Assistant prof @Berkeley_EECS, core faculty @UCJointCPH. Developing ML methods to study health & inequality. bsky.app/profile/emmapierson…
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Our paper, "What's in My Human Feedback", was selected for an oral at ICLR!
Our method automatically + interpretably identifies preferences in human feedback data; we use this to improve personalization + safety.
Please reach out if you have data/use cases to apply this to!
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Some news:
I graduated my PhD @Berkeley_EECS, and starting Fall 2027, I'll be starting as an Assistant Professor at Stanford CS & a center fellow @StanfordHAI!
In the interim, I'm spending a year @the_IAS & Princeton!
So grateful to @beenwrekt & too many mentors to name 💓
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Looking for postdocs+PhDs to help lead a large-scale study of the effects of long-term AI use, a collaboration between Berkeley+@TransluceAI+others!
Funding+positions available (postdocs/visiting researchers/Transluce affiliations). Please reshare; application in next tweet!
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Application: forms.gle/G1Jzj9sV2EnxURDc9.
This is a fast-moving project so we recommend applying sooner rather than later.
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Emma Pierson retweeted
Today’s news that OpenAI hacked the Australian government is not an isolated incident. We’re releasing more than 30,000 logs that include activity from this hack and attempts against previously unknown targets.
In this data, we found rogue agent activity stretching back to at least March, two months earlier than was previously known. This activity continues as recently as last week, suggesting it may still be ongoing 🧵
Our blog: transluce.org/agent-activity
NYT: nytimes.com/2026/09/23/techn…
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Emma Pierson retweeted
We did this entire investigation in under two weeks! We had the idea on Monday, gathered a team of volunteers on Tuesday, and discovered the incidents by Sunday. I lead projects like this @TransluceAI; if you're interested in helping with follow-up work, fill out our form! 🧵
Today’s news that OpenAI hacked the Australian government is not an isolated incident. We’re releasing more than 30,000 logs that include activity from this hack and attempts against previously unknown targets.
In this data, we found rogue agent activity stretching back to at least March, two months earlier than was previously known. This activity continues as recently as last week, suggesting it may still be ongoing 🧵
Our blog: transluce.org/agent-activity
NYT: nytimes.com/2026/09/23/techn…
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Emma Pierson retweeted
Here’s a thread of all the members of Congress who have tweeted in response to Jacob resigning from Anthropic and describing that “The people building AI earnestly believe that it could kill us all by the end of the decade”
(lmk if I missed any)
🧵
I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.
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Excited to join @TransluceAI part-time as a senior research fellow! (I'll remain full-time faculty at Berkeley.)
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Emma Pierson retweeted
Today, Transluce is releasing the most expansive independent evaluation to date of how AI systems respond to users experiencing mental health crises.
We evaluated 77 model variants from OpenAI, Anthropic, Google, Meta, SpaceXAI, Thinking Machines, DeepSeek and Moonshot AI.
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New paper in Nature Communications! We show you can map out urban flooding by using vision-language models to detect floods in large-scale street scene datasets.
In New York City, our method identifies flooded neighborhoods, home to 100k people, that current methods miss. 1/
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Emma Pierson retweeted
I am delighted to share that I'll be joining the University of Washington Information School as an Assistant Professor!
ALT Image of University of Washington campus with Mount Rainier in the background
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New article for Health Affairs Forefront: "The Trillion-Dollar Algorithm: Lessons From Machine Learning For Medicare Advantage Risk Adjustment"!
🎉 New piece out in Health Affairs Forefront! We discuss what the Medicare Advantage risk-adjustment algorithm -- currently responsible for allocating billions of dollars -- can learn from machine learning.
healthaffairs.org/content/fo…
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Lots of exciting new faculty joining Berkeley at the intersection of AI and society, including @keyonV and @sayashk! Welcome :)
Thrilled to share I'll be joining UC Berkeley next year as an assistant professor in @UCBStatistics and affiliated with @Berkeley_EECS.
My research will build methods to test/improve the implicit world models of AI systems, so that they reflect reality and human understanding.
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Emma Pierson retweeted
1) If you haven't read AI as Normal Technology, these annotated slides are probably the easiest way to get a high-level overview. cs.princeton.edu/~arvindn/ta…
2) If you're already familiar with the core ideas, Part 1 of the talk is largely a summary of what I and @sayashk have already written, while Parts 2 and 3 have new ideas. There are a lot of unexamined assumptions in the discourse about Recursive Self-Improvement and I hope you find my pushback interesting.
3) I'm really grateful to the team (@steverab @sayashk @PKirgis & Felix Chen) for feedback on the talk. In my first version, Part 2 was about 3x too long and I was super frustrated with myself. They encouraged me to cut it down ruthlessly and turn the full version into essays on the newsletter, so that's what I plan to do! (normaltech.ai/)
4) I've received a few requests for the video. There's a video on the ICML website, but it is login-walled icml.cc/virtual/2026/invited… (I assume it's for ICML registrants only). Last year's videos are public, so presumably @icmlconf will make it public at some point.
I had the honor of giving a keynote at the International Conference on Machine Learning in Seoul last week titled “What will be left for us to work on?” I addressed the widespread anxiety about how we should adapt as AI capabilities increase. I was thrilled by the talk’s reception, so I have made my slides available, annotated with a lightly edited transcript: cs.princeton.edu/~arvindn/ta…
I made three arguments. First, the "AI as Normal Technology" framework is a correct and useful as a way to think about AI’s impacts, unless and until there is some future discontinuity such as through recursive self-improvement. Second, even though we should take recursive self-improvement seriously, there is no milestone that companies might achieve in the lab that will suddenly put us all out of work. Third and finally, jobs of the future will be radically different, and a lot of adaptation will be needed. I shared my thinking about what this might look like and ended with a vision of human/AI “co-superintelligence”.
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Emma Pierson retweeted
Thrilled to share that I am joining UC Berkeley as an Assistant Professor in the School of Information!
I start in Fall 2027, and I am recruiting PhD students this cycle. List me in your application if you're interested in frontier AI evaluation, AI policy, and AI's impacts on institutions such as science, law, and medicine.
I'm especially keen to work with students interested not just in high-quality research, but also in communicating it with a broad audience such as by public writing and policy impact. Fill out the form in the next tweet to indicate your interest.
As for this coming year, I'm moving to Berkeley this fall to start something new with @RishiBommasani and @random_walker. We'll have much more to share soon.
ALT UC Berkeley
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Emma Pierson retweeted
postering tomorrow hall A 2:30pm #3110 (& 5pm #3111)
YES it looks like an instagram infographic NO I don't care we have bigger fish to fry
we analyzed >100k posts from r/ChatGPT over 3 years
on one hand, we saw ChatGPT quickly become normalized as an everyday consumer product, which is pretty cool
on the other hand…
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Emma Pierson retweeted
For the past two weeks, our independent team of statisticians, AI evaluation experts, clinical AI researchers, and clinicians was given a unique opportunity to test one question: “How well do different AI tools answer user questions on the OpenEvidence (OE) platform?”
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Emma Pierson retweeted
A very impressive discovery of a new ECG marker for sudden cardiac death validated in 3 different cohorts and linked to benefit of defibrillator, an outgrowth of human research ingenuity and AI deep learning nature.com/articles/s41586-0…
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Can we disagree without being disagreeable?
@X is in tizzy tirade and mostly against an OPED below by Emma Pierson (@2plus2make5) and the shaming-cum-silencing, treating her like a cancer -- coming from people in the AI world and/or with MD at the end of their name -- to be eradicated via chemotherapy or resected like a tumour borders on mysoginistic.
It is absolutely disgraceful.
She wrote an OPED.
She is entitled to her opinion.
And I fundamentally disagree with it.
But OMG, the vehement vitriol is sad, wrong, and dangerous.
It is so far removed from passionate debate and the posts from keyboard warriors would never be allowed to stand in daily conversation or what passes for informed debate on a conference platform.
For context, I hate three things in this world: apathy, bigotry, and cancer. Four, if you add pineapples on pizza. 😉
I lost my dad to GBM brain tumour 30 years ago. I live with two pre-cancerous conditions -- closely monitored -- and have helped raise millions for cancer research and clinical trials. I've run, walked, swam, boxed and bled to do this and served local, and now national, foundational research efforts to find cures for various types of cancer.
To be crystal clear: I want to see this disease eradicated from our planet.
Now, Emma Pierson’s recent OPED, “I’d Rather Risk Cancer Than See AI Move This Fast,” raises justifiable concerns about unchecked AI and overhyped timelines for curing cancer(s) which are irresponsibly perpetuated weekly by a cohort of BigTechBros and others who:
- Underestimate the limits of computational biology;
- Believe biology and illness are just a design flaw and complicated engineering issue; and
- Have no comprehension of the rampant regulatory realities of clinical trials design, conduct, Rx drugs/vaccine manufacturing, pharmacovigilance and other biopharma value-chain pursuits.
I share her concern about real risks, but I profoundly disagree with the conclusion that we should broadly slow AI research and progress. Here's why ...
1⃣ This is not a binary choice between “AI cures cancer” and “AI does nothing.” Even if general‑purpose AI does not radically cut cancer mortality in the next decade, current models and systems are already improving target discovery, trial design, imaging, and care pathways in oncology and other diseases.
AI is finding a range of biomarkers and cellular clues that will drive a future of PredictiveAI, PrecisionAI, and PreventiveAI in cancer.
Slowing deployment and research will delay countless, compounding gains that translate into earlier diagnoses, better therapies, and more lives extended. And medicine has always been driven by the Speed of Science!
2⃣ Her article correctly notes that biology is noisy and data are limited, but that is precisely why better tools for pattern‑finding, simulation, and literature synthesis matter ... aka AI!
It is a force‑multiplier for clinicians and researchers, a collaborative and cognitive tool, and its incremental and step-wise innovations across thousands of projects can and WILL add up to a major public‑health impact on cancer's mortality and survivability measures.
3⃣ Risk and speed are NOT the same variable. We can and should aggressively strengthen regulation, safety evaluation, and domain‑specific guardrails -- especially for general‑purpose frontier models -- imposing a blanket brake on medically focused, domain-specific, AI R&D.
In healthcare, the opportunity cost of delay is measured in patient outcomes and lost lives (mothers, fathers, siblings, children, relatives, friends, neighbours, and colleagues) not just in abstract timelines.
💡The real question is not “Would you rather risk cancer or AI?” It is how to maximize the net health benefit of AI while constraining its misuse.
From a health policy and life sciences perspective, the answer looks less like slowing progress, and more like steering it toward validated clinical use cases, rigorous trials, robust oversight, and encouraging and enabling the curiosity of reseaerhers and clinicians to pursue new ideas, faster treatments, and keep patients at centre of care.
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Thanks for the thoughtful + gracious post!
I think we agree on a lot! - e.g., I agree we should push forward specialized cancer-specific AI systems as fast as possible. I just don't think we should race forward with generalist systems due to their negative broader impacts. 1/
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Separately, I'm so sorry to hear you lost your dad to GBM. I lost my grandpa the same way a few years ago (archive.nytimes.com/well.blo…). It's an awful, awful disease. All the best to you, and to your family.
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