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PaperclipPerfector  8mo ago (text post)   29746 thread views

Culture War Roundup for the week of August 21, 2023

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The problem with this is that it assumes that problems have solutions. Easy mistake! But the strongest possible incentives have failed to stop aging, or find a way to increase IQ. No set of incentives will find a way to generate negative net entropy, or move faster than the speed of light. People can successfully build a bridge or fight a war, sure, but contemporary politics have solved most of these tractable problems.

But the strongest possible incentives have failed to stop aging, or find a way to increase IQ.

PGT-P, polygenic testing for diseases for pre-implantation embryos in IVF, is available right now. The exact same technology works for IQ, and could be used today if people weren't worried about legal/regulatory/PR pushback. 50% chance it already is being used secretly.

Also, even before that, we know how to increase IQ: use sperm/eggs from smarter parents. This isn't something most average parents would want, but it'd work. In a few decades, there'll probably be direct gene editing for something like 'your kid looks like you but has the intelligence-related-genes of someone much smarter', and even though valuing your child looking like you but not valuing them acting like you is quite confused, that'll probably sell. Black on the outside, jew on the inside.

I don't have great insight into this field but I think you overstate the science somewhat. A number of genes have been 'implicated in' intelligence but that is a long way off from the proof that inserting these genes into someone will make them intelligent. I believe there is evidence that genes can function differently in different circumstances/populations so it's not a trivial X makes Y scenario.

No, I'm not overstating it. Read this post by gwern or this post by genesmith for an overview of why embryo selection for intelligence works. Intelligence isn't caused by a few genes, but by thousands of genes that individually have a minuscule contribution but, when added up, cause >50% of existing variation in intelligence (note: i'm not claiming we understand each individual gene that's part of that, just that we've inferred that). (Note that this is in the modern environment - environmental effects have a much higher impact when malaria and starvation and infections from skin wounds are rampant, but they aren't in the modern US).

When parents have children, the DNA the child inherits from either parent is random. So we can say - okay, can we predict, just from the DNA of children, which child will be smarter? And polygenic scores indeed predict which child will be smarter.

So how big would the gain be? Using some code from Gwern's monster post on embryo selection for intelligence, I'd estimate that if both parents are of European ancestry and you have 10 euploid embryos to pick from, the gain would be about 5 IQ points.

There is significant room for this benefit to improve in the near future. If we simply gave intelligence tests to people who are already participating in existing biobanks, we could increase the IQ gain from embryo selection by about 70% or more. This would imply a gain of 8.5-13 IQ points. Administering these intelligence tests could be done for a few tens of millions of dollars (or perhaps less if you're clever about it).

I'm not 100% confident in these estimates, but they seem to be around the right order of magnitude. And ... that's a lot of IQ points. Even if we divide those estimates by ten, it's still well above anything that education reform can do.

Intelligence isn't caused by a few genes, but by thousands of genes that individually have a minuscule contribution but, when added up, cause >50% of existing variation in intelligence

I would bet good money that taking a genome, and then editing it until it had every gene which is correlated with higher intelligence, would not get you a baby that was even a single standard deviation above what you would naively predict based on the parents.

Consider a simple toy model, where

  1. Human intelligence is modulated by the production of a magical protein Inteliquotin (IQN), which causes brains to wrinkle.
  2. Human intelligence is a direct but nonlinear function of IQN concentration -- if IQN concentration is too low, it results in smooth brains (and thus lower intelligence), while if the concentration is too high, it interferes with metabolic processes in the neurons (and thus also results in lower intelligence). Let's say the average concentration is 1.0µg/mL.
  3. The optimal IQN concentration for inclusive fitness in the ancestral environment, and the average among the human population is 1.0µg/mL. However, the optimal concentration for intelligence specifically is 10% higher, at 1.1µg/mL (between those concentrations, improved fitness due to increased intelligence is more than offset to decreased fitness due to, let's say, "increased propensity for Oculoumbilical Tendency leading to elevated predation rates")
  4. The production of IQN is modulated by 1000 different genes IQN000 - IQN999, with the high-IQN variant of each gene occuring in 10% of the population, and where each gene independently causes IQN to increase by 0.01µg/mL.

If you have this scenario, each gene IQN000...IQN999 will explain about 0.1% of the variance in IQ, and yet using CRISPR to force just 5% more of the IQN genes to the "good" variant will lead to poorer outcomes than just leaving the system alone.

All that being said, you should be able to squeeze some results out of that technique. Just not multiple SD of improvement, at least not by doing the naive linear extrapolation thing.

I would bet good money that taking a genome, and then editing it until it had every gene which is correlated with higher intelligence, would not get you a baby that was even a single standard deviation above what you would naively predict based on the parents.

What do you mean by this bet? Actually waiting for gene edited baby to grow is slow, and illegal, and we're still nowhere near to being able to edit 10,000 genes of future child without breaking unintended genes .

You are entirely correct that linear model has its limits. Arguing that than it would break well before 1 SD, is... just wishful thinking. There's still a lot of low hanging fruit.

while if the concentration is too high, it interferes with metabolic processes in the neurons (and thus also results in lower intelligence).

Looks like in actual world, the tradeoff was "decreases libido, increases cuckoldry and other non-reproductive activity" rather than "decreases intelligence".

I am not arguing that you can't get a single standard deviation of gain using gene editing, and I am especially not arguing that you can't get there eventually using an iterative approach. I am arguing that you will get less than +1SD of gain (and, in fact, probably a reduction) in intelligence if you follow the specific procedure of

  1. Catalogue all of the different genes where different alleles are correlated with differences in the observed phenotypic trait of interest (in this case intelligence)
  2. Determine the "best" allele for every single gene, and edit the genome accordingly at all of those places.
  3. Hopefully have a 300-IQ super baby.

The specific thing I want to call out is that each of the alleles you've measured to be the "better" variant is the better variant in the context of the environment the measurements occurred in. If you change a bunch of them at once, though, you're going to end up in into a completely different region of genome space, where the genotype/phenotype correlations you found in the normal human distribution probably won't hold.

I don't know if you have any experience with training ML models. I imagine not, since most people don't. Still, if you do have such experience, you can read my point as "if you take some policy that has been somewhat optimized by gradient descent for a loss function which is different from, but correlated with, the one you care about, and calculate the local gradient according to the loss function you care about, and then you take a giant step in the direction of the gradient you calculated, you are going to end up with higher loss even according to the loss function you care about, because the loss landscape is not flat". Basically my point is "going far out of distribution probably won't help you, even if you choose the direction that is optimal in-distribution -- you need to iterate".

Actually waiting for gene edited baby to grow is slow, and illegal

Yep. And yet, I claim, necessary if you don't want to be limited to fairly small gains.

Arguing that than it would break well before 1 SD, is... just wishful thinking. There's still a lot of low hanging fruit.

Note that this is "below 1SD of gains beyond what you would expect from the parents, and in a single iteration". If you were to take e.g. Terry Tao's genome, and then identify 30 places where he has "low intelligence" variants of whatever gene, and then make a clone with only those genes edited, and a second clone with no gene edits, I would expect the CRISPR clone to be a bit smarter than the unaltered clone, and many SD smarter than the average person. And, of course, at the extreme, if you take a zygote from two average-IQ parents, and replace its genome with Tao's genome then the resulting child would probably be more than 1SD smarter than you'd expect based on the IQs of the parents, because in that case you're choosing a known place in genome space to jump to, instead of choosing a direction and jumping really far in that direction from a mediocre place.

Maybe technical arguments don't belong in the CW thread, but people assuming that the loss landscape is basically a single smooth basin is a pet peeve of mine.

Hopefully have a 300-IQ super baby.

I don't current state of art, but I think setting all genes to "high IQ allele" would have linear projection for IQ well past 300. So getting 300 IQ would need to avoid setting some alleles.

if you take some policy that has been somewhat optimized by gradient descent for a loss function which

I have some experience with gradient descent methods, thought, not with ML. I challenge the premise "somewhat optimized", we are currently living in dysgenic age. If we were talking about making Borzoi dogs run faster, I'd have agreed.

If you were to take e.g. Terry Tao's genome, and then identify 30 places where he has "low intelligence" variants of whatever gene, and then make a clone with only those genes edited, and a second clone with no gene edits, I would expect the CRISPR clone to be a bit smarter than the unaltered clone,

Alternatively, we could just skip detection on which alleles have low IQ and just eliminate very rare alleles, which are much more likely to be deleterious (e.g. replace allele with frequency below given threshold with its most similar allele with frequency above threshold) without studying any IQ.

Maybe technical arguments don't belong in the CW thread,

Well, people on this forum don't discuss genetics in detail at all.

but people assuming that the loss landscape is basically a single smooth basin is a pet peeve of mine.

It's a basin in some places until we travel to a mountain ridge. Since we are decades away from even trying "set all genes to specific allele" - even for model organisms - very few people discuss it.

In your hypothetical bet, how would result "IQ as intended, but baby brain too large for pregnancy to be delivered naturally" count?

I challenge the premise "somewhat optimized", we are currently living in dysgenic age.

The optimization happened in the ancestral environment, not the last couple hundred years. Current environment is probably mildly dysgenic but the effect is going to be tiny because the current environment just hasn't been around for very long.

Alternatively, we could just skip detection on which alleles have low IQ and just eliminate very rare alleles, which are much more likely to be deleterious (e.g. replace allele with frequency below given threshold with its most similar allele with frequency above threshold) without studying any IQ.

I expect this would help a bit, just would be surprised if the effect size was actually anywhere near +1SD.

In your hypothetical bet, how would result "IQ as intended, but baby brain too large for pregnancy to be delivered naturally" count?

If the baby is healthy otherwise, that counts just fine.

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