I’m a software developer by training with an interest in genetics. I am currently doing independent research on gene therapy with an emphasis on intelligence enhancement.
GeneSmith
Karma: 3,100
To the extent that transposon sequences affect traits and risks but can’t be measured we should expect that be reflected in “missing heritability”.
You may very well be right that highly repetitive sequences like transposons do affect traits, but that’s accounted for.
Thanks a lot for the comment. I’ll amend the post with some of this information in the next week. If your numbers are correct (and I have no current reason to doubt them, that substantially increases my estimate of the effectiveness of whole genome embryo sequencing.
I’ve been meaning to write a whole post about the different screening companies but a combination of little time due to starting a new company and a lack of clear data have preventing me from doing so thus far. With this information I might reconsider.
One more thing I’d like to ask at some point is whether you’re going to publish the AUCs of all the predictors in your panel within some reference population. That would be extremely helpful for patients trying to compare Orchid vs Genomic Prediction or any other company.
I am just now learning the origin of the quokka meme. The first and only time I ever saw the reference was with no explanation when someone posted this meme on Twitter
Who do you recommend asking to be a reader?
That’s a difficult question. I always tell readers that the number one thing I’m interested in is where they got bored and stopped reading. I ask them to be brutally honest and not feel like they need to keep reading to flatter my ego or because they are afraid of being harsh on me.
If they aren’t interested in the topic in the first place it’s harder. You need to be able to at least find an audience that is interested in sitting down to read it. Can you like join a hobbyist club for this stuff, or find a subreddit for it?
Here’s a kind of galaxy-brained idea that might just work for finding your crowd:
Go onto reddit and find the subreddit community closest to the thing you’re interested in/writing about
Go to https://subredditstats.com and enter the name of that subreddit to see which communities it has the most overlap with.
Go to meetup.com and see if you can find a local group dedicated to one of those related topics (or better yet, the topic itself)
Go to the meetup, pitch your thing, and see if people are into it. Maybe just TALK about what you’ve written first and if people seem interested offer to send them what you’ve written.
If you decide to actually give the above a shot, tell me how it goes. I’d be very interested to hear whether my idea works.
https://www.nature.com/articles/s41598-020-69927-7
This is one of the better papers I know of examining sibling validation. To quote from the article:
Sibling comparisons are a powerful method with which to validate genomic prediction in humans. Siblings (i.e., children who share the same mother and father) have typically experienced similar environments while growing up: family social status, exposure to toxins, diet, climate, etc. all tend to be similar3,4. Furthermore, siblings are concordant for ancestry and display negligible differences in population structure.
If environmental conditions in a specific region, such as, e.g., Northern England, affect disease risk, the predictor trained on UK data might assign nonzero effect sizes to SNPs associated with ancestries found in that region—i.e., the predictor learns to use population structure correlated to environmental conditions. These specific SNPs are correlated to disease risk for environmental reasons, but might not have any connection to genetic mechanisms related to the disease. They likely have little power to differentiate between siblings, who experienced similar family conditions and have have identical ancestry.
It is also possible that some SNP variants affect nurture (the way that parents raise their children). These SNPs could affect the child phenotype via an environmental mechanism under parental control, not a biochemical pathway within the child. This is sometimes referred to as a genetic nurture effect9,10,11,12,13. Note, siblings raised together would both be affected by parental genetic nurture variants, so these effects are weakened in family designs.
Sibling comparisons reduce the impact of factors such as those described above. We expect some reduction in power when predictors trained in a population of non-sibling individuals are tested among sibs. Sibling validation likely yields a better estimate of truly causal genetic effects. A more complicated measure of familial relatedness might lead to even better results14, but we restrict our analyses here to siblings.
There’s more in the paper if you care to take a look.
A relevant tweet from Nate Silver on the methodology used to conduct the survey:
This is not a scientific way to do a survey. The biggest issue is that it involved personalized outreach based on a totally arbitrary set of criteria. That’s a huge no-no. It also, by design, had very few biosafety or biosecurity experts.
The tweet has some screenshots of relevant parts of the paper
I’ve landed three jobs thanks to my writing, which is a bit strange to say because I still feel like I have substantial room for improvement. But that’s still a pretty good track record, so I’ll tell you what has worked for me:
Ask people to read your stuff and tell you at what point they get bored or want to stop. Tell them to be brutally honest. The most important part of writing is keeping your audience’s attention, and many writers make no effort to do this.
Write about something that’s actually important, and that interests you. I’ve done a large amount of high-quality writing about interest rates, banking and crypto. So far as I can tell it was a complete waste because no one cared. 80% of the battle is just picking the right topic.
Put the most imortant ideas at the start of whatever you’re writing. The drop-off among readers (even on a site like LessWrong) is shockingly high. I received almost 400 upvotes on my post about adult intelligence enhancement, and only four people sent me a DM in response to my request they do so that I placed at the end of the appendix.
Write the narrative of a story in the evening and then correct language and facts in the morning. My best, most productive narrative writing often occurs late at night, but when I re-read it in the morning it sounds sloppy and full of mistakes. However, I nearly always need to start with the sloppy, emotional version because good narrative writing is the one thing I can’t do well when my brain is functioning at full capacity.
Don’t be afraid to start a new draft of a post if you feel like you haven’t gotten the narrative right.
This reminds me a bit of my own hiring process. I wanted to work for a company doing polygenic embryo screening, but I didn’t fit any of the positions they were hiring for on their websites, and when I did apply my applications were ignored.
One day Scott Alexander posted “Welcome Polygenically Screened Babies”, profiling the first child to be born using those screening methods. I left a comment doing a long cost-effectiveness analysis of the technology, and it just so happened that the CEO of one of the companies read it and asked me if I’d like to collaborate with them.
The collaboration went well and they offered me a full-time position a month later.
All because a comment I left on a blog.
Gene editing can fix major mutations, to nudge IQ back up to normal levels, but we don’t know of any single genes that can boost IQ above the normal range
This is not true. We know of enough IQ variants TODAY to raise it by about 30 points in embryos (and probably much less in adults). But we could fix that by simply collecting more data from people who have already been genotyped.
None of them individually have a huge effect, but that doesn’t matter much. It just means you need to perform more edits.
If we want safe AI, we have to slow AI development.
I agree this would help a lot.
EDIT: added a graph
None of the stuff that you suggested has worked for any animal.
Has anyone done 2500 edits in the brain cells of an animal? No. The graphs are meant to illustrate the potential of editing to affect IQ given a certain set of assumptions. I think there are still significant barriers that must be overcome. But like… the trend here is pretty obvious. Look at how much editors have improved in just the last 5 years. Look at how much better our predictors have gotten. It’s fairly clear where we are headed.
Also, to say that none of this stuff has been done in animals seems a bit misleading. Here’s a paper where the authors were able to make a desired edit in 60% of mouse brain cells. Granted, they were using AAVs, but for some oligogenic conditions that may be sufficient; you can pack a single AAV with a plasmid holding DNA sufficient to make sgRNA for 31 loci using base editors. There are several conditions for which 30 edits would be sufficient to result in a >50% reduction in disease risk even after taking into account uncertanties about which allele is causal.
Granted, if we can’t improve editing efficiency in neurons to above 5% then the effect will be significantly reduced. I guess I am fairly optimistic on this front: if an allele is having an effect in brains, it seems reasonable to assume that some portion of the time it will not be methylated or wrapped around a histone, and thus be amenable to editing.
Regarding lipid nanoparticles as a delivery vehicle for editors: Verve-101 is a clinical trial underway right now evaluating safety and efficacy of lipid nanoparticles with a base editor to target PCSK9 mutations causing familial hypercholesterolemia.
There are other links in the post such as one showing transcytosis of BBB endothelial cells using angiopep conjugated LNPs. And here’s a study showing about 50% transfection efficiency of LNPs to brain cells following intracranial injection in mice.
it’s technically challenging if not impossible
Technically challenging? Yes.
Impossible?
Obviously not. You can get payloads into the brain. You can make edits in cells. And though there are issues with editing efficiency and delivery, both continue to improve every year. Eventually we will be able to do this.
if we want to achieve a true revolution in cognition, we need to target brain development not already developed brain!
If your contention is that it is easier to get a large effect by editing embryos vs the adult brain, I would of course agree! But consider all the conditions that are modulated by the timing and level of protein expression. It would be quite surprising to me if intelligence were not to modulated in a similar manner.
Furthermore, given what is happening in AI right now, we probably don’t have 25 years left for the technology for embryo editing to mature and for the children born with its benefits to grow up.
Imagine a monkey thinking of enhancing its abilities by injecting virus in its brain—will it ever reach a human level cognition? Sounds laughable. Who cares about +5 points to IQ
I have doubts we can enhance chimpanzee intelligence. We don’t have enough chimpanzees or enough intelligence phenotypes to create GWAS for chimp intelligence (or any other mental trait for that matter).
We could try porting human predictors but well… we already see substantial dropoff in variance explained when predictors are ported from one genetic ancestry group to another. Imagine how large the dropoff would be between species.
Granted, a lot of the dropoff seems to be due to differences in allele frequencies and LD structure. So maybe there’s some chance that a decent percentage of the variants would cause similar effects across species. But my current guess is few of the variants will have effects in both species.
Also, if I expected +5 IQ points to be the ceiling of in-vivo editing I wouldn’t care about this either. I do not expect that to be the ceiling, which is reflected in some of the later graphs in the post.
For >40 years, way before the discovery of CRISPRs and base editors, we’ve been successfully genetically engineering mice, but not other species. Why only mice? Because we can culture mouse embryonic stem cells that can give rise to complete animals. We did not understand why mouse cells were so developmentally potent, and why this didn’t work for other species. Now we do (I’m the last author): Highly cooperative chimeric super-SOX induces naive pluripotency across species—ScienceDirect
I’ve spent the better part of the afternoon reading and trying to understand this paper.
First, it’s worth saying just how impressive this work is. The improvement of success rates over existing embryogenesis techniques like SCNT. I have a few questions I wasn’t able to find answers to in the paper:
Do the rates of full-term and adult survival rates in iPSC mice match that which could be achieved by normal IVF, or do they indicate that there is still some suboptimality in culturing of tetraploid aggregated iPSC embryos? I’m not familiar with the normal rates of survival for mice so I wasn’t able to tell from the graph whether there is still room for improvement.
How epigenetically different are embryos produced with Sox2-17 compared to those produced through the normal IVF process?
If this process or an improved one in the future were capable of inducing embryo-viable iPSC’s, would you be able to tell this was the case in humans with the current data available? If not, what data are you missing? I’m particularly wondering about whether you feel that there is sufficient data available regarding the epigenetic state of normal embryonic cells at the blastocyst stage.
When you engineer stem cells rather than adult animals, all of those concerns you listed are gone: low efficiency, off-target mutations, delivery, etc. Pluripotent stem cells are immortal and clonogenic, which means that even if you get 1 in 1000 cells with correct edits and no off-target mutations, you can expand it indefinitely, verify by sequencing, introduce more edits, and create as many animals as you want. The pluripotent stem cells can either be derived from the embryos or induced artificially from skin or blood cells. The engineered pluripotent stem cells can either be used directly to create embryos or can be used to derive sperm and eggs; both ways work well for mice.
You are of course correct about everything here. And if we had unlimited time I think the germline editing approach would be better. But AGI appears to be getting quite near. If we haven’t alignment by the point that AI can recursively self-improve, then I think this technology becomes pretty much irrelevant. Meat-based brains, even genetically enhanced ones, are going to be irrelevant in a post-AGI world.
One would need to start with animals. I propose starting with rats, which are a great model of cognitive studies
How exactly do you propose to do this given we don’t have cognitive ability GWASes for rats, don’t have a feasible technique for getting them without hundreds of thousands of phenotypes, and given the poor track record of candidate gene studies in establishing causal variants?
I keep finding examples of decision making that can be explained by shard theory. In particular, here’s an example I read in ACX today about how depressed people make decisions that keep them depressed, which sounds an awful lot like “shards guiding behavior in ways that tend to lead to their own future self-reinforcement”:
Millgram et al (2015) find that depressed people prefer to listen to sad rather than happy music. This matches personal experience; when I’m feeling down, I also prefer sad music. But why? Try setting aside all your internal human knowledge: wouldn’t it make more sense for sad people to listen to happy music, to cheer themselves up?
A later study asks depressed people why they do this. They say that sad music makes them feel better, because it’s more “relaxing” than happy music. They’re wrong. Other studies have shown that listening to sad music makes depressed people feel worse, just like you’d expect. And listening to happy music makes them feel better; they just won’t do it.
Scott’s preferred explanation is one of a kind of “mood setpoint” which the depressed individual’s actions are trying to reach:
Depression is often precipitated by some psychosocial event (like loss of a job, or the death of a loved one). It’s natural to feel sad for a little while after this. But instead of correctly activating regulatory processes to get mood back to normal, the body accepts the new level as its new set point, and tries to defend it.
By “defend it”, I mean that healthy people have a variety of mechanisms to stop being sad and get their mood back to a normal level. In depression, the patient appears to fight very hard to prevent mood getting back to a normal level. They stay in a dark room and avoid their friends. They even deliberately listen to sad music!
Self-reinforcing “depression shards” are obviously a mechanism through which depressive states can be maintained. But then the question becomes why are some people more vulnerable to this kind of depression homeostasis than others?
There’s certainly a genetic component, but given how polygenic depression risk is (>30k variants involved) the mechanisms are likely multi-causal.
I think it’s plausible we could go higher but I’m fairly certain the linear model will break down at some point. I don’t know exactly where , but somewhere above the current human range is a good guess.
You’ll likely need a “validation generation” to go beyond that, meaning a generation or very high IQ people who can study themselves and each other to better understand how real intelligence has deviated from the linear model at high IQ ranges.
The reason they have lower IQs than humans can be explained entirely by neuron count.
Not true. Humans have an inbuilt propensity for language in a way that gorillas and other non-human primates don’t. There are other examples like this.
In Lex Fridman’s interview with Eliezer Yudkowsky, Eliezer presents no compelling path forward — and paints the future as almost non-existent.
It’s worth pointing out that Eliezer’s views on the relative hopelessness of the situation do not reflect those of the rest of the field. Nearly everyone else outside of MIRI is more optimistic than he is (though that is of course no guarantee he is wrong).
As an interested observer who has followed the field from a distance for about 6 years at this point, I don’t think there has ever been a more interesting time with more things going on than now. When I talk to some of my friends that work in the field, many of their agendas sound kind of obvious to me, which is IMO an indication that there’s a lot of low-hanging fruit in the field. I don’t think you have to be a supergenius to make progress (unless perhaps you’re working on agent foundations).
• The probability of doom given the development of AGI, + the probability of solving aging given AGI, nearly equals 1.
I’m not sure I understand what this means. Do you mean the “and” instead of “+”? Otherwise this statement is a little vague.
If you consider solving aging a high priority and are concerned that delays of AI might delay such a solution, here are a few things to cosider:
Probably over a hundred billion people have died building the civilization we live in today. It would be pretty disrespectful to their legacy if we threw all that away at the last minute just because we couldn’t wait 20 more years to build a machine god we could actually control. Not to mention all the people who will live in the future if we get this thing right. In the grand scheme of the cosmos, one or two generations is nothing.
If you care deeply about this, you might consider working on cryonics both to make it cheaper for everyone and to increase the odds of personality and memory recovery following the revival process.
I live in Scandinavia and see no major (except for maybe EA dk?) political movements addressing these issues. I’m eager to make an impact but feel unsure about how to do so effectively without dedicating my entire life to AI risk.
One potential answer here is “earn to give”. If you have a chance to enter a lucrative career you can use your earnings from that career to help fund work done by others.
If that’s not an option or doesn’t sound like something you’d enjoy, perhaps you could move? There are programs like SERI MATS you could attempt to enroll in if you’re a newcomer to the field of AI safety but have a relevant background in math or computer science (or are willing to teach yourself before the program begins).
A bad enough error in a regulatory region could cause a protein to stop being expressed. But the insertion or deletion of a single base pair is not nearly as devastating.
Let me explain by talking through how a promoter works.
Promoters sit upstream of a region of the genome that codes for a protein. They generally serve as a binding site for a very important enzyme called RNA polymerase, whose job it is to transcribe DNA into mRNA, which can then be exported from the nucleus and turned into proteins.
You can delete a letter from a promoter and RNA polymerase will still be able to bind. The “binding affinity” (meaning the strength of the bond) will be affected by this deletion, but except in rare circumstances it will still work.
You can see this reflected in the distribution of insertion and deletions throughout the genome; there’s not that many in coding regions, but there are tons in non-coding regions (on the order of 3-5 million).
You know, I actually looked into this at one point.
At the time I didn’t find any obvious reason why it wouldn’t work. But I didn’t spend that much time digging into the details, so my prior is it will be hard for some reason I haven’t discovered yet.
If you could actually find a way to present aribtrary “self-antigens” to T and B cells during the development phase within the thymus, that would be an incredibly powerful technology. It seems plausible to me that we could potentially cure a large percentage of autoimmune conditions with that technology, provided we knew which epitopes were triggering a particular immune response. But I know much less about this area than about gene editing, so it’s entirely plausible I’m wrong.
There’s already a few therapies that basically take this approach; allergy shots are probably the most basic, though I don’t believe they actually do anything with the thymus. The general term for this approach seems to be “Immune Tolerance”.
With a short search, I don’t see anything about reprogramming the thymus.
This would first require that you find an overlap of test subjects five hundred thousand strong that will not only volunteer to have their entire genome sequenced (a SNP array could be used to cut costs if you’re willing to sacrifice the breadth of variants interrogated), but will also sit down for an hours-long professionally-administered IQ test, like the WAIS-IV (again, could use some abridged test to cut costs and increase participation rate at the expense of lower-quality data)
I am more optimistic than you here. I think it is enough to get people who have already gotten their genomes sequenced through 23&Me or some other such consumer genomics service to either take an online IQ test or submit their SAT scores. You could also cross-check this with other data such people submit to validate their answer and determine whether it is plausible.
I think this could potentially be done for a few million dollars rather than 50. In fact companies like GenomeLink.io already have these kind of third party data analysis services today.
Also, we aren’t limited to western countries. If China or Taiwan or Japan or any other country creates a good IQ predictor, it can be used for editing purposes. Ancestry doesn’t matter much for editing purposes, only for embryo selection.
Would the quality of such tests be lower than those of professionally administered IQ tests?
Of course. But sample size cures many ills.
As far as delivery goes, the current state of these technologies will force you to use lipid nanoparticles because of the dangers of an inflammatory response being induced in the brain by an AAV, not to mention the risk of random cell death induction by AAVs, the causes of which are poorly understood.
I briefly looked into this and found these papers:
Adeno-Associated virus induces apoptosis during coinfection with adenovirus
I asked GPT4 whether adenoviruses enter the brain:
In general, adenoviruses are not commonly known to infect the brain or cause central nervous system diseases. Most adenovirus infections remain localized to the site where they first enter the body, such as the respiratory or gastrointestinal tracts. However, in rare cases, especially in individuals with weakened immune systems, adenoviruses can potentially spread to other organs, including the brain.
I also found this paper indicating much more problematic direct effects observed in mouse studies:
AAV ablates neurogenesis in the adult murine hippocampus
We demonstrate that neural progenitor cells (NPCs) and immature dentate granule cells (DGCs) within the adult murine hippocampus are particularly sensitive to rAAV-induced cell death. Cell loss is dose dependent and nearly complete at experimentally relevant viral titers. rAAV-induced cell death is rapid and persistent, with loss of BrdU-labeled cells within 18 hr post-injection and no evidence of recovery of adult neurogenesis at 3 months post-injection.
Also:
Efficient transduction of the dentategyrus (DG)– without ablating adult neurogenesis– can be achieved by injection of rAAV2-retro serotyped virus into CA3
So it sounds like there are potential solutions here and this isn’t necessarily a showstopper, especially if we can derisk using animal testing in cows or pigs.
You will need to use plasmid DNA (as opposed to mRNA, which is where lipid nanoparticles currently shine) if you want to keep them non-immunogenic and avoid the same immunogenicity risks of AAVs, which will significantly reduce your transduction efficiency lest you develop another breakthrough.
This is an update for me. I didn’t previously realize that the mRNA for a base or prime editor could itself trigger the innate immune system. I wonder how serious of a concern this would actually be?
If it is serious, we could potentially deliver RNPs directly to the cells in question. I think this would be plausible to do with pretty much any delivery vector except AAVs.
I don’t really see how delivering a plasmid with the DNA for the editor will be any better than delivering mRNA. The DNA will be transcribed into the exact same mRNA you would have been delivering anyways, so if the mRNA for CRISPR triggers the innate immune system thanks to CpG motifs or something, putting it in a plasmid won’t help much.
Lipid nanoparticles, even though they’re generally much safer, still have the potential to be immunogenic or toxic following repeated doses or high enough concentrations, which is another hurdle because you will need to use them repeatedly considering the number of edits you’re wanting to make.
Yeah, one other delivery vector I’ve looked into the last couple of days are extracellular vescicles. They seem to have basically zero problems with toxicity because the body already uses them to shuttle stuff around. And you can stick peptides on their surface similar to what we proposed with lipid nanoparticles.
The downside is they are harder to manufacture. You can make lipid nanoparticles by literally putting 4 ingredients plus mRNA inside a flask together and shaking it. ECVs require manufacturing via human cell colonies and purification.
A simple thought experiment may change your mind about mosaicism in the brain: consider what would happen in the case of editing multiple loci (whether purposeful or accidental) that happen to play a role in a neuron’s internal clock. If you have a bunch of neurons releasing substrates that govern one’s circadian rhythm in a totally discordant manner, I’d have to imagine the outcome is that the organism’s circadian rhythm will be just as discordant. This can be extrapolated to signaling pathways in general among neurons, where again one could imagine that if every 3rd or 4th or nth neuron is receiving, processing, or releasing ligands in a different way than either the upstream or downstream neurons, the result is some discordance that is more likely to be destructive than beneficial.
Thanks for this example. I don’t think I would be particularly worried about this in the context of off-target edits or indels (provided the distribution is similar to that of naturally occuring mutations), but I can see it potentially being an issue if the intellligence modifying alleles themselves work via regulating something like the neuron’s internal clock.
If this turns out to be an issue, one potential solution would be to exclude edits to genes that are problematic when mosaic. But this would probably be pretty difficult to validate in an animal model so that might just kill the project.
Thanks for the comment. This is actually quite helpful, as the effects of off-target edits or indels to promoter and enhancer regions is one of the primary uncertainties we have regarding feasibility of the proposal.
My prior for thinking that a few off-targets targets or indels wouldn’t necessarily be catastrophic was a paper I read that looked at the total accumulation of random mutations to neurons over the lifespan. I believe by age 40 the average person has about 1500.
Regulatory regions make up about 2% of the genome, so the average neuron has about 30 mutations in regulatory regions by the age of 40. So if we can keep our de novo mutations from increasing that number very much it will probably be ok.
Now it’s possible that the types of errors introduced by random mutations are of a different kind than those introduced by indels and off-targets from base and prime editors. A quick google search reveals that most de novos seem to be single base pair changes rather than insertion or deletion errors. So perhaps it WILL be an issue, at least for some editor variants.
I think the ideal approach to answer this question would be to use (or make) a computational model to predict the distribution of off-target edits and indels from editor variants, another to predict binding affinity as a function of sequence, and see how strongly such errors affect binding affinity. We could then compare those results to the affects of binding affinity from de novo mutations to see whether they were comparable in magnitude.
Perhaps others have already made such models. A quick search didn’t turn up anything, but I will continue looking.
The other option is to just test it empirically in cell cultures and then animal models.
If you have any other advice about how to approach this problem, I’d appreciate it.
The use of biotechnology to enhance intelligence raises ethical questions regarding fairness and equality. It would create an uneven playing field in terms of astronomical cost or not be FDA approved, as only those who can afford the enhancement may have access to it, exacerbating existing social inequalities like jet fuel on a dumpster fire.
This is of course something we’ve thought about. It’s a little hard to think too seriously about this so long as we stay on track to develop AGI, which will the gap between people due to genetic differences seem very small by comparison.
But if I ignore that for a moment, I think the best way to tackle this is to make sure the technology is to ensure the per unit cost is not too high (ideally < $10k), and to perhaps offer some innovative payment plans such as taking a percentage of people’s future earnings over the level they currently make for some period of time. So for example, instead of paying for the treatment directly, you might agree that for any money you make in excess of your current income, you pay the company 30% of it for 5 years.
Modifying genes to enhance intelligence could have unforeseen side effects or unintended consequences. Genetic modifications can have complex and unpredictable effects on various aspects of an individual’s physical and mental health, potentially leading to unforeseen negative outcomes.
This is of course somewhat of a concern, but most of the research I’ve read shows that whatever plieotropy exists between intelligence and other triats mostly works in your favor. In other words, the genes that increase intelligence generally tend to slightly decrease disease risk, violent behavior, and other traits generally considered negative.
The only exception to this I’ve seen this far is mild aspergers, the risk of which seems to be slightly increased by the same genes that affect intelligence. To the extent that this is a problem, we could simply identify the subset of genes that increase intelligence without increasing aspergers, or in addition to editing genes to increase intelligence, also edit genes to keep the risk of aspbergers constant at the same time.
Intellectual diversity is valuable for society as a whole. If everyone were genetically enhanced to have significantly higher intelligence, it may lead to a loss of diverse perspectives, creative thinking, and alternative problem-solving approaches that contribute to the richness of human society.
There’s 8 billion people on the planet. It’s going to take a very long time to edit even a million people. So I don’t think this should be a concern for at least another 50 years, by which point the question will probably be moot unless we have a global pause on AI development.
Individuals who choose not to undergo genetic enhancement for intelligence may face stigmatization or discrimination in a society that places a high value on intelligence. This could create a divide between enhanced and non-enhanced individuals, leading to social tensions and inequality.
Yes. I don’t really have a society-wide answer to this yet other than the good old “treat other people well”. This is already an issue with just natural variation in abilities, though gene editing would undoubtedly exacerbate it.
Focusing solely on increasing intelligence may lead to an imbalance in human development. Other important qualities such as emotional intelligence, creativity, empathy, and social skills may be undervalued or neglected, potentially resulting in a society that lacks holistic development. As we can deduce from the late 19th and early 20th centuries, pure application of intelligence even with the urging of now socially disregarded religious mores to empathy for others, there tends to be some really horrific wars that result and that creates incentives for weapons systems to be developed that could lead to the extinction of the species. As has been said by a wise sage of the Levant long ago, “man cannot live by bread alone.”
We don’t actually plan to solely focus on intelligence. In fact the first targets will be polygenic brain diseases like Alzheimers or treatment resistant depression. I also think it would be good if we could modify other traits such as conscientiousness, tolerance to sleep deprivation and others.
The long-term effects of genetically enhancing intelligence are currently unknown. It would require extensive research and testing to fully understand the potential consequences, including any negative impacts on individuals and the broader population. This would also be the means the gatekeepers are most likely to hold these things back using, if first they don’t use the patent court racket first (see below for thoughts on that specifically intended for the author’s consideration).
We have very smart people around right now. They seem to be doing fine. Maybe there are “long term consequences” to modifying people to be outside the human range, but we probably won’t push that far outside the limits of what naturally occurs.
It’s also plausible that we could reverse some of the effects of editing with another round of edits designed to push in the opposite direction.
Assuming intelligence is primarily IQ, which is how quickly and effectively an individual comes up with a solution for a novel problem, does not fully encapsulate the notion meant by the word intelligence.
I agree of course. The post was already over 30 pages long, so I decided not to discuss other forms of intelligence. But in reality those would be of interest as well.
However, the hard part with considering any of that is that we don’t currently have the phenotype data to create predictors of JUST reading ability or JUST math ability.
You are clearly very intelligent and apparently learn quickly, so now is the time to learn how to write patent applications for any and all processes you think evidently implied by these ideas or face paying a licensing fee to conduct research along lines you came up, if the patent holder even allows that much and doesn’t shovel the idea into the filing cabinet of doom.
I spoke with a few biologists before publishing this post, each of which informed me that I don’t have any truly novel ideas here. I have put together several existing ideas in a novel way, but it’s doubtful they are patentable.
We WILL file patents after we start doing lab work.
Interesting. I took Lumina around December and have noticed no change, though I don’t think I’ve actually been hungover during that time period. I definitely didn’t notice any difference in the AMOUNT needed to produce a hangover; I drunk a substantial amount and didn’t get hangovers any more easily.