You acknowledge this but I feel you downplay the risk of cancer - an accidental point mutation in a tumour suppressor gene or regulatory region in a single founder cell could cause a tumour.
For each target the likely off-targets can be predicted, allowing one to avoid particularly risky edits. There may still be issues with sequence-independent off-targets, though I believe these are a much larger problem with base editors than with prime editors (which have lower off-target rates in general). Agree that this might still end up being an issue.
...Unless you ar
This seems unduly pessimistic to me. The whole interesting thing about g is that it's easy to measure and correlates with tons of stuff. I'm not convinced there's any magic about FSIQ compared to shoddier tests. There might be important stuff that FSIQ doesn't measure very well that we'd ideally like to select/edit for, but using FSIQ is much better than nothing. Likewise, using a poor man's IQ proxy seems much better than nothing.
This may have missed your point, you seem more concerned about selecting for unwanted covariates than 'missing things', which i...
Non-coding means any sequence that doesn't directly code for proteins. So regulatory stuff would count as non-coding. There tend to be errors (e.g. indels) at the edit site with some low frequency, so the reason we're more optimistic about editing non-coding stuff than coding stuff is that we don't need to worry about frameshift mutations or nonsense mutations which knock-out the gene where they occur. The hope is that an error at the edit site would have a much smaller effect, since the variant we're editing had a very small effect in the first place (and...
Another thing: if you have a test for which g explains the lion's share of the heritable variance, but there are also other traits which contribute heritable variance, and the other traits are similarly polygenic as g (similar number of causal variants), then by picking the top-N expected effect size edits, you'll probably mostly/entirely end up editing variants which affect g. (That said, if the other traits are significantly less polygenic than g then the opposite would happen.)
I should mention, when I wrote this I was assuming a simple model where the causal variants for g and the 'other stuff' are disjoint, which is probably unrealistic -- there'd be some pleiotropy.
Even out of this 10%, slightly less than 10% of that 10% responded to a 98-question survey, so a generous estimate of how many of their customers they got to take this survey is 1%. And this was just a consumer experience survey, which does not have nearly as much emotional and cognitive friction dissuading participants as something like an IQ test.
What if 23&me offered a $20 discount for uploading old SAT scores? I guess someone would set up a site that generates realistically distributed fake SAT scores that everyone would use. Is there a standardize...
Thanks for leaving such thorough and thoughtful feedback!
You could elect to use proxy measures like educational attainment, SAT/ACT/GRE score, most advanced math class completed, etc., but my intuition is that they are influenced by too many things other than pure g to be useful for the desired purpose. It's possible that I'm being too cynical about this obstacle and I would be delighted if someone could give me good reasons why I'm wrong.
The SAT is heavily g-loaded: r = .82 according to Wikipedia, so ~2/3 of the variance is coming from g, ~1/3 from other ...
I don't think this therapy as OP describes it is possible for reasons that have already been stated by HiddenPrior and other reasons
Can you elaborate on this? We'd really appreciate the feedback.
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 intereste...
Promoters (and any non-coding regulatory sequence for that matter) are extremely sensitive to point mutations.
A really important question here is whether the causal SNPs that affect polygenic traits tend to be located in these highly sensitive sequences. One hypothesis would be that regulatory sequences which are generally highly sensitive to mutations permit the occasional variant with a small effect, and these variants are a predominant influence on polygenic traits. This would be bad news for us, since even the best available editors have non-negligible...
what improvements would be top of mind for you?
The hope is that local neural function could be altered in a way that improves fluid intelligence, and/or that larger scale structural changes could happen in response to the edits (possibly contingent on inducing a childlike state of increased plasticity).
Showing that many genes can be successfully and accurately edited in a live animal (ideally human). As far as I know, this hasn't been done before! Only small edits have been demonstrated.
This is more or less our current plan.
Showing that editing embryos can result in increased intelligence. I don't believe this has even been done in animals, let alone humans.
This has some separate technical challenges, and is also probably more taboo? The only reason that successfully editing embryos wouldn't increase intelligence is that the variants being targeted weren...
Probably not? The effect sizes of the variants in question are tiny, which is probably why their intelligence-promoting alleles aren't already at fixation.
There probably are loads of large effect size variants which affect intelligence, but they're almost all at fixation for the intelligence-promoting allele due to strong negative selection. (One example of a rare intelligence promoting mutation is CORD7, which also causes blindness).
I think that most are focusing on single-gene treatments because that's the first step. If you can make a human-safe, demonstrably effective gene-editing vector for the brain, then jumping to multiplex is a much smaller step (effective as in does the edits properly, not necessarily curing a disease). If this were a research project I'd focus on researching multiplex editing and letting the market sort out vector and delivery.
Makes sense.
...I am more concerned about the off-target effects; neurons still mostly function with a thousand random mutations, but you
Really interesting, thanks for commenting.
My lab does research specifically on in vitro gene editing of T-cells, mostly via Lentivirus and electroporation, and I can tell you that this problem is HARD.
...Even in-vitro, depending on the target cell type and the amount/ it is very difficult to get transduction efficiencies higher than 70%, and t
The smaller size of Fanzors compared to Cas9 is appealing and the potential for lower immunogenicity could end up being very important for multiplex editing (if inflammation in off-target tissues is a big issue, or if an immune response in the brain turns out to be a risk).
The most important things are probably editing efficiency and the ratio of intended to unintended edits. Hard to know how that will shake out until we have Fanzor equivalents of base and prime editors.
(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.)
The stakes could hardly be more different -- polygenic trait selection doesn't get everyone killed if we get it slightly wrong.
How large are the Chinese genotype datasets?
The scaling laws are extremely well established in DL and there are strong theoretical reasons (and increasingly experimental neurosci evidence) that they are universal to all NNs, and we have good theoretical models of why they arise.
I'm not aware of these -- do you have any references?
Both brains and DL systems have fairly simple architectural priors in comparison to the emergent learned complexity
True but misleading? Isn't the brain's "architectural prior" a heckuva lot more complex than the things used in DL?
...Brains are very slow so have limited combina
Of course if you combine gene edits with other interventions to rejuvenate older brains or otherwise restore youthful learning rate more is probably possible
We thought a bit about this, though it didn't make the post. Agree that it increases the chance of the editing having a big effect.
ANNs and BNNs operate on the same core principles; the scaling laws apply to both and IQ in either is a mostly function of net effective training compute and data quality.
How do you know this?
Genes determine a brain's architectural prior just as a small amount of python code determines an ANN's architectural prior, but the capabilities come only from scaling with compute and data (quantity and quality).
In comparing human brains to DL, training seems more analogous to natural selection than to brain development. Much simpler "architectural prior", vastly mo...
Repeat administration is a problem for traditional gene therapy too, since the introduced gene will often be eliminated rather than integrated into the host genome.
Mildly deleterious mutations take a long time to get selected out, so you end up with an equilibrium where a small fraction of organisms have them. Genetic load is a relevant concept.
It seems fairly straightforward to test whether a chromosome transfer protocol results in physical/genetic damage in small scale experiments (e.g. replace chromosome X in cell A with chromosome Y in cell B, culture cell A, examine cell A's chromosomes under a microscope + sequence the genome).
The epigenetics seems harder. Having a good gears-level understanding of the epigenetics of development seems necessary, because then you'd know what to measure in an experiment to test whether your protocol was epigenetically sound.
You probably wouldn't be able to tell if the fruit fly's development was "normal" to the same standards that we'd hold a human's development to (human development is also just way more complicated, so the results may not generalize). That said, this sort of experiment seems worth doing anyways; if someone on LW was able to just go out and do it, that would be great.
A working protocol hasn't been demonstrated yet, but it looks like there's a decent chance it's doable with the right stitching together of existing technologies and techniques. You can currently do things like isolating a specific chromosome from a cell line, microinjecting a chromosome into the nucleus of a cell, or deleting a specific chromosome from a cell. The big open questions are around avoiding damage and having the correct epigenetics for development.
From section 3.1.2:
C. The EU passes such a law. 90%
...
M. There’s nowhere that Jurgen Schmidhuber (currently in Saudi Arabia!) wants to move where he’s allowed to work on dangerously advanced AI, or he retires before he can make it. 50%
These credences feel borderline contradictory to me. M implies you believe that, conditional on no laws being passed which would make it illegal in any place he'd consider moving to, Jurgen Schmidhuber in particular has a >50% chance of building dangerously advanced AI within 20 years or so. Since you also believe the EU h...
Since I currently have the slack to do so, I'm going to try getting into a balanced biphasic schedule to start with. If I actually manage to pull it off I'll make another post about it.
If we consider the TM to be "infinitely more valuable" than the rest of our life as I suggested might make sense in the post, then we would accept whenever . We will never accept if i.e. accepting does not decrease the description length of the TM.
Right. I think that if we assign measure inverse to the exponent of the shortest description length and assume that the probability increases the description length of the physically instantiated TM by (because the probability is implemented through reality branching which means more bits are needed to specify the location of the TM, or something like that), then this actually has a numerical solution depending on what the description lengths end up being and how much we value this TM compared to the rest of our life.
Say ...
I see. When I wrote
such a TM embedded in our physical universe at some point in the future (supposing such a thing is possible)
I implicitly meant that the embedded TM was unbounded, because in the thought experiment our physics turned out to support such a thing.
physicality of the initial states of a TM doesn't make its states from sufficiently distant future any more physically computed
I'm not sure what you mean by this.
Let's suppose the description length of our universe + bits needed to specify the location of the TM was shorter than any other way you might wish to describe such a TM. So with the lottery, you are in some sense choosing whether this TM gets a shorter or longer description.
Suppose I further specify the "win condition" to be that you are, through some strange sequence of events, able to be uploaded in such a TM embedded in our physical universe at some point in the future (supposing such a thing is possible), and that if you do not accept the lottery then no such TM will ever come to be embedded in our universe. The point being that accepting the lottery increases the measure of the TM. What's your answer then?
Sure, having just a little bit more general optimization power lets you search slightly deeper into abstract structures, opening up tons of options. Among human professions, this may be especially apparent in mathematics. But that doesn't make it any less scary?
Like, I could have said something similar about the best vs. average programmers/"hackers" instead; there's a similarly huge range of variation there too. Perhaps that would have been a better analogy, since the very best hackers have some more obviously scary capabilities (e.g. ability to find security vulnerabilities).
It's certainly plausible that something like this pumps in quite a bit of variation on top of the genetics, but I don't think it detracts much from the core argument: if you push just a little harder on a general optimizer, you get a lot more capabilities out.
Specialization on different topics likely explains much more than algorithmic tweaks explain.
That the very best mathematicians are generally less specialized than their more average peers suggests otherwise.
There are other reasons why top mathematicians could have better output compared to average mathematicians. They could be working on more salient problems, there's selection bias in who we call a "top mathematician", they could be situated in an intellectual microcosm more suitable for mathematical progress, etc.
Do you really think these things contribute much to a factor of a thousand? Roughly speaking, what I'm talking about here is how much longer it would take for an average mathematician to reproduce the works of Terry Tao (assuming the same prior inf...
I doubt you could use numpy to compute this efficiently, since (afaik) numpy only gives you a big speedup on very regular vector/matrix type computations, which this is not.
Do you think it would be a good idea to delete this and repost it at the beginning of August?
Alternatively, I could repost an easier version in a month, since I'd be shocked if anyone solved this one. Though I guess that was part of the point -- to show that induction of fairly simple programs is super hard for humans in general. The previous challenge was too easy because each element in the output sequence was a simple function of two elements earlier in the sequence (and the 'elements' were easy to identify as they were output in the standard floating point format). On the other hand, it would be neat to make tough-but-solvable program induction challenges a recurring monthly thing as you suggested. Thoughts?
This is true, but the farther out into the tails of the distribution we get the more likely we are to see negative effects that from traits that aren't part of the index we're selecting on.
True, but we wouldn't need to strictly select for G by association with IQ via GWASes. I suspect G variation is largely driven by mutation load, in which case simply replacing each rare variant with one of its more common counterparts should give you a huge boost while essentially ruling out negative pleiotropy. To hedge your bets you'd probably want to do a combined app...
Even the brightest geniuses don't really start having much of an impact on a field until about 20. And it takes further time for ideas to spread, so perhaps they'd need to reach the age of 30.
We could probably create humans vastly smarter than have ever previously existed with full genome synthesis, who could have a huge impact at a much younger age. But otherwise I agree.
...Another short-term technology not even mentioned on your list is gamete sequencing. Sperm and eggs are produced in groups of four, with two complementary pairs per stem cell. If we could
(Edited because I don't think my original terse reply made my thoughts on this very clear)
If we're in a (very) long timeline world, I suspect the default thing that ends up happening is that embryo selection is gradually adopted, and G slowly rises population-wide. The reason timelines are long in such a world is that AGI ended up being way harder than it currently looks, so the gradually rising G levels would indeed increase the probability that unaligned AGI is created, unless this somewhat-higher-G world also manages to get large scale coordination righ...
Good point, I didn't address this at all in the post. Germline editing is indeed outside the current Overton window. One thing I'm curious about is whether there are any shreds of hope that we might be able to accelerate any of the relevant technical research: one thing this implies is not specifically focusing on the use case of enhancement, to avoid attracting condemnation (which would risk slowing existing research due to e.g. new regulations being levied).
For some techniques this seems harder than for others: iterated embryo selection is pretty clearly...
I think I mostly agree with the critique of "pause and do what, exactly?", and appreciate that he acknowledged Yudkowsky as having a concrete plan here. I have many gripes, though.
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