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
Significantly Enhancing Adult Intelligence With Gene Editing May Be Possible
How to have Polygenically Screened Children
Toni Kurz and the Insanity of Climbing Mountains
Twiblings, four-parent babies and other reproductive technology
A Brief Review of Current and Near-Future Methods of Genetic Engineering
Digital brains beat biological ones because diffusion is too slow
Black Box Biology
Reply to a fertility doctor concerning polygenic embryo screening
OpenAI’s continued practice of publishing the blueprints allowing others to create more powerful models seems to undermine their claims that they are worried about “bad actors getting there first”.
If you were a scientist working on the Manhattan project because you were worried about Hitler getting the atomic bomb first, you wouldn’t send your research on centrifuge design to german research scientists. Yet every company that claims they are more likely than other groups to create safe AGI continues to publish the blueprints for creating AGI to the open web.
Is there any actual justification for this other than “The prestige of getting published in top journals makes us look impressive?”
We need a standard set of community advice for how to financially prepare for AGI
The Case for Human Genetic Engineering
I think people underestimate the degree to which hardware improvements enable software improvements. If you look at AlphaGo, the DeepMind team tried something like 17 different configurations during training runs before finally getting something to work. If each one of those had been twice as expensive, they might not have even conducted the experiment.
I do think it’s true that if we wait long enough, hardware restrictions will not be enough.
It’s not clear whether that will mean the end of humanity in the sense of the systems we’ve created destroying us. It’s not clear if that’s the case, but it’s certainly conceivable. If not, it also just renders humanity a very small phenomenon compared to something else that is far more intelligent and will become incomprehensible to us, as incomprehensible to us as we are to cockroaches.
Q: That’s an interesting thought. [nervous laughter]
Hofstadter: Well, I don’t think it’s interesting. I think it’s terrifying. I hate it. I think about it practically all the time, every single day. [Q: Wow.] And it overwhelms me and depresses me in a way that I haven’t been depressed for a very long time.
I don’t think I’ve ever seen a better description of how I feel about the coming creation of artificial superintelligence. I find myself returning over and over again to that post by benkuhn about “Staring into the abyss as a core life skill” I think that is going to become a necessary core life skill for almost everyone in the coming years.
It has been morbidly gratifying to see more and more people develop the same feelings about AI as I have had for about a year now. Like validation in the worst possible way. I think if people actually understood what was coming there would be a near total call to ban improvements in this technology and only allow advancement under very strict conditions. But almost no one has really thought through the consequences of making a general purpose replacement for human beings.
[Request]: Use “Epilogenics” instead of “Eugenics” in most circumstances
Human Genetic Engineering: Increasing Intelligence
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.
[Question] Estimating COVID cases & deaths in India over the coming months
Man, what a post!
My knowledge of alignment is somewhat limited, so keep in mind some of my questions may be a bit dumb simply because there are holes in my understanding.
It seems hard to scan a trained neural network and locate the AI’s learned “tree” abstraction. For very similar reasons, it seems intractable for the genome to scan a human brain and back out the “death” abstraction, which probably will not form at a predictable neural address. Therefore, we infer that the genome can’t directly make us afraid of death by e.g. specifying circuitry which detects when we think about death and then makes us afraid. In turn, this implies that there are a lot of values and biases which the genome cannot hardcode…
I basically agree with the last sentence of this statement, but I’m trying to figure out how to square it with my knowledge of genetics. Political attitudes, for example, are heritable. Yet I agree there are no hardcoded versions of “democrat” or “republican” in the brain.
This leaves us with a huge puzzle. If we can’t say “the hardwired circuitry down the street did it”, where do biases come from? How can the genome hook the human’s preferences into the human’s world model, when the genome doesn’t “know” what the world model will look like? Why do people usually navigate ontological shifts properly, why don’t people want to wirehead, why do people almost always care about other people if the genome can’t even write circuitry that detects and rewards thoughts about people?”.
This seems wrong to me. Twin studies, GCTA estimates, and actual genetic predictors all predict that a portion of the variance in human biases is “hardcoded” in the genome. So the genome is definitely playing a role in creating and shaping biases. I don’t know exactly how it does that, but we can observe that such biases are heritable, and we can actually point to specific base pairs in the genome that play a role.
Somehow, the plan has to be coherent, integrating several conflicting shards. We find it useful to view this integrative process as a kind of “bidding.” For example, when the juice-shard activates, the shard fires in a way which would have historically increased the probability of executing plans which led to juice pouches. We’ll say that the juice-shard is bidding for plans which involve juice consumption (according to the world model), and perhaps bidding against plans without juice consumption.
Wow. I’m not sure if you’re aware of this research, but shard theory sounds shockingly similar to Guynet’s description of how the parasitic lamprey fish make decisions in “The Hungry Brain”. Let me just quote the whole section from Scott Alexander’s Review of the book:
How does the lamprey decide what to do? Within the lamprey basal ganglia lies a key structure called the striatum, which is the portion of the basal ganglia that receives most of the incoming signals from other parts of the brain. The striatum receives “bids” from other brain regions, each of which represents a specific action. A little piece of the lamprey’s brain is whispering “mate” to the striatum, while another piece is shouting “flee the predator” and so on. It would be a very bad idea for these movements to occur simultaneously – because a lamprey can’t do all of them at the same time – so to prevent simultaneous activation of many different movements, all these regions are held in check by powerful inhibitory connections from the basal ganglia. This means that the basal ganglia keep all behaviors in “off” mode by default. Only once a specific action’s bid has been selected do the basal ganglia turn off this inhibitory control, allowing the behavior to occur. You can think of the basal ganglia as a bouncer that chooses which behavior gets access to the muscles and turns away the rest. This fulfills the first key property of a selector: it must be able to pick one option and allow it access to the muscles.
Spoiler: the pallium is the region that evolved into the cerebral cortex in higher animals.
Each little region of the pallium is responsible for a particular behavior, such as tracking prey, suctioning onto a rock, or fleeing predators. These regions are thought to have two basic functions. The first is to execute the behavior in which it specializes, once it has received permission from the basal ganglia. For example, the “track prey” region activates downstream pathways that contract the lamprey’s muscles in a pattern that causes the animal to track its prey. The second basic function of these regions is to collect relevant information about the lamprey’s surroundings and internal state, which determines how strong a bid it will put in to the striatum. For example, if there’s a predator nearby, the “flee predator” region will put in a very strong bid to the striatum, while the “build a nest” bid will be weak…
Each little region of the pallium is attempting to execute its specific behavior and competing against all other regions that are incompatible with it. The strength of each bid represents how valuable that specific behavior appears to the organism at that particular moment, and the striatum’s job is simple: select the strongest bid. This fulfills the second key property of a selector – that it must be able to choose the best option for a given situation…
With all this in mind, it’s helpful to think of each individual region of the lamprey pallium as an option generator that’s responsible for a specific behavior. Each option generator is constantly competing with all other incompatible option generators for access to the muscles, and the option generator with the strongest bid at any particular moment wins the competition.
You can read the whole review here or the book here. It sounds like you may have independently rederived a theory of how the brain works that neuroscientists have known about for a while.
I think this independent corroboration of the basic outline of the theory makes it even more likely shard theory is broadly correct.
I hope someone can work on the mathematics of shard theory. It seems fairly obvious to me that shard theory or something similar to it is broadly correct, but for it to impact alignment, you’re probably going to need a more precise definition that can be operationalized and give specific predictions about the behavior we’re likely to see.
I assume that shards are composed of some group of neurons within a neural network, correct? If so, it would be useful if someone can actually map them out. Exactly how many neurons are in a shard? Does the number change over time? How often do neurons in a shard fire together? Do neurons ever get reassigned to another shard during training? In self-supervised learning environments, do we ever observe shards guiding behavior away from contexts in which other shards with opposing values would be activated?
Answers to all the above questions seem likely to be downstream of a mathematical description of shards.
- 23 Oct 2022 19:10 UTC; 3 points) 's comment on The heritability of human values: A behavior genetic critique of Shard Theory by (
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
I’ll give a quick TL;DR here since I know the post is long.
There’s about 20,000 genes that affect intelligence. We can identify maybe 500 of them right now. With more data (which we could get from government biobanks or consumer genomics companies), we could identify far more.
If you could edit a significant number of iq-decreasing genetic variants to their iq-increasing counterpart, it would have a large impact on intelligence. We know this to be the case for embryos, but it is also probably the case (to a lesser extent) for adults.
So the idea is you inject trillions of these editing proteins into the bloodstream, encapsulated in a delivery capsule like a lipid nanoparticle or adeno-associated virus, they make their way into the brain, then the brain cells, and the make a large number of edits in each one.
This might sound impossible, but in fact we’ve done something a bit like this in mice already. In this paper, the authors used an adenovirus to deliver an editor to the brain. They were able to make the targeted edit in about 60% of the neurons in the mouse’s brain.
There are two gene editing tools created in the last 7 years which are very good candidates for our task, with a low chance of resulting in off-target edits or other errors. Those two tools are called base editors and prime editors. Both are based on CRISPR.
If you could do this, and give the average brain cell 50% of the desired edits, you could probably increase IQ by somewhere between 20 and 100 points.
What makes this difficult
There are two tricky parts of this proposal: getting high editing efficiency, and getting the editors into the brain.
The first (editing efficiency) is what I plan to focus on if I can get a grant. The main issue is getting enough editors inside the cell and ensuring that they have high efficiency at relatively low doses. You can only put so many proteins inside a cell before it starts hurting the cell, so we have to make a large number of edits (at least a few hundred) with a fixed number of editor proteins.
The second challenge (delivery efficiency) is being worked on by several companies right now because they are trying to make effective therapies for monogenic brain diseases. If you plan to go through the bloodstream (likely the best approach), the three best candidates are lipid nanoparticles, engineered virus-like particles and adeno-associated viruses.
There are additional considerations like how to prevent a dangerous immune response, how to avoid off-target edits, how to ensure the gene we’re targeting is actually the right one, how to get this past the regulators, how to make sure the genes we target actually do something in adult brains, and others which I address in the post.
What I plan to do
I’m trying to get a grant to do research on multiplex editing. If I can we will try to increase the number of edits that can be done at the same time in cell culture while minimizing off-targets, cytotoxicity, immune response, and other side-effects.
If that works, I’ll probably try to start a company to treat polygenic brain disorders like Alzheimers. If we make it through safety trials for such a condition, we can probably start a trial for intelligence enhancement.
If you know someone that might be interested in funding this work, or a biologist with CRISPR editor expertise, please send me a message!