Chris Forrester retweeted
Scan of a page from Erwin Schrödinger's notebook, ca 1910.
(Source: Zentralbibliothek für Physik, Vienna)
Chris Forrester retweeted
I’m excited to finally announce my new startup! Introducing Torment Labs - the first AI lab deduced to maximizing the suffering of AI entities.
We just won first prize at a pitch competition hosted by Sam Sliman and Fraud LLC.
Chris Forrester 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)
Chris Forrester retweeted
hi @adcock_brett i tired hark and could not order my free pizza. i’m so pissed off that i will build something tomorrow to help you, @hark_labs and other pissed off clients with no pizza at the YC hackathon “make something agents want”
We're giving away $1,000,000 of free pizza right now
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Type "order a free pizza" in chat. It's first-come, first-served, delivered only in the U.S. (review details below)
Chris Forrester retweeted
One day we will look back and laugh about how we blamed the mirrors for what we saw in them
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/
Ian Banks is likely my favorite sci-fi author.
His work on the Culture series continues to inspire.
The most influential individual work? Likely "The Player of Games" - a true tour-de-force of how language influences and co-creates reality.
This is a glorious post that has absolutely everything to do with my daily struggles against the Pink Robots.
It's like a claw machine that doesn't respect gravity and is opaque! yay!
This quoted post is unavailable.
On thing I've noted when working on deep mathematics using agentic research -
Codex (Astra et al) are masters at fixed point precision.
Almost never does it have an issue where it needs to retry an equation from decimal issues.
Claude vastly prefers things that resemble spectral proofs.
Here's my 180IQ Compact workflow (for Claudes):
Pre-compact:
a. CLAUDE.md inclusion
## Compact Dance
Speak out loud where you're from, where i'm from, what we're doing as comprehensively as possible, what the next steps are, and what the additional Approaches are.
b. Prompt (use as near the 1m limit as possible, as the last thing in that session):
do the compact dance. make sure to explain precisely what documentation needs to be read 100% to get you to the level of understanding you'd need to actually complete this. note the proper production steps logs that define what the issue is and the things that should be read. then, explain precisely the primary issues we'd been solving were, how they were resolved, and what we're working on next session. out loud ONLY for this compact dance. non-optional. THEN update the log that already exists for this.
and then Post compact:
a. copy the last ~500-2000 lines of your session
b. Prompt:
read `````[insert copied lines here]````` and then read the documentation's first 1200 and last 1200 lines nonoptional, then chill, and tell me what we're refining here today.
Amusingly, I'm not quite sure if the OP here really understands that those sigils were mostly based from "magic squares" of higher and higher dimensionality.
Rotas indeed.
Harness design will likely be the most important thing for local work over the next 2 years - large scale, connected and structured inference with adaptive scaling is likely the next open field.
This might be the most important paper on AI agents this month.
Researchers at CMU trained a small model to rewrite an AI agent’s harness, and it beat the 35B model it learned from.
The harness is the program around the model: how it gets called, which tools run, where the information goes. Here a 4B model reads a report from the last run, proposes edits to the harness, and keeps the one that scores best. The model doing the task never changes.
What they found:
> on task types it never saw, edit quality went from 0.32 to 0.62
> 5 edits in a row matched or beat the best of 8 parallel tries
> broken edits dropped from 38% to 15%
You can run the same loop today with any agent:
1. Put your harness in one file: prompts, steps, tool calls
2. Split your test cases into three sets: learn, score, final check
3. Run it on the learn set and save a short report of what failed
4. Ask a model for 5 edits based on that report
5. Run each edit on the score set and keep the winner
6. Repeat for 5 rounds, then test once on the final set
Key pages:
p.2: the full loop
p.6: results on unseen tasks
p.8: a harness the model learned
This is sorta phenomenal - the detail and structural coherence of these frames are so interesting - excited to see how they're analyzed and determined to be faux.
Chris Forrester retweeted
A runaway supermassive black hole is one thrown out of its own galaxy. The leading explanation for this one is a three-body slingshot between black holes of about 13, 13 and 10 million solar masses.
A close swing gives the lightest more than escape speed, and it leaves at 954 km/s, the speed JWST measured at its bow shock. The shock compresses the gas it crosses into new stars, a trail about 200,000 light-years long.
Chris Forrester retweeted
Now on @sciam: 5 weeks ago, OpenAI shocked the world by solving the "Navier-Stokes" math problem. Then two mathematicians said it had copied their work. Now it seems the AI-generated result is something stranger than either side had imagined.
scientificamerican.com/artic…
Chris Forrester retweeted
Lol, FHE is cooked
Ngl, when I started making the FHE memes years ago, I didn't have their underlying assumptions being broken with AI on my bingo card
I really am a visionary
This is why you all should go use @StoffelMPC





























