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Stephanie Chan

@scychan.bsky.social
1K followers 277 following 23 posts

Staff Research Scientist at Google DeepMind. Artificial and biological brains 🤖 🧠

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Stephanie Chan @scychan.bsky.social · 21/09/2026
Absolutely. I think that it is a major concern for political economists and e.g. the authors of Gradual Disempowerment and The Intelligence Curse (and also what I try to address empirically with point #2 -- I may try to make that more explicit)
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Stephanie Chan @scychan.bsky.social · 21/09/2026
5/ In light of the above, policies focusing purely on financial transfers (e.g. UBI) are insufficient for ensuring wellbeing under labor displacement. They don't address the non-financial benefits of work, nor the need for agency and choice in work status, and they may even undermine overall agency.
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Stephanie Chan @scychan.bsky.social · 21/09/2026
4/ Work provides important non-financial benefits -- e.g. status, time structure, purpose. But these benefits can also be found outside traditional jobs, e.g. in volunteering, hobbies, and state employment. This lends optimism for alternate sources of work's benefits, and for universal basic jobs.
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Stephanie Chan @scychan.bsky.social · 21/09/2026
On one hand, this evidence points to potential positive futures where the majority of society does not work, causing societal expectations to change. Even so, we should still expect negative wellbeing effects with *partial* work displacement, or in the near-term while existing norms still persist.
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Stephanie Chan @scychan.bsky.social · 21/09/2026
3/ Societal norms matter enormously. As a striking example, unemployed people's wellbeing increases when they reach retirement age -- even without changes in income or routine. The stigma of non-work is lifted, simply by aging out of the "working age" bracket.
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Stephanie Chan @scychan.bsky.social · 21/09/2026
2/ We should be cautious about future scenarios involving systemic financial dependence. It's harder for financially dependent spouses to exit unsatisfactory marriages. And in countries where oil wealth funded subsidies and state employment, citizens have been vulnerable to state repression.
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Stephanie Chan @scychan.bsky.social · 21/09/2026
1/ Involuntary job loss is quite adverse, while voluntary job loss often goes well (e.g. most retirement). This important issue of **agency and choice** is under-discussed in AI policy discussions.
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Stephanie Chan @scychan.bsky.social · 21/09/2026
What does the *empirical evidence* tell us about work and wellbeing? And what does that imply for AI futures? arxiv.org/abs/2609.11019 We reviewed studies of populations across the world, including the unemployed, retirees, lottery winners, and financially dependent spouses. Major takeaways:
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Reposted by Stephanie Chan
Andrew Lampinen @lampinen.bsky.social · 05/08/2025
In neuroscience, we often try to understand systems by analyzing their representations — using tools like regression or RSA. But are these analyses biased towards discovering a subset of what a system represents? If you're interested in this question, check out our new commentary! Thread:
What do representations tell us about a system? Image of a mouse with a scope showing a vector of activity patterns, and a neural network with a vector of unit activity patterns
Common analyses of neural representations: Encoding models (relating activity to task features) drawing of an arrow from a trace saying [on_____on____] to a neuron and spike train. Comparing models via neural predictivity: comparing two neural networks by their R^2 to mouse brain activity. RSA: assessing brain-brain or model-brain correspondence using representational dissimilarity matrices
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Stephanie Chan @scychan.bsky.social · 06/06/2025
Great new paper by @jessegeerts.bsky.social, looking at a certain type of generalization in transformers -- transitive inference -- and what conditions induce this type of generalization
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Stephanie Chan @scychan.bsky.social · 02/05/2025
New paper: Generalization from context often outperforms generalization from finetuning. And you might get the best of both worlds by spending extra compute and train time to augment finetuning.
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Stephanie Chan @scychan.bsky.social · 11/03/2025
It was such a pleasure to co-supervise this research, but @aaditya6284.bsky.social should really take the bulk of the credit :) And thank you so much to all our wonderful collaborators, who made fundamental contributions as well! Ted Moskovitz, Sara Dragutinovic, Felix Hill, @saxelab.bsky.social
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Stephanie Chan @scychan.bsky.social · 11/03/2025
This paper is dedicated to our collaborator Felix Hill, who passed away recently. This is our last ever paper with him. It was bittersweet to finish this research, which contains so much of the scientific spark that he shared with us. Rest in peace Felix, and thank you so much for everything.
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Stephanie Chan @scychan.bsky.social · 11/03/2025
Some general takeaways for interp:
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Stephanie Chan @scychan.bsky.social · 11/03/2025
4. We provide intuition for these dynamics through a simple mathematical model.
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Stephanie Chan @scychan.bsky.social · 11/03/2025
3. A lot of previous work (including our own), has emphasized *competition* between in-context and in-weights learning. But we find that cIWL and ICL actually compete AND cooperate, via shared subcircuits. In fact, ICL cannot emerge if cIWL is blocked from emerging, even though ICL emerges first!
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Stephanie Chan @scychan.bsky.social · 11/03/2025
2. At the end of training, ICL doesn't give way to in-weights learning (IWL), as we previously thought. Instead, the model prefers a surprising strategy that is a *combination* of the two! We call this combo "cIWL" (context-constrained in-weights learning).
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Stephanie Chan @scychan.bsky.social · 11/03/2025
1. We aimed to better understand the transience of in-context-learning (ICL) -- where ICL can emerge but then disappear after long training times.
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Stephanie Chan @scychan.bsky.social · 11/03/2025
Dropping a few high-level takeaways in this thread. For more details please see Aaditya's thread, or the paper itself. bsky.app/profile/aadi... arxiv.org/abs/2503.05631
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Stephanie Chan @scychan.bsky.social · 11/03/2025
New work led by @aaditya6284.bsky.social "Strategy coopetition explains the emergence and transience of in-context learning in transformers." We find some surprising things!! E.g. that circuits can simultaneously compete AND cooperate ("coopetition") 😯 🧵👇
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Stephanie Chan @scychan.bsky.social · 04/01/2025
Sadly, we have lost a brilliant researcher and colleague, Felix Hill. Please see this note, where I have tried to compile some of his writings: docs.google.com/document/d/1...
docs.google.com
For Felix
Devastatingly, we have lost a bright light in our field. Felix Hill was not only a deeply insightful thinker -- he was also a generous, thoughtful mentor to many researchers. He majorly changed my lif...
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Reposted by Stephanie Chan
Andrew Lampinen @lampinen.bsky.social · 10/12/2024
What counts as in-context learning (ICL)? Typically, you might think of it as learning a task from a few examples. However, we’ve just written a perspective (arxiv.org/abs/2412.03782) suggesting interpreting a much broader spectrum of behaviors as ICL! Quick summary thread: 1/7
arxiv.org
The broader spectrum of in-context learning
The ability of language models to learn a task from a few examples in context has generated substantial interest. Here, we provide a perspective that situates this type of supervised few-shot learning...
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Reposted by Stephanie Chan
Noémi Éltető @noemielteto.bsky.social · 09/12/2024
Introducing the :milkfoamo: emoji
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Stephanie Chan @scychan.bsky.social · 09/12/2024
Hahaha. We need a cappuccino emoji?!
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Stephanie Chan @scychan.bsky.social · 09/12/2024
I'll be not at Neurips this week. Let's grab coffee if you want to fomo-commiserate with me
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Stephanie Chan @scychan.bsky.social · 09/12/2024
Hello hello. Testing testing 123
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