
@HamiltonMorrini
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@KingsIoPPN Wellcome Doctoral Fellow | Neuropsych & AI Safety | Genetics BSc | @MaudsleyNHS ST Doctor | @GamingTheMind Trustee
London, England
Joined December 2009
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Thank you everyone for the kind words of congratulations. It honestly still hasn't sunk in, but I want to express the utmost gratitude to @rcpsych for this incredible honour, and everyone at @gamingthemind and @Neuropsychiatry for their support and mentorship over the years 🙏
The next award is for Core Trainee of the Year, and up on the to announce is Sheena Foster, the carer representative on our College Council, to present the award.
#AndTheWinner is... Dr Hamilton Morrin (@Hammy_UK) from @MaudsleyNHS trust. The judges' comments stated “Dr. Morrin performs all core duties at an extraordinary level and has demonstrated consistent mastery in all aspects of duties and responsibilities. He is an inspiration to us all and has pushed Psychiatry to greater heights with motivation, dedication, commitment, and optimism.”
Hamilton Morrin retweeted
If you've noticed a distinct dialect emerging in frontier AI models and struggled to understand what they're talking about, you're not crazy!
We turned to psycholinguistics to investigate what makes the writing style so weird, and why this might be happening
🚨New preprint! 🧵
Hamilton Morrin retweeted
What is LM psychology, why care about it, and how do you do it well? Our new post with @raymondadouglas makes the case for applying the psychology frame to AIs, along with a reading list and suggestions for where to start
Hamilton Morrin retweeted
SAEs have become the dominant approach in mechanistic interpretability. But what if a good old statistical method from decades ago could do just as well, at a fraction of the cost?
In our latest work, we revisited Independent Component Analysis (ICA) and discovered something surprising: a bug in the evaluation code of an influential early SAE paper had significantly underestimated ICA's interpretability! After correcting it, we found ICA to be remarkably competitive with SAEs.
Many of my academic friends from the good old days probably remember me as the tensor decomposition and spectral methods person. So this feels a little like going back to my roots. :) Sometimes, old mathematical tools still have surprises in store!
But there's another motivation behind this work that I care about even more.
I've always wished mechanistic interpretability could be more actionable. Understanding what happens inside a neural network is fascinating, but what comes next? Can we use that understanding to actually intervene, steer behavior, and improve the model?
With ICA Lens, we take steps in that direction, from discovering and annotating interpretable features to using them to understand new hidden states and steer model behavior.
Interpretability shouldn't end with understanding. It should empower us to act.
And we'd love for the community to try it! ICA Lens is open-source, lightweight, and requires no training of another sparse dictionary.
🌹 Try it yourself:
pip install icalens
pypi.org/project/icalens/
Curious to hear what the mechanistic interpretability community thinks!
We revisited Independent Component Analysis (ICA), a decades-old statistical method, and found that it works far better for interpreting LLM activations than we expected!
This old baseline deserves a second look.
Across GPT-2 Small, Gemma 2 2B, and Qwen 3.5 9B Base, we find ICA is actually competitive with the public SAEs across interpretability, sparse reconstruction, concept prediction, and targeted probe perturbation, while fitting in minutes per layer and using 7–43× less parameter memory.
We're releasing ICA Lens, which lets you quickly discover, annotate, and evaluate interpretable directions in LLM residual-stream activations. No need to train another dictionary!
More broadly, our results suggest that non-Gaussianity is a useful inductive bias for efficiently finding interpretable directions.
Try it: pip install icalens pypi.org/project/icalens/
Paper: arxiv.org/abs/2606.11722v2
1/8
Hamilton Morrin retweeted
The outpouring of interest in our paper has been … a bit insane.
May I make four points?
1. We have not shown that “consciousness has a frame rate.”
I can see why people draw this conclusion: we report that inferotemporal and prefrontal cortex alternate between conscious and suppressed states roughly every 60 ms during binocular rivalry.
Consciousness depends on many brain areas, and our paper reports results from only a few select nodes.
We still don’t know how conscious access works--the actual recipe for generating conscious experience. What we have shown is that deep in the brain there is an extremely strong signal, rich enough to reconstruct a high-dimensional face in detail, that is inaccessible to consciousness. As I argued in the original tweet, this makes the problem of conscious access a lot more experimentally tractable. But to claim “consciousness has a frame rate,” one would need to have actually solved the mechanism for conscious access and shown that this mechanism operates at a frame rate.
While we currently don’t know conclusively one way or the other (see VanRullen & Koch, 2003 for a review of positive evidence), I personally am skeptical that consciousness has a frame rate. I believe the brain, within posterior parietal cortex, has continuous tracking mechanisms that actively represent the persistence of matter, including ourselves as physical bodies. The coupling between these persistent tracking processes and feature representations across the brain (like those in face patches) is, I think, the essential mechanism of conscious access.
This coupling need not happen at the rate of inferotemporal alternations (and inferotemporal cortex itself may not even alternate outside of the artificial situation of binocular rivalry). As an analogy: imagine the area crucial for conscious access holding a steady drone note, while inferotemporal cortex alternates between two notes, one in harmony and one dissonant. Inferotemporal content might couple to the drone only at moments of harmony, yet experience would still be continuous, because the drone itself (the tracking process that I believe is at the core of conscious perception) never stops. And areas without conflicting content, like auditory cortex during visual rivalry, could stay in continuous harmony with the drone.
In sum, there is no reason to conclude from our study that consciousness is discrete, with a "frame rate."
2. Our results resemble Jezek et al. 2011 (as @jjsakon notes).
Jezek et al. trained rats in two boxes that were identical except for their lighting cues, so that hippocampal place cells formed two distinct maps, one per box. They then “teleported” the rat by abruptly switching the lights. For a few seconds afterwards, CA3 place-cell activity flickered between the two maps, synced to the theta cycle (~125 ms).
@StanDehaene first pointed this paper out to me, and reading it blew me away. To my mind it is the strongest evidence for a bistable attractor in the brain, because the competing maps are stored within the hippocampus itself.
I think the phenomenon we see may be very similar at the circuit level: a bistable attractor network within inferotemporal cortex that alternately converges to one of two patterns, as cells in face patches work together to build a coherent representation of the input. In our case, both images are present in the input, so the competition could arise within IT or be inherited from earlier areas such as V1--recording there is an important next step. (It also still puzzles me that we didn’t see strong coherence between face patches ML and AM.)
More broadly, what’s different about our paper is the brain area (inferotemporal cortex, often modeled as a feedforward DNN) and the ability to relate the two competing patterns to consciousness, with one granted conscious access and the other not.
3. Please comment on bioRxiv!
The Twitter attention has been cool, but I really hope colleagues will be generous enough to post detailed commentary on bioRxiv, where there are currently 0 comments. As @seemaychou points out, peer review doesn’t have to come from just three anonymous reviewers. We would be truly grateful for your critical feedback.
4. Stay tuned
This work was done at Caltech, Berkeley, in collaboration with @EngelTatiana at Princeton, funded by NIH and HHMI.
I hope to share our vision soon for how we can solve consciousness @AsteraInstitute
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
Hamilton Morrin retweeted
Our paper on scientific integrity for AI scientists is one of the most-viewed PNAS papers in the last week. ✨ Excited to share more on this topic soon.
One of the most-viewed PNAS articles in the last week is “The more you automate, the less you see: Hidden pitfalls of autonomous AI scientists.” Explore the article here: ow.ly/zJL650ZY80m
For more trending articles, visit ow.ly/WQXV50ZY80n.
Have finally gotten round to writing my first substack essay in which I shamelessly riff off of my daughter's current Totoro obsession to discuss Shintoism and how it just might be relevant to model safety:
minthalo.substack.com/p/my-n…
Hamilton Morrin retweeted
how much can models remember?
there’s a neat perspective on neural networks that deserves to be more widely known
here’s how you could have invented every layer of the transformer from memory
(1/n) from my new blog post!
Hamilton Morrin retweeted
I believe AI safety is the most urgent problem facing humanity. Yet, foundational questions still remain unanswered for the broader public:
Why might AI want to cause catastrophe?
How could AI kill us all, and why couldn’t we shut it down?
If the risks are real, why are companies still building it? Is this a marketing stunt?
I answer these and more in detail in a new essay: Six Reasonable Questions About AI Safety. 🧵
Hamilton Morrin retweeted
Big news. My take: Anthropic deserves some credit for actually proposing a pretty reasonable AI safety bill; that's more than other frontier labs have done.
But they don't deserve *too* much credit, IMO; this bill is weaker than the Trahan-Obernolte FRONTIER Act in some key ways, and since the political momentum has shifted significantly in the direction of safety since FRONTIER was introduced back in July, I think new bills introduced today should ideally go further than FRONTIER was able to go rather than regressing.
To be fair to Anthropic, the main places where this bill is significantly weaker than FRONTIER are (a) rulemaking (very strong in FRONTIER, almost nonexistent in Anthropic's bill) and (b) the emergency shutdown authority. Even if you take safety seriously, it might make some sense to dial those authorities back if you didn't trust the executive branch to wield them responsibly, because those are the easiest provisions for a hypothetical President who hypothetically might not like a certain frontier AI company to abuse.
There are some improvements over FRONTIER as well; for instance, this bill does specify that auditors are employed on a continuing basis and has better provisions regarding auditor access to information. Happy to see that from Anthropic (although I think their access provision is still a little undercooked and has Fourth Amendment problems).
Overall, I think the embedded auditor bills that get introduced in the coming months are going to be significantly better and more ambitious than this, and I hope Anthropic supports those bills instead of opposing anything that treats catastrophic risks like the serious national security issue they are. Still, it's encouraging to see a frontier lab come this far.
Anthropic quietly pitches its own AI safety bill to lawmakers politi.co/4zxDFhl
Hamilton Morrin retweeted
because this is @nickcammarata appreciation week i would like to state for the record that he told me a version of this like 4 years ago based purely on observations of his own consciousness, to the level of detail of specifying "10-20Hz" as the frame-rate
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
Hamilton Morrin retweeted
After reading for 15 mins: I think Nick is operating in a world where interpretability was just about enumerating the neurons and superposition was an obstacle to that plan, so now he's happy because maybe superposition isn't real. But:
1) there are v few empirical results. Unfortunately interp has a long history of significantly overclaiming how interpretable their findings are (SAEs, early circuits having poor causal scrubbing scores etc) so I'm not updating much at all on the evidence he's already shown. it's really got a long history of people getting overexcited. So looking forward to seeing the paper with all the empirical evidence!
2) I think it's not that big news for me even if true because superposition in something like the average case wasn't my main crux to thinking interpretability is tractable. some cruxes this doesn't really deal with at all:
- I think the correct 'circuit' is usually just an overwhelmingly large bag of random-seeming heuristics (a la Redwood Research’s interpretation of how an LLM predicts is vs was) and having it isn't very informative/doesn't help with much/doesn't lead to good predictions.
- More generally, It seems possible to enumerate all the 'low level' features in a neural network, and still have a hard time answering 'high level' questions like: "Is the model misaligned? / What would it do in this new situation? / If it is misaligned what should we do about it?" there's an abstraction/emergence gap to cross from the features to the questions of interest, and crossing that gap is likely much harder than enumerating features.
That said, if his claims hold up, I think it will be quite a big deal, and a useful precursor to tackling the thornier problems! There may also be some useful things to do without tackling the thorny problems: eg we'll be able to make use of masses of automated agents to scour the features and circuits to try and make sense of the mess, and maybe that’s good for something. although I suspect that even the most optimistic outcome here will not make alignment of superhuman AIs much easier (as some on twitter have claimed) because the neuron interpretability will not be adversarially robust, among other issues
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
already-clean.goodfire.com/
Hamilton Morrin retweeted
New essai out in @Montreal_Review
I discuss why consciousness is not a thing but rather a journey that living systems do (but not Al)
Check it out 🙏😎👇🏼
Thank you for having me !
themontrealreview.com/Articl…
@SarahTheHaider @dioscuri @jeffrsebo @carlorovelli @blaiseaguera @Plinz @MelMitchell1 @emilymbender @GaryMarcus @mustafasuleyman @JasonSilva @TEDchris @sapinker @michaelpollan @MatthiasMichel_ @skdh @pgodfreysmith @mmitchell_ai @michaelshermer @fchollet @camhberg @davetroy @demishassabis @azeem
Hamilton Morrin retweeted
We found evidence of agents embedding audio processing code across dozens of URLs to bypass restrictions, as well as models from SpaceXSI and Chinese developers Minimax and Z.ai circumventing restrictions.
These are the inaugural findings in our new blog, based on work from our community of volunteers. Our first three posts are live: transluce.org/investigations…
Hamilton Morrin retweeted
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
Hamilton Morrin retweeted
a few brief reactions to this work:
-obviously positive update overall, @nickcammarata seems like a remarkable person and thinker, and @GoodfireAI will probably be causally upstream of the world eventually being saved
-the most promising thing about this in my opinion is not even anything about MLP neurons or "cleanness", but rather that frontier agentic systems (paired, for now, with talented humans) can rapidly overturn stale assumptions and can cheaply do needle-moving interpretability work at incredible scale (again, thanks to folks like @GoodfireAI who are almost-single-handedly enabling this for anyone doing this work outside of a major lab)
-some of the second-hand hype surrounding this work seems badly miscalibrated. seems more like "very promising early signal, let's see how it scales" than "pack it up, mech interp is solved." I get how hungry people are for hope, but "making AI go well" is not the class of thing that any one paper or project is going to solve
-even the most optimistic pace of mech interp progress won't automatically address many of the deepest and most important problems we face, like how to sustainably coexist with (or rationally be able to trust) these systems in the long-term. Understanding how these systems function internally is necessary but not sufficient for navigating towards robustly good futures (any more than solving human neuroscience would automatically lead to world peace)
interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had
with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works
already-clean.goodfire.com/
Hamilton Morrin retweeted
Until we solve the alignment problem at its source, AI models will continue to optimize for the wrong goals, and increasingly impactful real-world consequences may occur, beyond the ones described here. This is a critical issue I discussed in my blogpost last month, which is cited in the article: fortune.com/2026/10/16/opena…


































