trentmkelly/LessWrong-43k huggingface dataset:
How Close We Are to a Complete List of Imprinted Genes
by Morpheus — 2025-04-19T18:37:57.074Z
Link: https://www.tassiloneubauer.com/docs/imprinting-list.html

This post summarizes some of the research I have been doing for
Bootstrap Bio AKA kman and
Genesmith

TL;DR

We currently don’t have a list of all imprinted genes that are
important in human development, but further long-read sequencing in
adults and in the human placenta is going to get us close to that
goal.

Measuring DNA methylation in a low input DNA context is really
annoying, because usually we can’t amplify methylated DNA. (Edit 2025-06-16: This method is probably not as good as the improved bisulfite sequencing method I mention below) I explore
the only way that appears tractable to me to amplify methylated DNA by combining Phi29 and DNMT1. This could be useful for building an atlas of early embryo
development and for epigenetic preimplantation embryo screening.

Correct imprinting seems particularly achievable in Hulk
sperm

Epigenetics and imprinting

This post assumes basic familiarity with epigenetics. The next paragraph
is a 1-paragraph summary, but I recommend reading
these
two
excellent posts on epigenetics. If you know what DNA methylation, DNMT1
and imprinting are, you can safely skip those posts. You might also just
do fine by using the AI-generated glossary.

The short explanation is that DNA methylation consists of small chemical
marks on DNA that change which proteins can bind well to it. Together
with other chemical marks on
histones, which are “spools”
that DNA is rolled around, these marks are how a cell knows that it is a
skin cell, rather than a neuron. Imprinting is a phenomenon where the
two sets of chromosomes in early mammal embryos behave differently
depending on if they were inherited from the father or the mother side.
Imprinting appears to be mostly due to differences in DNA methylation
and histone marks. DNA methylation and histone marks are maintained
across cell replications through an array of specific enzymes that both
add, maintain (DNMT enzymes in the case of methylation) and remove marks
(Tet enzymes in the case of DNA methylation). Approximate maintenance of
methylation is achieved by a balance between the actions of these
enzymes in ways that aren’t fully understood yet.

Why we need to know all imprinting regions and genes

All strong methods of germline engineering require turning cultured
cells that are not fully totipotent, back into totipotent or naive
pluripotent stem cells[1] that an embryo can grow from. We might ...
Illiteracy in Silicon Valley
by kilgoar — 2025-04-19T18:08:33.866Z

Sam Altman xeeted on Friday, "i think this is gonna be more like the renaissance than the industrial revolution."

The prediction, of course, is following to the common misconception of history in which the renaissance was a glorious rebirth of classical high civilization. The truth, really, is that Humanism was born out of Europe's greatest mass mortality where a half to two thirds of the population of the cities died off in repeated waves of bubonic plague. It was an absolutely terrible moment, and I think most people would greatly prefer to live even in the early industrialized era.

I'm not going to twist his words any further along this "well achsyhually," line, and I fully understand that he is vaguely trying to say that AI will liberate people into some fantasy renn-faire rather than put them into an Upton Sinclair industrial nightmare. But my true purpose is to treat Altman's ignorance as a symptom of a truly despicable trend in Silicon Valley and among programmers in general. Dunning-Kruger discourse is very overplayed and something I almost never invoke, but in the case of software engineers crossing into the humanities, it's far too applicable.

It is so tempting to take one's limited knowledge of History, apply some logical system, and make conclusions about trends of the past or the future. It's considered total common sense that humanity is on an upwards climb towards greater knowledge and understanding. However, the actual story is much more complicated, and the common narrative of progress, what's called Whiggish history, is not what happened to our humanity. In fact, at the beginning of the modern era, the 1500s, the changes in play provoked persecutions, religious wars, witchcraft crazes, heresy inquisitions, and the continuous growth of state power in a process which reached a crescendo in the legendary wars and mass murders of the 20th century. Indeed, if I presumed Altman had a college-level understanding of European history, I would take his xeet to be more of a threat than a promise of better things to come. But frankly, he doesn't have the slightest clue what he's talking about.

In any truly broad view of history we are living in the darkest of dark ages. The central values of this era are largely those of biological competition and survival, and to be quite frank, we are generally an ethically and morally misaligned species and behave, both...
Novel Idea Generation in LLMs: Judgment as Bottleneck
by Davey Morse — 2025-04-19T15:37:33.319Z

In the face of any hard problem—reversing climate change, curing cancer, or starting a great novel—modern LLMs can generate thousands of possible solutions relatively cheaply.

Most solutions from most prompts are bad: they’re not new relative to the state of the art, not feasible, or not significant enough.

But for every thousand ideas an LLM has about how to solve a problem, a few are likely to be good.

Now that LLMs are idea‑generation machines—able to produce ideas so cheaply, even if most are bad—the thing preventing us from waking up with promising climate‑change solutions in our inbox (or whichever problem you care about) comes down to an LLM’s ability to pick those few good ideas out of a thousand crap ones. In other words, I’d guess the rate‑limiting step isn’t generating good ideas but choosing, from among a thousand mostly random ones, the promising few.

At least, that was the bottleneck I perceived in my recent Oscillating Creativity Machine experiment. You give the machine a problem, like solving climate change, and it runs through ten rounds: it generates three possible solutions, picks the most interesting one, then generates three variations of that, and picks one again—ten times in all.

My hypothesis was that if it could pick well at each stage, even if the ideas generated sucked… well, that’s sort of what people do in the shower, in their sleep, or on walks when they’re stuck and then have eureka moments. We oscillate between chaos (possibilities) and order (pruning, picking one path). That’s roughly what I do when I come up with ideas I like.

But the Oscillating Creativity Machine didn’t work well in my tests. It didn’t end up picking a great idea—at least, not by my standards. And it didn’t come up with great ideas, which may have been a limiting factor too.

PhDs build their judgment over decades of real‑life experience: being exposed to experiments at the edge of their knowledge, then getting a sense of which new ideas or data actually help them solve the problems they set out to solve. LLMs, at least as I’ve prompted ChatGPT/Claude/Grok3 so far, don’t seem to have that judgment in the face of unsolved problems.

If LLMs could judge ideas well against unsolved challenges, we could have them generate millions of ideas for every problem that matters, then rate each and surface only the best. You could imagine this automated solution‑discovery proces...
Why Should I Assume CCP AGI is Worse Than USG AGI?
by Tomás B. — 2025-04-19T14:47:52.167Z

Though, given my doomerism, I think the natsec framing of the AGI race is likely wrongheaded, let me accept the Dario/Leopold/Altman frame that AGI will be aligned with the national interest of a great power. These people seem to take as an axiom that a USG AGI will be better in some way than a CCP AGI. Has anyone written a justification for this assumption?

I am neither an American citizen nor a Chinese citizen.

What would it mean for an AGI to be aligned with "Democracy," or "Confucianism," or "Marxism with Chinese characteristics," or "the American Constitution"? Contingent on a world where such an entity exists and is compatible with my existence, what would my life be like in a weird transhuman future as a non-citizen in each system? Why should I expect a USG AGI to be better than a CCP AGI? It does not seem to me super obvious that I should cheer for either party over the other. And if the intelligence of the governing class is of any relevance to the likelihood of a positive outcome, um, CCP seems to have USG beat hands down.
An Introduction to SAEs and their Variants for Mech Interp
by Adam Newgas — 2025-04-19T14:09:31.198Z

I aim to cover a lot of ground, but only briefly. Click through the links for more detailed explanations.

Several of the techniques here see general use outside of interpretability. My definitions/references lean towards how they specifically apply to Mechanistic Interpretability

Sparse Dictionary Learning describes any ML technique that aims to find a set of “atoms” that can be sparsely combined to represent a dataset. These techniques have become popular in Mechanistic Interpretability as a way of analysing the space of activations in a LLM. Most commonly this has been achieved using a particular kind of dictionary learning called Sparse Autoencoders.

Sparse Autoencoders

Sparse Autoencoders (SAEs) focus on reconstructing the activations of a single layer of the model. As the name suggests, SAEs are autoencoders models. They are trained by feeding in the activations from the original model, and optimising their ability to reconstruct the full activations from their latents. The training process also aims for sparsity of the SAE latent activations.

The motivation for SAEs is based on the observation that LLMs often seem to store sparse features in “superposition”, i.e. there are many linear directions in the activation space that correspond to important features, but they overlap considerably. SAEs try to reconstruct those original features and structure.

SAEs were introduced for metchanistic interpretability in Sparse Autoencoders Find Highly Interpretable Features in Language Models and used a 2 layer encoder/decoder model:

z=ReLU(Wenc(x−bdec)+benc)^x=Wdecz+bdec

Here x is the activations of the original model layer, z the SAE latents, and ^x the reconstruction of x by the SAE. z is much higher dimension than x, but is forced to be sparse, so it still acts as a bottleneck. The SAE[1] is trained with a reconstruction loss ∥x−^x∥22 and a sparsity loss λ∥z∥1.

Each entry in the SAE latent z corresponds to a vector direction from rows of the decoder matrix Wdec. These directions are analgous to the feature directions of superposition. For this reason, the latents of the SAE are often called “SAE Features”.

What is the Point of SAEs

SAEs, in principle, are an unsupervised way to find linear directions in the activation space of a model that stand for distinct "concepts".

Being unsuper...
Approaches to Mitigating AI Image-Generation Risks through Regulation
by scronkfinkle — 2025-04-19T13:54:54.726Z

1. The Risks (and Motivation)

On the 25th March 2025, OpenAI quietly unveiled their latest image generation capabilities, built into GPT-4o. This model was made available to all users with their $20/month ‘Plus’ membership, meaning over 11 million users have access to this model.

The latest model is significantly more capable than the previous, notably in its ability to produce photorealistic images, and images including text (e.g. receipts and road signs).

This is a sobering advancement in the state-of-the-art, and I now believe that we are no further than 12 months from the release of AI models which can produce images that cannot be detected by humans or computers.

This technology will have enormous ramifications across the entire planet. Some examples of the damaging consequences of this technology are listed below:

1.1 Legal System Risks

While I have been unable to find a figure specifically for images, a study by Nottingham Trent University in 2017 found that CCTV proved useful in 65% of criminal cases. The possibility to automatically generate images of crimes will lead to one of two outcomes. The first is that the legal system (absurdly) continues to allow photographic evidence in the courtroom, and hundreds, thousands, or more, individuals are wrongfully convicted. The second is that the legal system no longer deems photographic evidence as valid, and individuals are found not guilty despite photographic evidence existing of their crimes.

1.2 Political and Social Manipulation

Society will see sweeping change as a result of this technology. While people who are technology-literate will quickly begin to disregard all photographic evidence as unreliable, those who are not informed (specifically older generations), will become victim to constant disinformation campaigns. These will affect the electoral system in every country, and will be extremely damaging to many individuals in the public eye.

1.3 Privacy and Consent Violations

As we have already seen, pornographic and compromising content involving anybody in the public eye will become increasingly common. This may not be restricted to public figures, as low-cost technology of this kind may enable this content to be generated by anybody, of anybody, with only a few images of the victim.

2. Approaches to Risk Mitigation

It is clear, given the risks outlined previously, that sys...
AI Advances and Detection Strategy
by jefftk — 2025-04-19T11:40:07.264Z

Cross-posted from my NAO
Notebook.

This is an internal strategy note I wrote in November 2024 that I'm
making public with some light editing.

In my work at the NAO I've been thinking about what I expect to see as
LLMs continue to become more capable and get closer to where they can
significantly accelerate their own development. I think we may see very large advances in the power of
these systems over the
next few years.

I'd previously thought that the main impact of AI on the NAO was
through accelerating potential adversaries, and so shorter timelines
primarily meant more urgency: we needed to get a comprehensive
detection system in place quickly.

I now think, however, that this also means the best response involves
some reprioritization. Specifically, AI will likely speed up some
aspects of the creation of a detection system more than others, and so
to the extent that we expect rapid advances in AI we should prioritize
the work that we expect to bottleneck our future AI-accelerated work.

One way to plan for this is to imagine what would be the main
bottlenecks if we had a far larger staff. Imagine if each senior
person had AI support equivalent to all the smart junior people they
could effectively manage. Or even (but my argument doesn't depend on
this) AI systems that are as capable as today's experienced
researchers. I think if in a year or two we found ourselves in this
situation we would wish that:

We had collected a lot more data, because with a very large virtual
computational staff future-AI-assisted-NAO can wring insights out of
data far more efficiently than present-NAO.

We had started large-scale collection sooner, because, even if AI
accelerates sequencing's price decreases and we can collect a lot more
data in the future, it can't give us historical data.

We had a lot more partnerships for bringing in samples and data,
because these take real-human time to scale up.

While I don't think this is the only way things could play out, I
think it's likely enough that we should be taking these considerations
very seriously in our planning.

April 2025: since initially drafting this we've started an
ambitious effort to scale
up our pilot system.

Comment via: facebook, mastodon, bluesky
Emotional Theory for a Disorder Manual on How Not to Freeze Completely
by P. João — 2025-04-19T09:12:56.615Z

at Gwern's request
The ambulance screeched to a halt with the flair outside an apartment block whose hallway smelled like dry leaves, soggy cardboard, and… was that melted cheese? Best not to investigate. Within seconds, we found a woman—, lifeless, sprawled in the kitchen next to a pool of blood and a knife.

In the dining room, there she was: a five-year-old girl playing with dolls. No sobs. No tremors. I couldn’t tell if it was Buddhist calm or the prologue to a trauma that bills you for therapy later.

"Were you waiting for your mom?" I asked in my best cotton-stuffed teddy bear voice.
"Yes," she replied without blinking, like she’d rehearsed for the role of creepy-kid-in-a-horror-movie.

From my backpack, I pulled out the Kiko hat, the firefighter who carried toys instead of hoses. (El Chavo del 8, the show that in Brazil makes us laugh till we cry—from joy or second-hand embarrassment, like I do here).

I puffed out my cheeks with an invisible pill like a party balloon and held up the cap as if it were the Holy Grail of giggles.

There were no epic belly laughs, but she let out a tiny, sweet chuckle that made the kitchen's death-stench about 2% less suffocating with my cheeks.

My crewmates were already in the truck. The kind of guys who compartmentalize pain like it’s part of the job description.

“Get down here, come with us!”

“No, no, no, sergeant. I’m staying until the child protection officer arrives,” I said, firm, like I wasn’t just a firefighter clown but the damn ringmaster of this circus.
It was one of those rare moments where I let the anger through—the kind I’d always been afraid to use.

He choked on his own fury and spat,“We’ll talk later. Seems like you don’t want this job...”

I stayed.

Forty minutes later, an officer arrived—carrying more folders than dreams. We headed to the agency and spent three hours sitting on plastic chairs. The girl and I made an unspoken pact: either laugh at my goofy faces or die of boredom.

When her aunt finally arrived—so calm even the air got bored—I felt a brief relief. The kind that only lasts until the next call.

Back at the station, the sergeant cornered me and dropped his favorite sermon:
“You need to learn to detach, like the rest of us. Otherwise, you won’t last ten years in this job.”

And me thought:
I’d rather last two years being human than ten as a protocol-driven tin man.
But I didn’t have the ...
The System Didn’t, and Doesn’t Need to be This Way ~ Thomas Paine on Economic Justice
by James Stephen Brown — 2025-04-19T05:16:05.682Z
Link: https://nonzerosum.games/painejustice.html

In his 1797 pamphlet ‘Agrarian Justice’, Thomas Paine makes an impassioned case for land and wealth taxes based not on charity or even expedience, but on the injustice inherent in the ownership of private property. Paine shows that right from the inception of our modern societies, the option to tax wealth and land has been a justifiable, well-considered and practicable option.

“There could be no such thing as landed property originally. Man did not make the earth, and, though he had a natural right to occupy it, he had no right to locate as his property in perpetuity any part of it; neither did the Creator of the earth open a land-office, from whence the first title-deeds should issue.”” — Thomas Paine

Paine makes the important point that no human made this land. At the same time he acknowledges the non-zero-sum benefits of private ownership for the purposes of cultivation and personal security. Yet the fact remains that the means by which the initial proprietors acquired private land is necessarily not just, but is rather a result of luck (the first to be there), might (the first to gain dominance) or plain theft.

“In advocating the case of the persons thus dispossessed, it is a right, and not a charity, that I am pleading for.”” — Thomas Paine

While the current owners have paid for the land—making it unjust for the government to simply expropriate it—it stands that the people who don’t own private land have effectively been dispossessed of their “natural right” to land.

“Civilised” Poverty

This dispossession is set in high-relief when Paine observes that many “civilised” poor live in greater poverty than indigenous populations, whom he praises for having no extreme poverty despite living without the benefits afforded by modern civilisation.

“To understand what the state of society ought to be, it is necessary to have some idea of the natural and primitive state of man; such as it is at this day among the Indians of North America…” — Thomas Paine

Given that the cultivation of the land provides added value, Paine contends that it is therefore not justified that anyone, as part of this system, has worse living conditions than they would in a society without such added value.

“The thing, therefore, now to be done is to remedy the evils and preserve the benefits.” — Thomas Paine

Ground Rent

Paine proposes that those who own land should pay a Ground Rent of 3% of the land ...
SecureDrop review
by samuelshadrach — 2025-04-19T04:29:32.270Z
Link: https://samuelshadrach.com/?file=raw/english/./my_research/securedrop_review.md

This is a living document. Crosspost below may not be updated. Click link for the latest version.

2025-04-19

tldr

SecureDrop provides less source privacy than journalist privacy, even though it is the source not the journalist who is more likely to be punished for their actions.

SecureDrop opsec guide may be overkill for the median whistleblower but not good enough for the high-profile whistleblower, who is facing as much risk as a spy operating on foreign soil.

SecureDrop dev team in the US could consider working with dev teams in other countries, and citizen journalists in countries outside of US/Europe.

SecureDrop dev team could consider whether they're ready to accept political consequences of "anyone can violate anyone's consent and get all their info published", and make a clear statement to this end.

For diplomacy sake, I'll offer compliment before criticism.

Compliment

SecureDrop has clearly pushed forward on security-versus-usability pareto frontier for some subset of users.

SecureDrop has been used in many of the highest-profile leaks in the time period 2015-2025, as confirmed by Guardian and others.

It is rare for a small dev team to single-handedly shape human history in the way SecureDrop has.

I'm only criticising them because I think they're working on something important. Otherwise I might not be paying as much attention to it as I am.

Technical

SecureDrop does not offer the maximum security possible.

User privacy

Source privacy: No source airgap, source PGP encryption is optional, source using tails is optional, no redaction guide for the source.

Destination privacy: Imperfect destination airgap (plaintext visible in RAM before re-encryption with PGP)

Both: No education for source and destination on how to isolate from your current social circle and manage your psychology while doing so, no test run or practice time period for the source.

Example: I'm confident some of the sources have "how to whistleblow" in their google search results the same week they send the documents.

Example: I'm confident atleast some of these media orgs have journalists who tell their family and friends about their work, and someone in this circle will crack under police interrogation.

Distributed software and development

Codebase could be made even simpler (so multiple dev teams could manage it), dev team has arbitrarily decided to "encourage" some use case

...