Note:I spent my holidays writing a bunch of biology-adjacent, nontechnical pieces. I’ll intermittently mix them between whatever technical thing I send out, much like how a farmer may mix sawdust into feed, or a compounding pharmacist, butter into bathtub-created semaglutide. This one is about history!
The book ‘Death with Interruptions’ is a 2005 speculative fiction novel written by Portuguese author José Saramago. It is about how, mysteriously, on January 1st of an unnamed year in an unnamed country, death ceases to occur. Everyone, save the Catholic church, is initially very delighted with this. But as expected, the natural order collapses, and several Big Problems rear their ugly heads. I recommend reading it in full, but the synopsis is all I need to mention.
The situation described by José is obviously impossible. Cells undergo apoptosis to keep tissues healthy; immune systems kill off infected or malfunctioning cells; predators and prey form a food chain that only works because things end.
But what you may find interesting is that what exactly happens after death has not always been so clear-cut. Not the religious aspect, but the so-called thanatomicrobiome—the community of microbes that colonize and decompose a body after death—is not necessarily a given. And there is some evidence that, for a very, very long time, it simply did not exist at all. Perhaps for several millennia, the endless earth was a graveyard of pristine corpses, forests of bodies, oceans of carcasses, a planet littered with the indigestible dead.
Implausible, yes, but there is some evidence for it: the writings of a young apprentice scribe, aged fifteen, named Ninsikila-Enlil who was born in 1326 BCE and lived at a temple in ancient Babylon. Ninsikila-Enlil kept a diary, inscribed in tight, spiraling cuneiform on long clay tablets. In these tablets is his daily life, which primarily consisted of performing religious rituals for what has been loosely translated as the ‘Pit of Eternal Rest’. The purpose of this pit was precisely what the name implies: to store the deceased. It is, from the writings, unclear how deep the hole went, only that it was mentioned to be monstrously deep, so deep that centuries of bodies being slid down into it continued to slip into the nearly liquid darkness, sounds of their eventual impact never rising back to the surface.
But a particular curiosity were the bodies themselves.
Here I shall present two passages from Ninsikila’s writings, the first from early in his service, the second from a year later. The former is as follows:
The bodies wait in the preparation hall for seven days before consignment. I am permitted to visit after the second washing. My mother’s mother has been waiting for three days. She is the same as the day she passed. [The chief priest?] says the gods have made a gift of flesh. That it will remain this way even after she enters the pit. Her hands were always cracked from work, and they are still cracked.
There are many, many other paragraphs through his tablets that parallel this. An amber-like preservation is referenced repeatedly, described variously as “the stillness of resins,” or “flesh locked in golden sap.” But, later, Ninsikila put down the first observation of something new occurring amongst the bodies that wait to be placed in the pit. The second writing is this:
The wool-merchant [deposited?] on the third of Nisannu, and had been waiting for some time now. I pressed his chest and the flesh moved inward and did not return. Fluid on my hand. A smell I have not encountered before. Small, ebony things in his eyes, moving. I washed with būrtu-water seven times. I do not know what this is.
Rot, decomposition, it seemed, had finally arrived to a world that had not yet made room for it.
We know from Ninsikila writings that the wisest of the period, in search of what could have caused this, posited that the whole world had been tricked. That the flesh had once made a pact with time to remain eternally perfect, and time, in its naivety, had agreed. But something in the ink, some theorized, had curdled. Some insects had crawled across the tablet while the covenant was still wet, dragging one word into another and rendering the entire contract void.
Of course, it is worth raising some doubt at this. Ninsikila is a child, albeit clearly an erudite one, and would be prone to some flights of fantasy. How could we trust his retelling of the story? Unfortunately, we cannot, not fully, at least if our standard of proof here is having multiple, corroborating writings from the same period. But what we do have is historical evidence, or, at least, what some have argued is corroborating historical evidence.
Just a month after the initial finding of decomposition, Ninsikila writings cease. Moreover, this ending coincided with beginnings of the Hittite plague, an epidemic that, depending on which Assyriologist you consult, began somewhere between 1322 and 1324 BCE. And there is proof to suggest the fact that the true geographic foundations of the plague were, in fact, at the exact site of the pile of bodies watched over by Ninsikila. Some historians will protest at this, claiming that the Hittite plague was primarily a disease of the Anatolian heartland, far removed from Babylonian temple complexes. They will point to the well-documented military campaigns, the movement of prisoners of war.
But they all fail to account for, during the years in which the plague is believed to have started, there were multiple independent corroborations of the the skies of Babylon turning nearly ebony with flies, a canopy so dense it shaded the temple courtyards and drowned out religious chants with its own droning liturgy—a wet, collective susurration, the sound of ten billion small mouths working. The air turned syrupy, clinging to the skin, the foulness so thick it could nearly be chewed, metallic and rotten-fruit sweet. And the closer one got to Babylon, the more it drowned them beneath this sensory weight. We have records from a trade caravan whose leader—a merchant of salted fish and copper ingots—noted in his ledger that he could smell the city three days before he could see it. At one day’s distance, taste it, the foulness nearly making him retch.
The concentration of bodies in the Babylonian pile was higher than it had ever been not just in Babylon, not just in Mesopotamia, but in the entire known world. Tens of thousands of bodies stacked, pressed, pooled together in heat and humidity; an unprecedented density of biological matter that, prior to the centuries-long effort to gather it together, had never existed. Is it not possible that in this particular place, in the wet anaerobic environment, that new forms of life emerged? It feels obvious to posit that something was created here, something that consumed the pile, infected the air, and gorged itself on so much biological matter that it survives to this day, still swimming in our land and oceans.
Ninsikila-Enlil’s final entry is not particularly illuminating, but what is worth mentioning is where his resting place lies. Ninsikila was born with a birth defect: his sternum never fused, a fact we know from his writings. A soft hollow where his chest should have been, the bones bowing outward like the peeled halves of a pomegranate, exposing a quivering pouch of skin that pulsed visibly with his heartbeat. He noted that his priest-physicians, embarrassed, called it a divine aperture. His mother bound the hollow in layers of linen and never spoke of it again.
This is important, since it allowed us to place Ninsikila’s skeleton, which lies not at the top of the pile—as one may expect of a child succumbing to disease—but near the bottom. Endless bodies lay above him, centuries of death, likely nearly liquified when he encountered them. But his position is not passive, rather, his arms are outstretched, fingers cracked and blackened, the bones of his hands splintered at the ends, as though he had clawed his way down through thousands of corpses. Ninsikila was a child of God, born into the priesthood, spent his short life in faithful rituals to the divine, and it is perhaps only expected that his final moments were in desperate excavation, believing that somewhere below, at the base, lay the answer as to what had been corrupted, and whether it could be undone.
Why would an AI "want" anything? This post answers that question by examining a key part of the structure of intelligent cognition.
When you solve a novel problem, your mind searches for plans, predicts their outcomes, evaluates whether those outcomes achieve what you want, and iterates. I call this the "thinking loop". We will build some intuition for why any AI capable of solving difficult real-world problems will need something structurally similar.
This framework maps onto model-based reinforcement learning, where separate components predict outcomes (the model) and evaluate them (the critic). We'll use model-based RL as an important lens for analyzing alignment in this sequence—not because AGI will necessarily be built this way, but because analogous structure will be present in any very capable AI, and model-based RL provides a cleaner frame for examining difficulties and approaches.
The post is organized as follows:
Section 1.2 distinguishes goal-directed reasoning from habitual/heuristic reasoning, and explains how these interact. It also builds intuition for the thinking loop through everyday examples, and explains why it is necessary for solving novel problems.
Section 1.3 connects this to model-based reinforcement learning - the main framework we will be using for analyzing alignment approaches in future posts.
Section 1.4 explains that some behaviors are not easily compatible with goal-directed reasoning, including some we find intuitive and desirable.
Section 1.5 argues that for most value functions, keeping humans alive isn't optimal. It considers why we might survive - the AI caring about us, successful trade, exotic possibilities - but concludes we mostly need to figure out how to point the values of an AI.
Section 1.6 is a brief conclusion.
1.2 Thinking ahead is useful
1.2.1 Goal-Directed and Habitual Reasoning
Suppose you want to drive to your doctor’s office. To get there, you need to get to your doctor’s town, for which you need to drive on the highway from your town to your doctor’s town, for which you need to drive to the highway, for which you need to turn left. Your mind quickly reasons through those steps without you even noticing, so you turn left.
We can view your mind here as having a desired outcome (being at the doctor’s office) and searching for plans (what route to take) to achieve that outcome. This is goal-directed reasoning, which is an important part of cognition, but not the whole story yet.
Suppose the route to your doctor’s office starts out similar to the route to your workplace. So you’re driving along the highway and —wait was that the intersection where you needed to get out? Darn.
Here, you mistakenly drove further towards your workplace than you wanted to. Why? Because you have heuristics that guessed that this would be the right path to take, because you were thinking about something else so your goal directed reasoning was sleeping. This is an instance of habitual reasoning.
For effectively accomplishing something, there’s usually an interplay between goal-directed and habitual reasoning. Indeed, when you originally planned the route to your doctor’s town, heuristics played a huge role in you being able to think of a route quickly. Probably you drove there often enough to remember what route to take, and even if not, you have general heuristics for quickly finding good routes, e.g. “think of streets between you and your doctor’s town”.
1.2.2 The Thinking Loop
For now we have heuristics for proposing good plans, and a plan-evaluator which checks whether a plan is actually good for accomplishing what we want. If a plan isn’t good, we can query our heuristics again to adjust the plan or propose a new plan, until it’s good.
Let’s split up the plan-evaluator into a model, which predicts the outcomes of a plan, and a critic, which evaluates how good those outcomes are. Let’s also call the plan-proposing heuristics part “actor”. This gives us the thinking loop:
This diagram is of course a simplification. E.g. the actor may be very intertwined with the model rather than separate components, and plans aren’t proposed all at once. What matters is mostly that there is some model that predicts what would happen conditional on some actions/plan, some evaluation function of what outcomes are good/bad, and some way to efficiently find good plans.
Also, “outcomes” is supposed to be interpreted broadly, encompassing all sorts of effects from the plan including probabilistic guesses, not just whether a particular goal we care about is fulfilled.
And of course, this is only one insight into intelligence—there are more insights needed for e.g. figuring out how to efficiently learn a good world model, but we won’t go into these here.
Let’s look at a few examples for understanding thinking-loop structure.
Catching a ball:
You see the ball flying and you have a current plan for how to move your muscles.
The model part of your brain predicts where the ball will end up and where your hands will be given the planned muscle movements.
The critic part checks whether the predicted position of your hands is such that you’ll catch the ball. Let’s say it’s a bit off, so it sends the feedback that the hands should end up closer to the predicted position of the ball.
The actor part adjusts the planned muscle movements such that it expects the hands to end up in the right place.
… these steps repeat multiple times as the ball is flying.
Again, this isn’t meant to be a perfect description of what happens in your mind, but it is indeed the case that something vaguely like this is happening. In particular, there is some lookahead to what you expect the future state to be. It may feel like your muscles just moved reflexively to catch the ball, but that’s because you don’t have good introspective access to what’s happening in your mind.
Reminding a friend of a meeting:
Say you have a meeting in a few hours with a friend who often forgets meetings.
You remember your default plan of going to the meeting.
The model predicts that it’s plausible that your friend forgets to show up.
The critic evaluates the possibility where your friend doesn’t show up as bad.
The actor takes in that information and proposes to send a message to remind your friend.
The model predicts your friend will read it and come.
This evaluates as good.
Programming a user interface:
When a programmer programs a user interface, they have some vision in mind of how they want the user interface to look like, and their mind is efficiently searching for what code to write such that the user interface will look the way they intend.
1.2.3 Goal-Directed Reasoning is Important for Doing New Stuff
Suppose I give you the following task: Take a sheet of paper and a pair of scissors, and cut a hole into the sheet of paper, and then put yourself fully through the hole. (Yes it’s possible.)
Now, unless you heard the problem before, you probably don’t have any heuristics that directly propose a working solution. But you have a model in your mind with which you can simulate approaches (aka visualizing cutting the paper in some way) and check whether they work. Through intelligently searching for approaches and simulating them in your model, you may be able to solve the problem!
This is an example of the general fact that goal-directed reasoning generalizes further than just behavioral heuristics.
A related phenomenon is that when you learn a skill, you usually first perform the skill through effortful and slow goal-directed reasoning, and then you learn heuristics that make it easier and faster.
Although this is not all you need for learning skills—you also often need feedback from reality in order to improve your model. E.g. when you started to learn driving, you perhaps didn’t even have a good model of how much a car accelerates when you press the gas pedal down 4cm. So you needed some experience trying to learn a good model, and then some more experience for your heuristics to guess very well how deep you want to press the gas pedal depending on how much you want to accelerate.
1.2.4 The deeper structure of optimization
The thinking loop is actually an intuitive description of a deeper structure. In the words of Eliezer Yudkowsky:
The phenomenon you call by names like "goals" or "agency" is one possible shadow of the deep structure of optimization - roughly, preimaging outcomes onto choices by reversing a complicated transformation.
Aka, the model is a complicated transformation from choices/plans to outcomes, and we want to find choices/plans that lead to desired outcomes. One common way to find such plans is by doing some good heuristic search like in the thinking loop, but in principle you could also imagine other ways to find good plans—e.g. in a simple domain where the model is a linear transformation one could just invert the matrix.
1.3 Model-Based RL
Hopefully the above gave you an intuition for why smart AIs will very likely reason in a way that is at least in some way reminiscent of the actor-model-critic loop, although it could be only in an implicit way.
Current LLMs don’t have a separately trained model and critic. But they are trained to think in goal-directed ways in their chain of thought, so with that their thinking does embody the thinking loop at least a bit, although I expect future AIs to be even significantly more goal-directed.[1]
But the special case where we indeed have separately-trained modules for the actor, model, and critic, is called “actor-critic model-based RL”. One example of actor-critic model-based RL is the series that includes AlphaGo, AlphaZero, MuZero, and EfficientZero.
In a sense, the actor mostly helps with efficiency. It’s trained to propose plans whose consequences will be evaluated as good by the critic, so if you want to see where the actor steers towards you can look at what outcomes the critic likes. So for analyzing alignment, what matters is the model and the critic[2].
For the start of this sequence we will focus on the case where we do have a separately trained model and critic—not because I think AGI will be created this way (although it could be[3])—but because it’s a frame where alignment can be more easily analyzed.[4]
2.3.1 How the Model and Critic are Trained
Reward is a number that indicates how good/bad something that just happened was (the higher reward the better). Here are examples of possible reward functions:
For an AI that solves math problems, you could have a reward function that gives reward every time the AI submits a valid proof (or disproof) for a list of conjectures we care about.
For a trading AI, the changes in worth of the AI’s portfolio could be used as reward.
We can also use humans deciding when to give reward as a reward function. Aka they can look when the AI did something that seems good/bad to them and then give positive/negative reward.
The model is trained to predict observations, the critic is trained to predict expected future reward given the state of the model.[5]However, the critic isn’t necessarily a very smart part of the AI, and it’s possible that it learns to predict simple correlates of reward, even if the model part of the AI could predict reward even better.
For example, if I were to take heroin, that would trigger high reward in my brain, and I know that. However, since I’ve never taken heroin, a key critic (also called valence thought assessor) in my brain doesn’t yet predict high value for the plan of taking heroin. If the critic was a smart mind trying to (not just trained to) predict reward, I would’ve become addicted to heroin just from learning that it exists! But it’s just a primitive estimator that doesn’t understand the abstract thoughts of the model, and so it would only learn to assign high value to taking heroin after I tried heroin.
Anyway, very roughly speaking, the alignment problem is the problem of how to make the critic assign high value to states we like and low value to states we don’t like. More in upcoming posts.
1.4 Getting the behavior you want may be more difficult than you think
We would like to have AIs that can work on a difficult problem we give it (e.g. curing cancer), and also shut down when we ask it to shut down. Suppose we solved the problem of how to get the goals we want into an AI, then it seems like it should be quite feasible to make an AI behave that way, right?
Well, it’s possible in principle to have an AI that behaves that way. If we knew the 10.000 actions an AI would need to take to cure cancer, we could program an AI that behaves as follows:
For each timestep t:
Did the operator ask me to shut down?
Yes -> shut down.
No -> do the t-th action in the list of actions for curing cancer.
The problem is that we do not have a list of 10.000 steps to curing cancer. In order to cure cancer, we need some goal-directed search towards curing cancer.
So can we make the AI care about both curing cancer and shutting down when asked, without it trying to make us shut it down or otherwise behaving in an undesirable way?
No, we currently can’t—it’s an unsolved problem. Nobody knows how to do that even if we could make an AI pursue any goals we give it. See Shutdown Buttons and Corrigibility for why.
Although there is a different approach to corrigibility that might do a bit better, which I’ll discuss later in this series.
The lesson here isn’t just that shutdownability is difficult, but that intuitive hopes for how we expect an AI should behave may not actually be a realistic possibility for smart AIs.
1.4.1 Current AIs don’t provide good intuitions for this difficulty
Current AIs consist mostly out of behavioral heuristics - their goal-directed reasoning is still relatively weak. So for now you can often just train your AI to behave the way you want and it sorta works.[6]But when the AI gets smarter it likely stops behaving the way you want, unless you did your alignment homework.
1.5 In the limit of capability, most values lead to human extinction
Initially, the outcomes a young AI considers may be rather local in scope, e.g. about how a user may respond to an answer the AI gives.
But as an AI gets much smarter and thereby able to strongly influence the future of the universe, the outcomes the AI has preferences over will be more like universe-trajectories[7].[8][9]This change is likely driven both by the AI imagining more detailed consequences of its actions, and by parts of the AI trying to rebind its values when the AI starts to model the world in a different way[10]and to resolve inconsistencies of the AI’s preferences.
In the limit of technology, sentient people having fun isn't the most efficient solution for basically any value function, except if you value sentient people having fun. A full-blown superintelligence could take over the world and create nanotechnology with which it disassembles the lithosphere[11]and turns it into von-Neumann probes.
Viewed like this, the question isn’t so much “why would the AI kill us?” but “why would it decide to keep us alive?”. 3 possible reasons:
The AI terminally cares about us in some way. (Including e.g. if it mostly has alien values but has some kindness towards existing agents.[12])
We’ve managed to trade with the AI in a way it is bound by its commitments.
(Acausal trade with distant superintelligences that pay for keeping us alive.)
I’m not going to explain option 3 here - that might take a while. I just listed it for completeness, and I don’t think it is strategically relevant for most people.
Option 2 looks unworkable, unless we manage to make the superintelligence robustly care about particular kinds of honor or fairness, which it seems unlikely to care about by default.[13]
One way to look at option 1 is like this: Most value functions over universe-trajectories don’t assign high value to configurations where sentient people are having fun (let alone those configurations being optimal), nor do most value functions imply kindness to agents nearby.
That’s not a strong argument for doom yet - value functions for our AIs aren’t sampled at random. We might be able to make the AI have the right values, and we haven’t discussed yet whether it’s easy or hard. Introducing you to the difficulties and approaches here is what this sequence is for!
1.6 Conclusion
Now you know why we expect smart AIs to have something like goals/wants/values.
Such AIs are sometimes called “optimizers” or “consequentialists”, and the insight that smart AIs will have this kind of structure is sometimes called “consequentialism”[14]. The thinking loop is actually just an important special case in the class of optimization processes, which also e.g. includes evolution and gradient descent.[15]
In the next post, we’ll look at an example model-based RL setup and what values an AI might learn there, and thereby learn about key difficulties in making an AI learn the right values.
Questions and Feedback are always welcome!
Aside from thinking-loop structure in the chain of thought of the models, there likely also is lookahead within the forward pass of a LLM, where this information is then used within the same forward pass to decide which tokens to output. Although given the standard transformer architecture this lookahead->decision structure lacks some loopiness, so the full thinking loop structure comes from also having chain of thought reasoning (unless the newest LLMs have some relevant changes in architecture). ↩︎
Btw, the critic is sometimes also called “value function” or “reward model”. ↩︎
Brains seem to have a separately learned model and critic (aka “learned value function”) that scores thoughts in the human brain. See Steven Byrnes’ Intro to Brain-Like AGI and Valence series for more. ↩︎
Whether that makes alignment easier or harder than with LLMs is hard to say and I will return to this question later in this series. ↩︎
Where future reward may be time-discounted, aka reward in the far future doesn’t count as fully into the value score as reward soon. ↩︎
By universe-trajectory I mean the time-expanded state of the universe, aka how good the history and the future are combined, aka like the history of the universe from the standpoint of the end of the universe. (It’s actually not going to be exactly universe-trajectories either, and instead something about how greater reality even beyond our universe looks, but that difference doesn’t matter for us in this series of posts.) ↩︎
The claim here isn’t that the AI internally thinks about full universe-trajectories and scores how much it likes them, but that it will have a utility function over universe-trajectories which it tries to optimize, but may do so imperfectly because it is computationally bounded. This utility function can be rather indirect and doesn’t need to be written in pure math but can draw on the AI’s ontology for thinking about things. Indeed, some smart reflective humans already qualify here because they want something like their coherent extrapolated volition (CEV) to be fulfilled, which is a very indirect function over universe-trajectories (or rather greater reality). (Of course, these humans often still take actions other than what they expect is best according to their CEV, or you could see it as them pursuing CEV but in the presence of constraints of other drives within themselves.) This all sounds rather fancy, but ultimately it’s not that complex. It’s more like realizing that if you had god-like power to reshape the resources of a galaxy, you could reshape it into a state that seems nice to you, and then you update that it’s part of your preferences to fill galaxies with cool stuff. ↩︎
There’s no claim here about the AI needing to value far-future universe states similarly much as very near-future states. You can totally have a function over universe-trajectories that is mostly about what happens in the soon future, although it may be less simple, and how the universe will look in the far future will mostly depend on the parts of the AI preferences that also are about the far future. ↩︎
For instance, suppose the AI has a simple model of the human overseer’s mind with a “values” component, and it cares about whatever those values say. But then the AI learns a much more detailed psychological model of human minds, and there’s no very clear “values” node - there may be urges, reflectively endorsed desires, what the human thinks are his values vs what they will likely think in the future. Then there needs to be some procedure to rebind the original “values” concept to the new ontology so the AI can continue to care about it. ↩︎
Although I will mostly focus on the case where the AI learns human values, so we fulfill humanity’s potential of spreading love and joy through the galaxies, rather than merely surviving. ↩︎
Achieving this is also an alignment problem. To me this approach doesn’t look much easier than to make it care about human values, since the bottleneck is in targeting particular values of an AI at all in a way they get preserved as the AI becomes superintelligent. ↩︎
Because there are close parallels to the ethical theory that’s also called “consequentialism”, which argues that the morality of an action should be judged based on the action’s outcomes. ↩︎
Roughly speaking, other optimizers will still have some loop like the thinking loop, but instead of optimizing plans they may optimize genes or model weights, and instead of using the model to predict outcomes, you can just run tests in the world and see the outcome, and instead of having complex actor heuristics, you can have very simple heuristics that systematically improve the thing that is being optimized. So for evolution we have: genotype distribution -> phenotype distribution –which-reproduce?--> genotype distribution. For gradient descent: model weights -> predictions on data -> loss (aka how badly you predicted) -> compute gradient and update model weights to produce a lower loss. ↩︎
I gave a talk on exceptionally gifted children at the Reproductive Frontiers Summit at Lighthaven this June. I believe the subject matter is highly relevant to the experience of many rationalists (e.g. one of Scott's surveys has put the average IQ of his readers at 137, and although that's not as extreme as 160+, I think many of the observations generalize to the merely highly gifted). The talk is on YouTube:
I'd say the talk is more fun and more rationalist-focused, while the article is a bit more serious and meant for a wider audience. But mostly just pick whichever format you prefer.
The central policy proposal is that schools should allow students to progress through each subject at whatever rate fits them, and the cheapest implementation is to let them take placement tests and move up or down grade levels as appropriate (so a child might be taking 3rd grade math, 5th grade English, 4th grade history, etc. at once). I think this would benefit children of all ability levels, and have some systemic benefits as well; but obviously it makes the largest difference at the extremes.
Ultimately, it comes down to one question. Are you in? For you, and for them.
You’re Single Because They Got The Ick
The Ick, the ultimate red flag, makes perfect sense and is all about likelihood ratios.
Koenfucius: The ‘ick’ is a colloquial term for a feeling of disgust triggered by a specific—typically trivial—behaviour from a romantic partner, often leading to the relationship’s demise. New research explores why some are more prone to getting it than others.
Robin Hanson: “Women also experienced the ick more frequently, with 75% having had the ick compared to 57% of men … Those with a higher tendency for disgust … [&] grandiose narcissism was linked to stronger ick reactions, as was holding partners to exceptionally high standards.”
Paul Graham: About 30% of Seinfeld episodes were about this.
One gets The Ick because a small act is evidence of one’s general nature. The right type of person would never do [X], ideally never want to do [X], and at minimum would have learned not to do [X]. Often this is because they would know this is indicative of attribute [Y]. Indeed, if they should be aware that [X] is indicative of [Y], then their failure to do [X] is indicative not only of a lack of [Y], but also of a lack of desire or ability to even fake or signal [Y], especially in a romantic context. They don’t respect [Y]. Thus, this is extremely strong evidence. Thus, The Ick.
The person is not consciously thinking through this, but that’s the point.
That doesn’t mean that The Ick is always valid. Quite the contrary. Mistakes are made.
It’s fun to look at this list of Icks. There are very clear categories involved – status markers, stupidity and Your Mom are the big three. In general, it’s something that ‘looks bad’ and the fact that the man should know it looks bad and therefore not do it.
To what extent is The Ick a win-win? The majority of the time, I think it’s win-win, because them caring so much about this little thing, combined with you not caring, means it was never going to work. But on occasion there’s a classification mismatch, and when that happens it is super bad. And even if the particular person getting The Ick here is good, overall reaction to you continuing to do that thing is still bad, it’s almost certainly a mistake. So in general, if there’s something that is known to give The Ick, it’s worth making an effort to not do it.
You Are Still Single Because You Are Inventing Red Flags
This might be the new strangest take. It’s bad if he bought a house?
Cold: It’s offputting when a man buys a house when he’s single. Too prepared. The wife should help choose where they live, obviously. Is he just looking for a woman to slot in the missing hole in the fantasy he’s created? Even if a single man has money he should live in an apartment.
Midwest Antiquarian: What if you own an apartment?
cold: You’re doing great king.
Generous Farm: I bought when interest rates were most attractive. 2.9%. Now they’re 7.5%. Sorry couldn’t wait.
Cold: Love is bigger than 4.6% difference in rates.
Robin Hanson: Maybe many are a bit too eager to judge men for every little thing they do?
Any sane person would view ‘I own a house’ a highly positive sign. ‘Too prepared’?
If it’s a case of ‘I own this house and refuse to move’ then I can see an issue, and you should think about whether you want to live in that house. But houses can be sold.
This is what the wise man calls ‘favorable selection.’ If they turn you down because of this you presumably dodged a bullet. If someone thinks you should be paying 7.5% in interest rather than 2.9% so that you can avoid signaling you’re ‘too prepared’? Run. Or, rather, you don’t have to run, all you have to do is stay put. That’s the idea.
I hope and presume not too many people are treating ‘owns a house’ (but not an apartment, that would mean you’re doing great king?) in particular as a red flag.
Note that in the next section, one of Claire’s demands is that the man ‘has a small house,’ so a direct contradiction. I wonder if a large house is okay for her?
The more important point is yes, there is a large trend of judging based on a single data point, and looking for ways in which to find that data point a red flag. Stop it, or at least stop it once you’ve got substantial amounts of other information.
You’re Still Single Because You Demand The Same Generic Things
If you’re looking for the same things everyone is looking for, it’s rough out there.
it’s just that there is no depth here, no values, nothing to anchor a connection on, you couldn’t even write a compelling side character in a comic book on this outline.
The question isn’t where is he, it’s what would you do with him if you found him.
Danneskjold (with the correct answer): Happily married to a normal person.
Cynical Mike (also probably correct): All the things I’ll find before you find him:
Waldo, Carmen Santiago, Jimmy Hoffa, Epstein’s Suicide video, Pot of Gold at the end of a Rainbow, A Dragon, Aliens, Noah’s ARK, Hogwarts and a Pegasus.
The good news is this is not 15 things, it is more like 5. A lot of redundancy.
As Mason says, there’s nothing wrong with anything on the list but also nothing that differentiates what you in particular want, and it focuses on exactly the legible universally desirable features that put someone in high demand. The useful list has the things that you value more than ‘the market,’ and importantly drops some things that you value less.
You’re Single Because Everyone Is Too Picky
When the going gets weird, be careful not to inadvertently turn pro.
Misha: I think these days I see the biggest problem in dating is people are both increasingly weird and increasingly picky.
This applies not just to dating qua dating but all aspects of socialization, which are of course upstream of romance.
I think our minds are (sometimes perniciously) good at making us content with what seems possible.
The modern world shows us a far wider range of what’s possible.
What’s possible and what’s expected depends a lot on local culture and your knowledge of the world and one of these is drastically in flux right now and the other has grown immensely.
Imagine you get into hiking. This is a fairly common hobby, and you want a partner who will go with you. Woops, you might’ve just cut your potential partners by a large percentage.
Any given thing you want, or want to avoid? Mostly you can solve for that. Combine enough different such things, and you quickly get into trouble. The search algorithms are not that robust.
The Secretary Problem thus suggests that if you are maximizing, you should be deeply stingy about accepting a match until you’ve done a lot of calibration, and then take your sweet time after that.
But two big differences work against this in the dating case. You have an uncertain number of shots at this, taking each shot is costly in several ways, the pool you’re drawing from starts getting worse over time after a while, each time you’ve previously drawn may impose its own form of penalty to the final outcome, and you can easily miss outright. And once you pick, the comparisons directly impact your satisfaction levels. Thus, you want to be much quicker on the trigger.
Research on how individuals respond to ghosting, defined as unilaterally ending a relationship without providing explanations and ignoring communication attempts, has primarily relied on retrospective and imaginative methodologies. The present research introduced a novel multi-day daily-diary experimental paradigm to examine the psychological consequences of ghosting compared to rejection.
It should be common knowledge at this point that not explaining, and especially outright ghosting, is making your life easier at the expense of the person ghosted.
It can be the right move anyway, as in it sometimes helps you more than it hurts them. Not ghosting can have its own downsides, starting with them demanding a reason, or if you share a reason arguing about it, offering to change or getting really angry about it or using it against you (or in non-dating contexts outright suing). The less you say, the safer and better, and if you change your mind your options might be open.
Despite this, by default, you should be ghostminning.
If you know that you don’t want to continue talking to someone, say so. By default treat ghosting as a black mark on the person doing it. This applies to all forms of ghosting, not only in dating. Also, if they decide to ghost you, in some ways that’s a black mark on your ability to credibly signal that they don’t have to.
You’re Single Because You Won’t Tell Them The Problem
Cate Hall: unironically this is why everyone’s single
“oh, there’s a small thing you don’t like about a promising match? definitely break up with him instead of mentioning it”
no, you don’t have “the right” to force him to change it. but maybe just give him the info & let him decide?
Cate is correct that this is ludicrously terrible advice. He has hit a good vibe two dates out of three, everything else about him is great. Obviously you tell him that this cologne and aesthetic did not work for you. When did this become a ‘right to micromanage’ anything? Is there any possible world in which the guy is better off if you dump him rather than telling him, or silently suffer and never tell him?
I do think Jorbs is right that this reads like a ‘good on paper’ description, and she may be looking for an excuse. But that’s dumb, if you don’t want to date him then don’t.
Jorbs: it reads like someone who met someone she feels like she should want to date but doesn’t actually like him very much, and is struggling to process that or work out what to do with it. the positives aren’t enchanting, the negatives are cosmetic.
The response is ludicrous though.
Zac Hill: The thing that is really insidious about this response is the subtle everyone-has-forgotten-their-Carol-Dweck-lessons habit of *internalization of a behavior as a fixed characteristic*. It’s not that you don’t like his cologne — it’s that you ARE sensitive to fragrance.
A good starting rule is that if them changing one eminently changeable thing would make dating worthwhile, you should tell them about it.
You’re Not Single But The Clock Is Ticking
VB Knives: Just two useless boyfriends can easily consume the entire period between 21 and 30. You have one who doesn’t seem to ever propose. Then you finally get rid of him (you are 27) and the next effort brings you to 30+ with no ring. Not some exotic string of bad decisions.
Literary Chad: This is actually the issue– the median bodycount is 4, there aren’t wild sex parties, it’s serial monogamy without marriage or children.
Which is obvious unless you’re a boomer-like media consumer who believes that the existence of a sex party somewhere means they’re ubiquitous.
The more I think about this question, the more it seems like an obvious mistake to stay in a long term relationship for years and not fully commit.
I realize this is easier said than done. It is scary to go full The Ultimatum and insist on a rapid move up or move out, when you have something pretty good. It does seem like it is obviously a mistake to give things more than a year or at most two.
You’re Single Because of Your Bodycount
How much does it matter?
Steve Stewart-Williams: As shown in the graph below, the sweet spot was two to four past partners; fewer or more reduced attractiveness. In effect, people wanted someone with a bit of a past, but not too much (which was the title of our paper describing the research).
Intriguingly, we found no evidence for a sexual double standard: none, zilch, nada. Contrary to what’s often claimed, women weren’t judged any more harshly than men for having a high body count. That’s not to say they weren’t judged for it, but only that men were judged too.
This can be seen in the next graph. The left panel shows willingness ratings for long-term relationships, the right panel for short-term ones. As you can see, the sexes barely differed in their willingness to engage in long-term relationships. For short-term relationships, in contrast, men expressed greater willingness at every past-partner level.
This looks like a relative ranking, so it does not tell you how much this ‘willingness’ matters. Claiming ‘it literally does not matter at all’ would be bizarre, certainly this number contains information especially if the number is zero or one.
Also, I flat out defy the data on there being no double standard? No way. Even if on some abstract 1-9 scale it looks similar, the practical impact is very obviously totally different. Yes, a male body count of 60+ is functionally a negative, but not at the same level.
You’re Single Because You Failed To Read The Signs
Astrology is a problem for men when dating, because:
A remarkably high percentage of women believe in it to remarkably high degrees.
If taken at all seriously, it is deeply stupid.
Even as a clearly delineated fake framework, it’s pretty terrible.
It is also an opportunity, because the subset of humans that use astrology talk is not random, and the details of how a person interacts with astrology, no matter how seriously they do or don’t take it, are even less random.
Seattle Min You: If a girl asks your zodiac sign and your first response is to be annoyed, you’ve already fucked up. You don’t have enough whimsy in your heart to entertain an arbitrary topic for even a little tiny bit and it’s ugly.
Drea: Huge red flag… like, lighten up Francis.
Seattle Min You: Totally.
Purpskurp Capital LLC: If it was a whimsy topic for fun I’d 100% agree with too.
Problem is many of these girls use this stuff to make real life decisions, instead of using critical thinking and reasoning skills. It’s terrifying. And that’s why a lot of men like me treat it as a red flag.
Texanus: I will absolutely do that. for a child. are you a child? do you need to be treated like a child? I love my nieces and I’ve gotten all dressed up and played tea party with them. I’m not doing that with a grown woman however.
Positivity Moon: His jaw tightens a little. His eyes do that micro roll. He says something like “I don’t believe in that stuff” with the same energy you would use for “I don’t support war crimes.” The girl just asked for his birthday. No one was trying to rewrite physics.
People like to pretend it is about logic. Rationality. Being above “nonsense.” It almost never is. It is usually about control.
Because on the surface, the question is stupidly harmless. She is not building a medical treatment plan off your sun sign. She is not deciding whether to let you hold her wallet based on whether Mercury is breakdancing. She is opening a door to talk about patterns, personality, taste, how you see yourself. If you answer “Scorpio” or “Gemini” or whatever, the conversation that follows is almost never actually about stars. It is about you, but sideways. She is saying: teach me how to play with you for a second.
When your first instinct is annoyance, what you are really saying is: I hate being touched anywhere that does not fit my script.
Because if you truly did not care, you would just answer and move on. “I’m a Virgo.” Smile. Shrug. Ask hers. Make a joke. You do not have to secretly download Co–Star in the bathroom and start believing. You just have to have enough flexibility to sit in someone else’s little universe for five minutes without throwing a tantrum about empirical evidence.
People underestimate how much relationships are built on that ability. To step into someone’s weird side hobby, their micro belief system, their little rituals, even when you do not share them. She might have astrology. Someone else has Dungeons & Dragons lore. Another person has fantasy football statistics. Your uncle has his grill. None of it matters in a lab. All of it matters when you are trying to figure out: can I talk to this person about something that is technically pointless and still feel respected.
Annoyance at the sign question is rarely about skepticism. It is about contempt.
Philip Arola: “Astrology is a vehicle women use to communicate indirectly. Why would it possibly make you annoyed?”
Responding with visible annoyance, or declining to answer with your birthday or sign, is everywhere and always an unforced error. Don’t do that, even if you’re effectively writing her off and no matter how actually annoyed you are. There’s no reason to make things unpleasant, especially before you know how far she’s going with this.
However, well, do you still respect her as a potential partner? Should you?
Annoyance here can come from many places. One of which is ‘oh god I have to deal with this now,’ another related one is ‘damn it I am no longer as interested.’
There are three related but distinct stories from Moon here about a reaction of annoyance. You have logic versus control, and you have skepticism versus contempt, and you have ability to indulge in whimsey and retain respect.
There is also the claim motte and bailey claim that of course she doesn’t actually believe in astrology and won’t let this influence things beyond a little whimsy.
That brings us back to Purpskurp. There is a continuum of possibilities, but centrally three cases.
Topic of whimsy. It’s a way of making conversation, seeing how you play off an arbitrary set of predictions to get things rolling, a form of cold reading.
This is still a negative update even once you establish this, because she is indicating she thinks this is a good move. Why? Because unless this was maximally explicit, she is engaging in a form of selection, and that choice of selection tells you about her, and it makes this less likely to be a good match.
That said, this is totally survivable, especially if the whimsey is clear. Thus, you don’t want to fail the s*** test aspect of this by showing annoyance up top.
If this is approached explicitly and strategically up front as a fake framework, then that is totally fine.
Taken somewhat seriously as actual Bayesian evidence, as in it might influence her decisions regarding you, including in the future.
That’s trouble, potentially of two distinct forms.
It’s a bad sign on her general decision making capabilities and epistemics, and given you are reading this it’s a really bad sign about your compatibility across the board. It’s a red flag.
The actual astrological implications could be bad news for you. Then again, they might also be good news. What matters is her interpretation of this, on whatever level she understands astrology.
It’s worth noting that astrology is super flexible, so if you have green flags elsewhere you can ‘fight back’ if you know enough to play within the game.
Actual real belief in astrology, the way I believe in having lunch.
For the long term, this is a dealbreaker, period, straight up.
How it impacts the short term is, of course, up to you.
An ‘interest in’ astrology or tarot cards can be fine, although tarot cards are strictly better and astrology indicates poor taste. Actual belief? That’s a dealbreaker, ladies.
yashkaf: this is entirely correct in that being dismissive of people’s niche interests on a date is a much bigger “red flag” than astrology.
and yet if a cute girl posted “he brought up D&D and fantasy football on our first date, I rolled my eyes so hard” will anyone take the guy’s side?
no matter who brings up the cringe topic and who rolls their eyes, the guy fumbled.
no matter who escalated intimacy and who shot it down, the guy fumbled.
it’s always the guy who fumbles. thus, it’s always the guy who improves from feedback.
this makes dating very hard for women.
John Nerst: I mean astrology isn’t an interest, it’s a belief. Very different.
yashkaf: differentiating interests from beliefs from beliefs in belief from world models is just your own special weird niche interest
John Nerst: Going meta, how droll. But yes, most people are totally nuts and this is one of the surest signs
yashkaf: if you’re want to know the difference between “rat” and “post rat” without prejudice against either, read [the above] conversation between me and John and see who you intuitively side with
Sarah Constantin: yeah, rat all the way.
i think astrology is fun and i would not consider an interest in astrology a dealbreaker.
but i *definitely* don’t believe in stretching the meaning of “truth” or going “what is true or false, really?”
… i think it’s important to be grounded in (ordinary, waking, non-mystical) reality, to the point that you can enjoy *playing* with deviations from it, without getting seriously confused and throwing your life off the rails.
“zero playing around allowed” types are no-fun scolds, but “come on in, this is literally real and true, from a certain point of view, let me talk you into that” can destabilize some people for real.
There's a take I've seen going around, which goes approximately like this:
It used to be the case that you had to write assembly to make computers do things, but then compilers came along. Now we have optimizing compilers, and those optimizing compilers can write assembly better than pretty much any human. Because of that, basically nobody writes assembly anymore. The same is about to be true of regular programming.
I 85% agree with this take.
However, I think there's one important inaccuracy: even today, finding places where your optimizing compiler failed to produce optimal code is often pretty straightforward, and once you've identified those places 10x+ speedups for that specific program on that specific hardware is often possible[1]. The reason nobody writes assembly anymore is the difficulty of mixing hand-written assembly with machine-generated assembly.
The issue is that it's easy to have the compiler write all of the assembly in your project, and it's easy from a build perspective to have the compiler write none of the assembly in your project, but having the compiler write most but not all of the assembly in your project is hard. As with many things in proramming, having two sources of truth leads to sadness. You have many choices for what to do if you spot an optimization the compiler missed, and all of them are bad:
Hope there's a pragma or compiler flag. If one exists, great! Add it and pray that your codebase doesn't change such that your pragma now hurts perf.
Inline assembly. Now you're maintaining two mental models: the C semantics the rest of your code assumes, and the register/memory state your asm block manipulates. The compiler can't optimize across inline asm boundaries. Lots of other pitfalls as well - using inline asm feels to me like a knife except the handle has been replaced by a second blade so you can have twice as much knife per knife.
Factor the hot path into a separate .s file, write an ABI-compliant assembly function and link it in. It works fine, but it's an awful lot of effort, and your cross-platform testing story also is a bit sadder.
Patch the compiler's output: not a real option, but it's informative to think about why it's not a real option. The issue is that you'd have to redo the optimization on every build. Figuring out how to repeatably perform specific transforms on code that retain behavior but improve performance is hard. So hard, in fact, that we have a name for the sort of programs that can do it. Which brings us to
Improve the compiler itself. The "correct" solution, in some sense[2] — make everyone benefit from your insight. Writing the transform is kinda hard though. Figuring out when to apply the transform, and when not to, is harder. Proving that your transform will never cause other programs to start behaving incorrectly is harder still.
Shrug and move on. The compiler's output is 14x slower than what you could write, but it's fast enough for your use case. You have other work to do.
I think most of these strategies have fairly direct analogues with a codebase that an LLM agent generates from a natural language spec, and that the pitfalls are also analogous. Specifically:
Tweak your prompt or your spec.
Write a snippet of code to accomplish some concrete subtask, and tell the LLM to use the code you wrote.
Extract some subset of functionality to a library that you lovingly craft yourself, tell the LLM to use that library.
Edit the code the LLM wrote, with the knowledge that it's just going to repeat the same bad pattern the next time it sees the same situation (unless you also tweak the prompt/spec to avoid that)
I don't know what the analogue is here. Better scaffolding? More capable LLM?
Shrug and move on.
One implication of this worldview is that as long as there are still some identifiable high-leverage places where humans still write better code than LLMs[3], if you are capable of identifying good boundaries for libraries / services / APIs which package a coherent bundle of functionality, then you will probably still find significant demand for your services as a developer.
Of course if AI capabilities stop being so "spiky" relative to human capabilities this analogy will break down, and also there's a significant chance that we all die[4]. Aside from that, though, this feels like an interesting and fruitful near-term forecasting/extrapolation exercise.
For a slightly contrived concrete example that rhymes with stuff that occurs in the wild, let's say you do something along the lines of "half-fill a hash table with entries, then iterate through the same keys in the same order summing the values in the hash table"
Like so
// Throw 5M entries into a hashmap of size 10M
HashMap h;
h->keys = calloc(10000000 * sizeof(int));
h->values = calloc(10000000 * sizeof(double));
for (int k = 0; k < 5000000; k++) {
hashmap_set(h, k, randn(0, 1));
}
// ... later, when we know the keys we care about are 1..4999999
double sum = 0.0;
for (int k = 0; k < 5000000; k++) {
sum += hashmap_get(h, k);
}
printf("sum=%.6f\n", sum);
Your optimizing compiler will spit out assembly which iterates through the keys, fetches the value of each one, and adds it to the total. The memory access patterns will not be pretty
Example asm generated by gcc -o3
...
# ... stuff ...
# key pos = hash(key) % capacity
.L29: # linear probe loop to find idx of our key
cmpl %eax, %esi
je .L28
leaq 1(%rcx), %rcx
movl (%r8,%rcx,4), %eax
cmpl $-1, %eax
jne .L29
.L28:
vaddsd (%r11,%rcx,8), %xmm0, %xmm0 # sum += values[idx]
# ... stuff ...
This is the best your compiler can do: since the ordering of floating point operations can matter, it has to iterate through the keys in the order you gave. However, you the programmer might have some knowledge your compiler lacks, like "actually the backing array is zero-initialized, half-full, and we're going to be reading every value in it and summing". So you can replace the compiler-generated code with something like "Go through the entire backing array in memory order and add all values".
Example lovingly hand-written asm by someone who is not very good at writing asm
I observe a ~14x speedup with the hand-rolled assembly here.
In real life, I would basically never hand-roll assembly here, though I might replace the c code with the optimized version and a giant block comment explaining the terrible hack I was doing, why I was doing it, and why the compiler didn't do the code transform for me. I would, of course, only do this if this was in a hot region of code.
Chromosome selection is a hypothetical technology that assembles the genome of a new living cell out of whole chromosomes taken from multiple source cells. To do chromosome selection, you need a method for chromosome identification—distinguishing between chromosomes by number, and ideally also by allele content. This article investigates methods for chromosome identification. It seems that existing methods are subject to a tradeoff where they either destroy or damage the chromosomes they measure, or else they fail to confidently identify chromosomes. A paradigm for non-destructive high-confidence chromosome identification is proposed, based on the idea of complementary identification. The idea is to isolate a single chromosome taken from a single cell, destructively identify all the remaining chromosomes from that cell, and thus infer the identity of the preserved chromosome. The overall aim is to eventually develop a non-destructive, low-cost, accurate way to identify single chromosomes, to apply as part of a chromosome selection protocol.
Context
Reprogenetics is biotechnology to empower parents to make genomic choices on behalf of their future children. One key operation that's needed for reprogenetics is genomic vectoring: creating a cell with a genome that's been modified in some specific direction.
Chromosome selection is one possible genomic vectoring method. It could be fairly powerful if applied to sperm chromosomes or applied to multiple donors. The basic idea is to take several starting cells, select one or more chromosomes from each of those cells, and then put all those chromosomes together into one new cell:
There are three fundamental elements needed to perform chromosome selection:
Transmission and Exclusion. Get some chromosomes into the final cell, while excluding some other chromosomes.
Targeting. Differentially apply transmission and exclusion to different chromosomes.
This article deals with the targeting element. Future articles will deal with the other elements. Specifically, this article tries to answer the question:
How can we identify chromosomes?
That is, how can we come to know the number of one or more chromosomes that we are handling (i.e. is it chromosome 1, or chromosome 2, etc.)? Further, how can we come to know what alleles are contained in the specific chromosome we are handling, among whatever alleles are present among the chromosomes we're selecting from?
This problem has been approached from many angles. There are several central staples of molecular biology, such as DNA sequencing, karyotyping, flow cytometry, CRISPR-Cas9, and FISH; and there are several speculative attempts to study chromosomes in unusual ways, such as acoustics, laser scattering, hydrodynamic sorting, and electrokinesis.
This article presents an attempt to sort through these methods and find ones that will work well as part of a chromosome selection method. This goal induces various constraints on methods for chromosome identification; hopefully future articles will further clarify those constraints.
Synopsis and takeaways
A human cell has 46 chromosomes, 2 of each number, with each number (and X and Y) being of different sizes:
We want to identify chromosomes. Technically, that means we want to be able to somehow operate differently on chromosomes of different numbers. In practice, for the most part, what we want is to isolate one or more chromosomes, and then learn what number(s) they are. (If possible, we also want to learn what alleles they carry.)
How do we identify chromosomes? We have to measure them somehow.
There's a tradeoff between different ways of measuring chromosomes: How much access do you have to the DNA inside the chromosome? (Chromosomes are not just DNA; they also incorporate many proteins.)
On one extreme, there is, for example, standard DNA sequencing. In this method, you have lots of direct access to the DNA, so you can easily measure it with very high confidence, and learn the number of a chromosome and almost all of the alleles it carries. However, this method is also completely destructive. You strip away all the proteins from the DNA, you disrupt the epigenetic state of the DNA, and you chop up the DNA into tiny little fragments. High DNA access comes with high information, but also comes with high destructiveness.
On the other extreme, there is, for example, standard light microscopy. In this method, you have very little direct access to the chromosome's DNA. You just shine light on the chromosome and see what you can see. This method is not at all destructive; the chromosome's DNA, structural proteins, and epigenetic state are all left perfectly intact. However, this method definitely cannot tell you what alleles the chromosome carries, and may not even be able to distinguish many chromosomes by number. Low DNA access comes with low destructiveness, but also comes with low information.
If we're assembling a new cell (for example, to use in place of a sperm), we cannot use chromosomes that we have destroyed. We also (roughly speaking) cannot use a chromosome unless we're confident we know what number it is, because we have to be confident that the final cell will be euploid. Are there methods that are non-destructive and also make confident calls about chromosome number?
I don't know of a theoretical reason such a method should not exist. Why not measure physical properties of a chromosome from a distance and infer its number? For example, a single paper from 2006 claimed to use Raman spectroscopy to distinguish with fairly high confidence between human chromosomes 1, 2, and 3, just by bouncing (scattering) a laser off of them[2]. However, all such methods I've looked at are similar, in that they are very poorly refined: they have not been extensively replicated, so they may not work at all, and definitely have not been developed to be easy and reliable.
Therefore, as far as I know, there is currently probably no good way to identify chromosomes by directly measuring them. Every single such method will destroy the chromosome, or will not make confident calls about the chromosome's number, or else has not been well-demonstrated to work. Here's a visual summary of the situation:
[(Hi r/ChartCrimes!)]
Sidenote: Many readers might wonder: Why not just use standard cell culture sequencing? The reason will be explained more fully in a future article. But basically, the reason is that ensembling a target genome using cell culturing methods (such as MMCT) is likely to be very inconvenient. To avoid that, we want a more reductive mechanical method, an "isolating-ensembling" method, where we isolate single chromosomes, identify them, and then put target chromosomes into a new cell. Isolating-ensembling methods require a way to identify single chromosomes (or small sets of chromosomes); it's not enough to just learn the content of some full euploid genomes, which is all that is offered by cell culture sequencing.
So, if we cannot identify chromosomes by directly measuring them, what to do?
My proposal is to identify chromosomes by indirectly measuring them. To indirectly measure a chromosome, we get some material that comes from the same place as the chromosome. We then directly measure that material, and use that measurement to infer something about the chromosome:
A key indirect identification method is complementary chromosome identification. That's where you take a single cell with a known genome, isolate one chromosome, and then sequence the rest of the chromosomes. This tells you the identity of the isolated chromosome, without ever directly measuring that chromosome:
Another indirect identification method is single-cell RNA sequencing for sperm. This works by separating out RNAs from a single sperm and sequencing them. It turns out that those RNAs actually tell you which alleles are present in that sperm's genome. (See the subsection "Sequencing post-meiotic RNA".) This tells you the set of chromosomes you have, including what crossovers happened. (Another way to do this might be to briefly culture the sperm as haploid cells using donor oocytes[3]; see the subsection "Haploid culture".)
By combining complementary chromosome number identification with one of these indirect allele-measuring methods ("setwise homolog identification"), we could in theory isolate a single fully intact chromosome with a confidently, almost completely known genome.
This would be a good solution to chromosome identification. Unfortunately, these methods would be very challenging to actually develop. But, that effort might be worth it, since it seems there are not better chromosome identification methods available. See future articles for discussion of how to implement these methods.
The rest of this article will go into much more detail on many of the above points.
Ojeda, Jenifer F., Changan Xie, Yong-Qing Li, Fred E. Bertrand, John Wiley, and Thomas J. McConnell. ‘Chromosomal Analysis and Identification Based on Optical Tweezers and Raman Spectroscopy’. Optics Express 14, no. 12 (2006): 5385–93. https://doi.org/10.1364/OE.14.005385↩︎
