Regulation and complexity of effects seem like another two big blockers.
Effects of genes are complex. Knowing a gene is involved in intelligence doesn’t tell us what it does and what other effects it has.
I wouldn’t accept any edits to my genome without the consequences being very well understood (or in a last-ditch effort to save my life). I’d predict severe mental illness would happen alongside substantial intelligence gains.
Source: research career as a computational cognitive neuroscientist.
I put this as a post- ASI technology, but that’s also a product of my relatively short timelines.
Yes, I think many in the field would share this viewpoint and that’s part of why we haven’t seen someone already attempt this.
I disagree for reasons I’ve shared in my post on “Black Box Biology”, but it’s worth reiterating my reasons here:
You don’t need to understand the causal mechanism of genes. Evolution has no clue what effects a gene is going to have, yet it can still optimize reproductive fitness. The entire field of machine learning works on black box optimization.
Most genetic variants (especially those that commonly vary among humans, which are the ones we would be targeting) have linear effects on a single trait. We don’t actually need to worry about gene-gene interactions that much.
To the degree plieotropy does exist and is a concern, you can optimize your edit targeting criteria according to multiple traits. For example, you could try to edit to reduce (or at the very least keep constant) the risk of schizophrenia and other mental disorders.
(As stated in the post), a delivery vector that doesn’t induce an adaptive immune response can be administered in multiple rounds, with a relatively small number of edits made each time, further decreasing the risk of large side-effects.
As far as regulation goes, we’ve already approved one CRISPR-based gene therapy in the US. I see no reason to expect that you couldn’t conduct a clinical trial to treat a polygenic brain disease like Alzheimers or treatment resistant depression. That’s why in my roadmap I proposed clinical trails for treating a fatal brain disorder as a first step before we tackle intelligence.
Your point 2 is my big hangup. If you mean each genetic variant affects each trait roughly linearly, sure. If you mean each genetic variant affects only one trait, I think that’s completely wrong. Most studies only address the affect on a single trait, but given the re-use of proteins for different roles, I fully expect multiple effects of genes on average. It seems like I’ve seen studies and papers on this, but it’s never been my area, so I don’t remember anything clearly.
Evolution succeeds by tinkering over many generations. It creates as many downsides as upsides. Who’s going to volunteer to be tinkered upon?
Actually, as soon as I pose the question that way, I realize that the answer is “lots of people” (as long as there’s a reason to think you’ll get more upside than downside, which limited theory will provide.)
However, there’s no way the FDA is going to approve tinkering. You’d have to do this outside of US jurisdiction.
I am not saying plieotropy doesn’t exist. I’m saying it’s not as big of a deal as most people in the field assume it is.
Take disease risk for example. Here’s a chart showing the genetic correlations between various conditions:
With a few notable exceptions, there is not very much correlation between different diseases.
And to the extent that plieotropy does exist, it mostly works in your favor. That’s why most of the boxes are yellowish instead of bluish. Editing or selecting embryos to reduce the risk of one disease usually results in a tiny reduction of others.
Evolution succeeds by tinkering over many generations. It creates as many downsides as upsides. Who’s going to volunteer to be tinkered upon?
Evolution cannot simultaneously consider data from millions of people when deciding which genetic variants to give someone. We can.
None of these proposals deal with novel genetic variants. Every target variant we would introduce is already present in tens of thousands of individuals and is known to not cause any monogenic disorder.
as long as there’s a reason to think you’ll get more upside than downside, which limited theory will provide
I’m not quite sure what you’re getting at here. Do you believe it’s impossible to make advantageous genetic tradeoffs? Or that there is no way to genetically alter organisms in a way that results in a net benefit?
However, there’s no way the FDA is going to approve tinkering. You’d have to do this outside of US jurisdiction.
The FDA routinely approves clinical trials to treat fatal diseases with no effective treatments. There are many lethal brain disorders that satisfy this requirement; Alzheimer’s, dementia, ALS, Parkinson’s and others.
Would the FDA approve a treatment to enhance intelligence? Probably not, unless US citizens were flying out of the country to get it. But if you can treat a polygenic brain disorder like Alzheimer’s with gene therapy, you can quite easily repurpose the platform to target intelligence by simply swapping the guide RNAs.
That matrix goes a long way in showing that there isn’t much correlation between diseases in the natural distribution. What is the reason to believe those correlations will remain low when you are making edits resulting in an extremely unlikely genome?
We’d edit the SNPs which have been found to causally influence the trait of interest in an additive manner. The genome would only become “extremely unlikely” if we made enough edits to push the predicted trait value to an extreme value—which you probably wouldn’t want to do for decreasing disease risk. E.g. if someone has +2 SD risk of developing Alzheimer’s, you might want to make enough edits to shift them to −2 SD, which isn’t particularly extreme.
You’re right that this is a risk with ambitious intelligence enhancement, where we’re actually interested in pushing somewhat outside the current human range (especially since we’d probably need to push the predicted trait value even further in order to get a particular effect size in adults) -- the simple additive model will break down at some point.
Also, due to linkage disequilibrium, there are things that could go wrong with creating “unnatural genomes” even within the current human range. E.g. if you have an SNP with alleles A and B, and there are mutations at nearby loci which are neutral conditional on having allele A and deleterious conditional on having allele B, those mutations will tend to accumulate in genomes which have allele A (due to linkage disequilibrium), while being purged from genomes with allele B. If allele B is better for the trait in question, we might choose it as an edit site in a person with allele A, which could be highly deleterious due to the linked mutations. (That said, I don’t think this situation of large-conditional-effect mutations is particularly likely a priori.)
I am not saying plieotropy doesn’t exist. I’m saying it’s not as big of a deal as most people in the field assume it is.
Molecular biologists should be in charge of AI research and regulation, because AGI is not as big of a deal as AI researchers who work in the field assume it is.
(I should clarify, I don’t see modification of polygenic traits just as a last ditch hail mary for solving AI alignment—even in a world where I knew AGI wasn’t going to happen for some reason, the benefits pretty clearly outweigh the risks. The case for moving quickly is reduced, though.)
Regulation and complexity of effects seem like another two big blockers.
Effects of genes are complex. Knowing a gene is involved in intelligence doesn’t tell us what it does and what other effects it has.
I wouldn’t accept any edits to my genome without the consequences being very well understood (or in a last-ditch effort to save my life). I’d predict severe mental illness would happen alongside substantial intelligence gains.
Source: research career as a computational cognitive neuroscientist.
I put this as a post- ASI technology, but that’s also a product of my relatively short timelines.
Yes, I think many in the field would share this viewpoint and that’s part of why we haven’t seen someone already attempt this.
I disagree for reasons I’ve shared in my post on “Black Box Biology”, but it’s worth reiterating my reasons here:
You don’t need to understand the causal mechanism of genes. Evolution has no clue what effects a gene is going to have, yet it can still optimize reproductive fitness. The entire field of machine learning works on black box optimization.
Most genetic variants (especially those that commonly vary among humans, which are the ones we would be targeting) have linear effects on a single trait. We don’t actually need to worry about gene-gene interactions that much.
To the degree plieotropy does exist and is a concern, you can optimize your edit targeting criteria according to multiple traits. For example, you could try to edit to reduce (or at the very least keep constant) the risk of schizophrenia and other mental disorders.
(As stated in the post), a delivery vector that doesn’t induce an adaptive immune response can be administered in multiple rounds, with a relatively small number of edits made each time, further decreasing the risk of large side-effects.
As far as regulation goes, we’ve already approved one CRISPR-based gene therapy in the US. I see no reason to expect that you couldn’t conduct a clinical trial to treat a polygenic brain disease like Alzheimers or treatment resistant depression. That’s why in my roadmap I proposed clinical trails for treating a fatal brain disorder as a first step before we tackle intelligence.
Your point 2 is my big hangup. If you mean each genetic variant affects each trait roughly linearly, sure. If you mean each genetic variant affects only one trait, I think that’s completely wrong. Most studies only address the affect on a single trait, but given the re-use of proteins for different roles, I fully expect multiple effects of genes on average. It seems like I’ve seen studies and papers on this, but it’s never been my area, so I don’t remember anything clearly.
Evolution succeeds by tinkering over many generations. It creates as many downsides as upsides. Who’s going to volunteer to be tinkered upon?
Actually, as soon as I pose the question that way, I realize that the answer is “lots of people” (as long as there’s a reason to think you’ll get more upside than downside, which limited theory will provide.)
However, there’s no way the FDA is going to approve tinkering. You’d have to do this outside of US jurisdiction.
I am not saying plieotropy doesn’t exist. I’m saying it’s not as big of a deal as most people in the field assume it is.
Take disease risk for example. Here’s a chart showing the genetic correlations between various conditions:
With a few notable exceptions, there is not very much correlation between different diseases.
And to the extent that plieotropy does exist, it mostly works in your favor. That’s why most of the boxes are yellowish instead of bluish. Editing or selecting embryos to reduce the risk of one disease usually results in a tiny reduction of others.
Evolution cannot simultaneously consider data from millions of people when deciding which genetic variants to give someone. We can.
None of these proposals deal with novel genetic variants. Every target variant we would introduce is already present in tens of thousands of individuals and is known to not cause any monogenic disorder.
I’m not quite sure what you’re getting at here. Do you believe it’s impossible to make advantageous genetic tradeoffs? Or that there is no way to genetically alter organisms in a way that results in a net benefit?
The FDA routinely approves clinical trials to treat fatal diseases with no effective treatments. There are many lethal brain disorders that satisfy this requirement; Alzheimer’s, dementia, ALS, Parkinson’s and others.
Would the FDA approve a treatment to enhance intelligence? Probably not, unless US citizens were flying out of the country to get it. But if you can treat a polygenic brain disorder like Alzheimer’s with gene therapy, you can quite easily repurpose the platform to target intelligence by simply swapping the guide RNAs.
This matrix closes the case in my book
That matrix goes a long way in showing that there isn’t much correlation between diseases in the natural distribution. What is the reason to believe those correlations will remain low when you are making edits resulting in an extremely unlikely genome?
We’d edit the SNPs which have been found to causally influence the trait of interest in an additive manner. The genome would only become “extremely unlikely” if we made enough edits to push the predicted trait value to an extreme value—which you probably wouldn’t want to do for decreasing disease risk. E.g. if someone has +2 SD risk of developing Alzheimer’s, you might want to make enough edits to shift them to −2 SD, which isn’t particularly extreme.
You’re right that this is a risk with ambitious intelligence enhancement, where we’re actually interested in pushing somewhat outside the current human range (especially since we’d probably need to push the predicted trait value even further in order to get a particular effect size in adults) -- the simple additive model will break down at some point.
Also, due to linkage disequilibrium, there are things that could go wrong with creating “unnatural genomes” even within the current human range. E.g. if you have an SNP with alleles A and B, and there are mutations at nearby loci which are neutral conditional on having allele A and deleterious conditional on having allele B, those mutations will tend to accumulate in genomes which have allele A (due to linkage disequilibrium), while being purged from genomes with allele B. If allele B is better for the trait in question, we might choose it as an edit site in a person with allele A, which could be highly deleterious due to the linked mutations. (That said, I don’t think this situation of large-conditional-effect mutations is particularly likely a priori.)
Molecular biologists should be in charge of AI research and regulation, because AGI is not as big of a deal as AI researchers who work in the field assume it is.
The stakes could hardly be more different—polygenic trait selection doesn’t get everyone killed if we get it slightly wrong.
(I should clarify, I don’t see modification of polygenic traits just as a last ditch hail mary for solving AI alignment—even in a world where I knew AGI wasn’t going to happen for some reason, the benefits pretty clearly outweigh the risks. The case for moving quickly is reduced, though.)