at this very moment? 🧵
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