kman

Karma: 1,237

kman Feb 23, 2025, 3:28 AM
4 points
0
in reply to: LGS’s comment on: How to Make Superbabies
I don’t quite understand your numbers in the OP but it feels like you’re inflating them substantially. Is the full calculation somewhere?
Not quite sure which numbers you’re referring to, but if it’s the assumed SNP heritability, see the below quote of mine from another comment talking about missing heritability for IQ:
The SNP heritability estimates for IQ of (h^2 = ~0.2) are primarily based on a low quality test that has a test-retest reliability of 0.6, compared to ~0.9 for a gold-standard IQ test. So a simple calculation to adjust for this gets you a predicted SNP heritability of 0.2 * (0.9 / 0.6)^2 = 0.45 for a gold standard IQ test, which matches the SNP heritability of height. As for the rest of the missing heritability: variants with frequency less than 1% aren’t accounted for by the SNP heritability estimate, and they might contribute a decent bit if there are lots of them and their effects sizes are larger.
The h^2 = 0.19 estimate from this GWAS should be fairly robust to stratification, because of how the LDSC estimator works. (To back this up: a recent study that actually ran a small GWAS on siblings, based on the same cognitive test, also found h^2 = 0.19 for direct effects.)

kman Feb 23, 2025, 2:55 AM
9 points
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in reply to: LGS’s comment on: How to Make Superbabies
For cognitive performance, the ratio was better, but it’s not 0.824, it’s ${0.824}^{2} = 0.68$ .
That’s variance explained. I was talking about effect size attenuation, which is what we care about for editing.
I checked supplementary table 10, and it says that the “direct-population ratio” is 0.656, not 0.824. So quite possibly the right value is ${0.656}^{2} = 0.43$ even for cognitive performance.
Supplementary table 10 is looking at direct and indirect effects of the EA PGI on other phenotypes. The results for the Cog Perf PGI are in supplementary table 13.

kman Feb 23, 2025, 1:38 AM
1 point
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in reply to: LWLW’s comment on: How to Make Superbabies
sadism and wills to power are baked into almost every human mind (with the exception of outliers of course). force multiplying those instincts is much worse than an AI which simply decides to repurpose the atoms in a human for something else.
I don’t think the result of intelligence enhancement would be “multiplying those instincts” for the vast majority of people; humans don’t seem to end up more sadistic as they get smarter and have more options.
i would argue that everyone dying is actually a pretty great ending compared to hyperexistential risks. it is effectively +inf relative utility.
I’m curious what value you assign to the ratio [U(paperclipped) - U(worst future)] / [U(best future) - U(paperclipped)]? It can’t be literally infinity unless U(paperclipped) = U(best future).
with humans you’d need the will and capability to engineer in at least +5sd empathy and −10sd sadism into every superbaby.
So your model is that we need to eradicate any last trace of sadism before superbabies is a good idea?

kman Feb 23, 2025, 12:20 AM
9 points
2
in reply to: LGS’s comment on: How to Make Superbabies
The IQ GWAS we used was based on only individuals of European ancestry, and ancestry principal components were included as covariates as is typical for GWAS. Non-causal associations from subtler stratification is still a potential concern, but I don’t believe it’s a terribly large concern. The largest educational attainment GWAS did a comparison of population and direct effects for a “cognitive performance” PGI and found that predicted direct (between sibling) effects were only attenuated by a factor of 0.824 compared to predicted population level effects. If anything I’d expect their PGI to be worse in this regard, since it included variants with less stringent statistical power cutoffs (so I’d guess it’s more likely that non-causal associations would sneak in, compared to the GWAS we used).

kman Feb 22, 2025, 1:56 AM
7 points
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in reply to: LGS’s comment on: How to Make Superbabies
Plain GWAS, since there aren’t any large sibling GWASes. What’s the basis for the estimates being much lower and how would we properly adjust for them?

kman Feb 21, 2025, 4:48 PM
6 points
5
in reply to: momom2’s comment on: How to Make Superbabies
I mostly think we need smarter people to have a shot at aligning ASI, and I’m not overwhelmingly confident ASI is coming within 20 years, so I think it makes sense for someone to have the ball on this.

kman Feb 21, 2025, 4:13 PM
5 points
2
in reply to: RichardJActon’s comment on: How to Make Superbabies
I’m curious about the basis on which you are assigning a probability of causality without a method like mendelian randomisation, or something that tries to assign a probability of an effect based on interpreting the biology like a coding of the output of something like SnpEff to an approximate probability of effect.
Using finemapping. I.e. assuming a model where nonzero additive effects are sparsely distributed among SNPs, you can do Bayesian math to infer how probable each SNP is to have a nonzero effect and its expected effect size conditional on observed GWAS results. Things like SnpEff can further help by giving you a better prior.

kman Feb 21, 2025, 4:07 PM
7 points
1
in reply to: lumire’s comment on: How to Make Superbabies
The SNP heritability estimates for IQ of (h^2 = ~0.2) are primarily based on a low quality test that has a test-retest reliability of 0.6, compared to ~0.9 for a gold-standard IQ test. So a simple calculation to adjust for this gets you a predicted SNP heritability of ~~0.2 * (0.9 / 0.6)^2 = 0.45~~ 0.2 * (0.9 / 0.6) = 0.30 for a gold standard IQ test, which matches the SNP heritability of height. As for the rest of the missing heritability: variants with frequency less than 1% aren’t accounted for by the SNP heritability estimate, and they might contribute a decent bit if there are lots of them and their effects sizes are larger.
EDIT: the original adjustment for test-retest reliability was incorrect: the correlations shouldn’t be squared.

kman Feb 21, 2025, 2:45 AM
4 points
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in reply to: RichardJActon’s comment on: How to Make Superbabies
Could you expand on what sense you have ‘taken this into account’ in your models? What are you expecting to achieve by editing non-causal SNPs?
If we have a SNP that we’re 30% sure is causal, we expect to get 30% of its effect conditional on it being causal. Modulo any weird interaction stuff from rare haplotypes, which is a potential concern with this approach.
The first paper I linked is about epistasic effects on the additivity of a QTLs for quantitative trait, specifically heading date in rice, so this is evidence for this sort of effect on such a trait.
I didn’t read your first comment carefully enough; I’ll take a look at this.

kman Feb 21, 2025, 2:42 AM
8 points
7
in reply to: jimrandomh’s comment on: How to Make Superbabies
I definitely don’t expect additivity holds out to like +20 SDs. We’d be aiming for more like +7 SDs.

kman Feb 21, 2025, 12:13 AM
1 point
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in reply to: Noosphere89’s comment on: How to Make Superbabies
I think I’m at <10% that non-enhanced humans will be able to align ASI in time, and if I condition on them succeeding somehow I don’t think it’s because they got AIs to do it for them. Like maybe you can automate some lower level things that might be useful (e.g. specific interpretability experiments), but at the end of the day someone has to understand in detail how the outcome is being steered or they’re NGMI. Not sure exactly what you mean by “automating AI safety”, but I think stronger forms of the idea are incoherent (e.g. “we’ll just get AI X to figure it all out for us” has the problem of requiring X to be aligned in the first place).

kman Feb 20, 2025, 11:50 PM
3 points
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in reply to: Noosphere89’s comment on: How to Make Superbabies
Much less impactful than automating AI safety.
I don’t think this will work.

kman Feb 20, 2025, 11:23 PM
2 points
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in reply to: LWLW’s comment on: How to Make Superbabies
So you think that, for >95% of currently living humans, the implementation of their CEV would constitute an S-risk in the sense of being worse than extinction in expectation? This is not at all obvious to me; in what way do you expect their CEVs to prefer net suffering?

kman Feb 20, 2025, 11:11 PM
1 point
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in reply to: Noosphere89’s comment on: How to Make Superbabies
Because they might consider that other problems are more worth their time, since smartness changes change their values little.
I mean if they care about solving problems at all, and we are in fact correct about AGI ruin, then they should predictably come to view it as the most important problem and start to work on it?
Are you imagining they’re super myopic or lazy and just want to think about math puzzles or something? If so, my reply is that even if some of them ended up like that, I’d be surprised if they all ended up like that, and if so that would be a failure of the enhancement. The aim isn’t to create people who we will then carefully persuade to work on the problem, the aim is for some of them to be smart + caring + wise enough to see the situation we’re in and decide for themselves to take it on.

kman Feb 20, 2025, 10:44 PM
1 point
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in reply to: Noosphere89’s comment on: How to Make Superbabies
My interpretation is that you’re 99% of the way there in terms of work required if you start out with humans rather than creating a de novo mind, even if many/most humans currently or historically are not “aligned”. Like, you don’t need very many bits of information to end up with a nice “aligned” human. E.g. maybe you lightly select their genome for prosociality + niceness/altruism + wisdom, and treat them nicely while they’re growing up, and that suffices for the majority of them.

kman Feb 20, 2025, 10:36 PM
2 points
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in reply to: Amy Johnson’s comment on: How to Make Superbabies
Sure, sounds hard though.

kman Feb 20, 2025, 10:33 PM
3 points
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in reply to: RichardJActon’s comment on: How to Make Superbabies
The SNP itself is (usually) not causal Genotyping arrays select SNPs the genotype of which is correlated with a region around the SNP, they are said to be in linkage with this region as this region tends to be inherited together when recombination happens in meiosis. This is a matter of degree and linkage scores allow thresholds to be set for how indicative a SNP is about the genotype a given region.
This is taken into account by our models, and is why we see such large gains in editing power from increasing data set sizes: we’re better able to find the causal SNPs. Our editing strategy assumes that we’re largely hitting non-causal SNPs.
In practice epistatic interactions between QTLs matter for effects sizes and you cannot naively add up the effect sizes of all the QTLs for a trait and expect the result to reflect the real effect size, even if >50% effect are additive.
I’m not aware of any evidence for substantial effects of this sort on quantitative traits such as height. We’re also adding up expected effects, and as long as those estimates are unbiased the errors should cancel out as you do enough edits.
One thing we’re worried about is cases where the haplotypes have the small additive effects rather than individual SNPs, and you get an unpredictable (potentially deleterious) effect if you edit to a rare haplotype even if all SNPs involved are common. Are you aware of any evidence suggesting this would be a problem?

kman Feb 20, 2025, 7:27 PM
2 points
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in reply to: TsviBT’s comment on: How to Make Superbabies
This paper found that the heritability of most traits is ~entirely additive, supposedly including IQ according to whatever reference I followed to the paper, though I couldn’t actually find where in the paper it said/implied that.

kman Feb 20, 2025, 6:58 PM
2 points
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in reply to: kman’s comment on: How to Make Superbabies
IIUC that R = 0.55 number was just the raw correlation between the beta values of the sibling and population GWASes
Actually I don’t think this is correct, it accounted for sampling error somehow. I’ll need to look into this deeper.

kman Feb 20, 2025, 6:51 PM
4 points
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in reply to: kman’s comment on: How to Make Superbabies
Subtle population stratification not accounted for by the original GWAS could still be an issue, though I don’t expect this would inflate the effects very much. If we had access to raw data we could take into account small correlations between distant variants during finemapping, which would automatically handle assortative mating and stratification.