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You're right, this is not a morality-specific phenomenon. I think there's a general formulation of this that just has to do with signaling, though I haven't fully worked out the idea yet.
For example, if in a given interaction it's important for your interlocutor to believe that you're a human and not a bot, and you have something to lose if they are skeptical of your humanity, then there's lots of negative externalities that come from the Internet being filled with indistinguishable-from-human chatbots, irrespective its morality.
Since you marked as a crux the fragment "absent acceleration they are likely to die some time over the next 40ish years" I wanted to share two possibly relevant Metaculus questions. Both of these seem to suggest numbers longer than your estimates (and these are presumably inclusive of the potential impacts of AGI/TAI and ASI, so these don't have the "absent acceleration" caveat).
OK, agreed that this depends on your views of whether cryonics will work in your lifetime, and of "baseline" AGI/ASI timelines absent your finger on the scale. As you noted, it also depends on the delta between p(doom while accelerating) and baseline p(doom).
I'm guessing there's a decent number of people who think current (and near future) cryonics don't work, and that ASI is further away than 3-7 years (to use your range). Certainly the world mostly isn't behaving as if it believed ASI was 3-7 years away, which might be a total failure of people acting on their beliefs, or it may just reflect that their beliefs are for further out numbers.
Simple math suggests that anybody who is selfish should be very supportive of acceleration towards ASI even for high values of p(doom).
Suppose somebody over the age of 50 thinks that p(doom) is on the order of 50%, and that they are totally selfish. It seems rational for them to support acceleration, since absent acceleration they are likely to die some time over the next 40ish years (since it's improbable we'll have life extension tech in time) but if we successfully accelerate to ASI, there's a 1-p(doom) shot at an abundant and happy eternity.
Possibly some form of this extends beyond total selfishness.
So, if your ideas have potential important upside, and no obvious large downside, please share them.
What would be some examples of obviously large downside? Something that comes to mind is anything that tips the current scales in a bad way, like some novel research result that directs researchers to more rapid capabilities increase without a commensurate increase in alignemnt. Anything else?
Immorality has negative externalities which are diffuse, and hard to count, but quite possibly worse than its direct effects.
Take the example of Alice lying to Bob about something, to her benefit and his detriment. I will call the effects of the lie on Alice and Bob direct, and the effects on everybody else externalities. Concretely, the negative externalities here are that Bob is, on the margin, going to trust others in the future less for having been lied to by Alice than he would if Alice has been truthful. So in all of Bob's future interactions, his truthful counterparties will have to work extra hard to prove that they are truthful, and maybe in some cases there are potentially beneficial deals that simply won't occur due to Bob's suspicions and his trying to avoid being betrayed.
This extra work that Bob's future counterparties have to put in, as well as the lost value from missed deals, add up to a meaningful cost. This may extend beyond Bob, since everyone else who finds out that Bob was lied to by Alice will update their priors in the same direction as Bob, creating second order costs. What's more, since everyone now thinks their counterparties suspect them of lying (marginally more), the reputational cost of doing so drops (because they already feel like they're considered to be partially liars, so the cost of confirming that is less than if they felt they were seen as totally truthful) and as a result everyone might actually be more likely to lie.
So there's a cost of deteriorating social trust, of p*ssing in the pool of social commons.
One consequence that seems to flow from this, and which I personally find morally counter-intuitive, and don't actually believe, but cannot logically dismiss, is that if you're going to lie you have a moral obligation to not get found out. This way, the damage of your lie is at least limited to its direct effects.
Agreed that ultimately everything is reverse-engineered, because we don't live in a vacuum. However, I feel like there's a meaningful distinction between:
1. let me reverse engineer the principles that best describe our moral intuition, and let me allow parsimonious principles to make me think twice about the moral contradictions that our actual behavior often implies, and perhaps even allow my behavior to change as a result
2. let me concoct a set of rules and exceptions that will justify the particular outcome I want, which is often the one that best suits me
For example, consider the contrast between "we should always strive to treat others fairly" and "we should treat others fairly when they are more powerful than us, however if they are weaker let us then do to them whatever is in our best interest whether or not it is unfair, while at the same time paying lip service to fairness in hopes that we cajole those more powerful than us into treating us fairly". I find the former a less corrupted piece of moral logic than the latter even though the latter arguably describes actual behavior fairly well. The former compresses more neatly, which isn't a coincidence.
There's something of a [bias-variance tradeoff](https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff) here. The smaller the moral model, the less expressive it can be (so the more nuance it misses), but the more helpful it will be on future, out-of-distribution questions.
The more complex the encoding of a system (e.g. of ethics) is, the more likely it is that it's reverse-engineered in some way. Complexity is a marker of someone working backwards to encapsulate messy object-level judgment into principles. Conversely, a system that flows outward from principles to objects will be neatly packed in its meta-level form.
In linear algebra terms, as long as the space of principles has fewer dimensions than the space of objects, we expect principled systems / rules to have a low-rank representation, with a dimensionality approaching that of the space of principles and far below that of the space of objects.
As a corrolary, perhaps we are justified in being more suspicious of complex systems over simple ones, since they come with a higher risk that the systems are "insincere", in the sense that they were deliberately created with the purpose of justifying a particular outcome rather than being genuine and principled.
This rhymes with Occam's razor, and also with some AI safety approaches which planned to explore whether dishonesty is more computationally costly than honesty.
Does this mean that meta-level systems are memetically superior, since their informational payloads are smaller? The success of Abrahamic religions (which mostly compress neatly into 10-12 commandments) might agree with this.
What's the cost of keeping stuff stuff around vs discarding it and buying it back again?
When you have some infrequently-used items, you have to decide between keeping them around (default, typically) or discarding them and buying them again later when you need them.
If you keep them around, you clearly lose use of some of your space. Suppose you keep these in your house / apartment. The cost of keeping them around is then proportional to the amount of either surface area or volume they take up. Volume is the appropriate measure to use especially if you have dedicated storage space (like closets) and the items permit packing / stacking. Otherwise, surface area is a more appropriate measure, since having some item on a table kind of prevents you from using the space above that table. The motivation for assigning cost like this is simple: you could (in theory) give up the items that take up a certain size, live a house that is smaller by exactly that amound, and save on the rent differential.
The main levers are:
- the only maybe non-obvious one is whether you think 2d or 3d the fair measure. 3d gives you a lot more space (since items are not cubic, and they typically take up space on one of their long sides, so they take up a higher fraction of surface area than volume). In my experince it's hard to stack too many things while still retaining access to them so I weigh the 2d cost more.
- cost (per sqft) of real estate in your area
- how expensive the item is
- how long before you expect to need the item again
There's some nuance here like perhaps having an item laying around has higher cost than just the space it takes up because it contributes to an unpleasant sense of clutter. On the other hand, having the item "at the ready" is perhaps worth an immediacy premium on top of the alternative scenario of having to order and wait for it when the need arises. We are also ignoring that when you discard and rebuy, you end up with a brand new item, and potentially in some cases you can either gift or sell your old item, which yields some value to yourself and/or others. I think on net these nuances nudge in the direction of "discard and rebuy" vs what the math itself suggests.
I made a spreadsheet to do the math for some examples here, so far it seems like for some typical items I checked (such as a ball or balloon pump) you should sell and rebuy. For very expensive items that pack away easily (like a snowboard) you probably want to hang onto them.
The spreadsheet is here, feel free to edit it (I saved a copy) https://docs.google.com/spreadsheets/d/1oz7FcAKIlbCJJaBo8XAmr3BqSYd_uoNTlgCCSV4y4j0/edit?usp=sharing
This raises the question of what it means to want to do something, and who exactly (or which cognitive system) is doing the wanting.
Of course I do want to keep watching YT, but I also recognize there's a cost to it. So on some level, weighing the pros and cons, I (or at least an earlier version of me) sincerely do want to go to bed by 10:30pm. But, in the moment, the tradeoffs look different from how they appeared from further away, and I make (or, default into) a different decision.
An interesting hypothetical here is whether I'd stay up longer when play time starts at 11:30pm than when play time starts at, say, 10:15pm (if bedtime is 10:30pm). The wanting to play, and the temptation to ignore the cost, might be similar in both scenarios. But this sunk cost / binary outcome fallacy would suggest that I'll (marginally) blow further past my deadline in the former situation than in the latter.
- Things slow down when Ilya isn't there to YOLO in the right direction in an otherwise very high-dimensional space.
I often mistakenly behave as if my payoff structure is binary instead of gradual. I think others do too, and this cuts across various areas.
For instance, I might wrap up my day and notice that it's already 11:30pm, though I'd planned to go to sleep an hour earlier, by 10:30pm. My choice is, do I do a couple of me-things like watch that interesting YouTube video I'd marked as "watch later", or do I just go to sleep ASAP? I often do the former and then predictably regret it the next day when I'm too tired to function well. I've reflected on what's going on in my mind (with the ultimate goal of changing my behavior) and I think the simplest explanation is that I behave as if the payoff curve, in this case of length of sleep, is binary rather than gradual. Rational decision-making would prescribe that, especially once you're getting less rest than you need, every additional hour of sleep is worth more rather than less. However, I suspect my instinctive thought process is something like "well, I've already missed my sleep target even if I go to sleep ASAP, so might as well watch a couple of videos and enjoy myself a little since my day tomorrow is already shot."
This is pretty terrible! It's the opposite of what I should be doing!
Maybe something like this is going on when poor people spend a substantial fraction of their income on the lottery (I'm already poor and losing an extra $20 won't change that, but if I win I'll stop being poor, so let me try) or when people who are out of shape choose not to exercise (I'm already pretty unhealthy and one 30-minute workout won't change that, so why waste my time.) or when people who have a setback in their professional career have trouble picking themselves back up (my story is not going to be picture perfect anyway, so why bother.)
It would be good to have some kind of mental reframing to help me avoid this prectictably regrettable behavior.
What if a major contributor to the weakness of LLMs' planning abilities is that the kind of step-by-step description of what a planning task looks like is content that isn't widely available in common text training datasets? It's mostly something we do silently, or we record in non-public places.
Maybe whoever gets the license to train on Jira data is going to get to crack this first.
Right - successful private companies (like nearly all the hot AI labs) are staying private for far longer (indefinitely?) so this bet will not capture any of the value they create for themselves.
It might also be that AGI is broadly deflationary, in that it will mostly melt moats and, with them, corporate margins (in most cases, except maybe the ones of the first company to roll out AGI).
Daniel Gross' [AGI Trades](https://dcgross.com/agitrades) (in particular the first question under "Markets") comes to mind.
It just seems far from certain to me that this bet will benefit from the outcome it's trying to hedge / capture, and given the possible implications here, I'd just urge whoever is considering putting this kind of bet on to get comfortable with that linkage (between real-world outcome and financial outcome) and not just take it for granted.
What gives you confidence that much value will accrue to the equity of the companies in those indices?
It seems like, in the past, technological revolutions mostly increase churn and are anti-incumbent in some way e.g. (this may be false in particular, but just to illustrate my argument with a concrete-sounding example) ORCL has over 150k employees whose jobs might get nuked if AGI can painlessly and securely transfer its clients to OSS instead of expensive enterprise solutions.
If I try to think about what's the most incumbent-friendly environment, almost by definition it ought to be one where not much is changing, but you're trying to capture value in the opposite scenario.
(sci-fi take?) If time travel and time loops are possible, would this not be the (general sketch of the) scenario under which it comes into existence:
1. a lab figures out some candidate particles that could be sent back in time, build a detector for them and start scanning for them. suppose the particle has some binary state. if the particle is +1 (-1) the lab buys (shorts) stock futures and exits after 5 minutes
2. the trading strategy will turn out to be very accurate and the profits from the trading strategy will be utilized to fund the research required to build the time machine
3. at some arbitrary point in the future, eventually, the r&d and engineering efforts are successful. once the device is built, the lab starts sending information back in time to tip itself to future moves in stock futures (the very same particles it originally received). this closes the time loop and guarantees temporal consistency
Reasons why this might not happen:
- time doesn't work like this, or time travel / loops aren't possible
- civilization doesn't survive long enough to build the device
- the lab can't commit to using its newfound riches to build the device, breaking the logic and preventing the whole thing from working in the first place
Thanks for these references! I'm a big fan, but for some reason your writing sits in the silly under-exploited part of my 2-by-2 box of "how much I enjoy reading this" and "how much of this do I actually read", so I'd missed all of your posts on this topic! I caught up with some of it, and it's far further along than my thinking. On a basic level, it matches my intuitive model of a sparse-ish network of causality which generates a much much denser network of correlation on top of it. I too would have guessed that the error rate on "good" studies would be lower!
Does belief quantization explain (some amount of) polarization?
Suppose people generally do Bayesian updating on beliefs. It seems plausible that most people (unless trained to do otherwise) subconsciosuly quantize their beliefs -- let's say, for the sake of argument, by rounding to the nearest 1%. In other words, if someone's posterior on a statement is 75.2%, it will be rounded to 75%.
Consider questions that exhibit group-level polarization (e.g. on climate change, or the morality of abortion, or whatnot) and imagine that there is a series of "facts" that are floating around that someone uninformed doesn't know about.
If one is exposed to facts in a randomly chosen order, then one will arrive at some reasonable posterior after all facts have been processed -- in fact we can use this as a computational definition of the what it would be rational to conclude.
However, suppose that you are exposed to the facts that support the in-group position first (e.g. when coming of age in your own tribe) and the ones that contradict it later (e.g. when you leave the nest.) If your in-group is chronologically your first source of intel, this is plausible. In this case, if you update on sufficiently many supportive facts of the in-group stance, and you quantize, you'll end up with a 100% belief on the in-group stance (or, conversely, a 0% belief on the out-group stance), after which point you will basically be unmoved by any contradictory facts you may later be exposed to (since you're locked into full and unshakeable conviction by quantization).
One way to resist this is to refuse to ever be fully convinced of anything. However, this comes at a cost, since it's cognitively expensive to hold onto very small numbers, and to intuitively update them well.
Causality is rare! The usual statement that "correlation does not imply causation" puts them, I think, on deceptively equal footing. It's really more like correlation is almost always not causation absent something strong like an RCT or a robust study set-up.
Over the past few years I'd gradually become increasingly skeptical of claims of causality just by updating on empirical observations, but it just struck me that there's a good first principles reason for this.
For each true cause of some outcome we care to influence, there are many other "measurables" that correlate to the true cause but, by default, have no impact on our outcome of interest. Many of these measures will (weakly) correlate to the outcome though, via their correlation to the true cause. So there's a one-to-many relationship between the true cause and the non-causal correlates. Therefore, if all you know is that something correlates with a particular outcome, you should have a strong prior against that correlation being causal.
My thinking previously was along the lines of p-hacking: if there are many things you can test, some of them will cross a given significance threshold by chance alone. But I'm claiming something more specific than that: any true cause is bound to be correlated to a bunch of stuff, which will therefore probably correlate with our outcome of interest (though more weakly, and not guaranteed since correlation is not necessarily transitive).
The obvious idea of requiring a plausible hypothesis for the causation helps somewhat here, since it rules out some of the non-causal correlates. But it may still leave many of them untouched, especially the more creative our hypothesis formation process is! Another (sensible and obvious, that maybe doesn't even require agreement with the above) heuristic is to distrust small (magnitude) effects, since the true cause is likely to be more strongly correlated with the outcome of interest than any particular correlate of the true cause.
Perhaps that can work depending on the circumstances. In the specific case of a toddler, at the risk of not giving him enough credit, I think that type of distinction is too nuanced. I suspect that in practice this will simply make him litigate every particular application of any given rule (since it gives him hope that it might work) which raises the cost of enforcement dramatically. Potentially it might also make him more stressed, as I think there's something very mentally soothing / non-taxing about bright line rules.
I think with older kids though, it's obviously a really important learning to understand that the letter of the law and the spirit of the law do not always coincide. There's a bit of a blackpill that comes with that though, once you understand that people can get away with violating the spirit as long as they comply with the letter, or that complying with the spirit (which you can grok more easily) does not always guarantee compliance with the letter, which puts you at risk of getting in trouble.
Pretending not to see when a rule you've set is being violated can be optimal policy in parenting sometimes (and I bet it generalizes).
Example: suppose you have a toddler and a "rule" that food only stays in the kitchen. The motivation is that each time food is brough into the living room there is a small chance of an accident resulting in a permanent stain. There's cost to enforcing the rule as the toddler will put up a fight. Suppose that one night you feel really tired and the cost feels particularly high. If you enforce the rule, it will be much more painful than it's worth in that moment (meaning, fully discounting future consequences). If you fail to enforce the rule, you undermine your authority which results in your toddler fighting future enforcement (of this and possibly all other rules!) much harder, as he realizes that the rule is in fact negotiable / flexible.
However, you have a third choice, which is to credibly pretend to not see that he's doing it. It's true that this will undermine your perceived competence, as an authority, somewhat. However, it does not undermine the perception that the rule is to be fully enforced if only you noticed the violation. You get to "skip" a particularly costly enforcement, without taking steps back that compromise future enforcement much.
I bet this happens sometimes in classrooms (re: disruptive students) and prisons (re: troublesome prisoners) and regulation (re: companies that operate in legally aggressive ways).
Of course, this stops working and becomes a farce once the pretense is clearly visible. Once your toddler knows that sometimes you pretend not to see things to avoid a fight, the benefit totally goes away. So it must be used judiciously and artfully.
Agreed with your example, and I think that just means that L2 norm is not a pure implementation of what we mean by "simple", in that it also induces some other preferences. In other words, it does other work too. Nevertheless, it would point us in the right direction frequently e.g. it will dislike networks whose parameters perform large offsetting operations, akin to mental frameworks or beliefs that require unecessarily and reducible artifice or intermediate steps.
Worth keeping in mind that "simple" is not clearly defined in the general case (forget about machine learning). I'm sure lots has been written about this idea, including here.
Regularization implements Occam's Razor for machine learning systems.
When we have multiple hypotheses consistent with the same data (an overdetermined problem) Occam's Razor says that the "simplest" one is more likely true.
When an overparameterized LLM is traversing the subspace of parameters that solve the training set seeking the smallest l2-norm say, it's also effectively choosing the "simplest" solution from the solution set, where "simple" is defined as lower parameter norm i.e. more "concisely" expressed.
In early 2024 I think it's worth noting that deep-learning based generative models (presently, LLMs) have the property of generating many plausible hypotheses, not all of which are true. In a sense, they are creative and inaccurate.
An increasingly popular automated problem-solving paradigm seems to be bolting a slow & precise-but-uncreative verifier onto a fast & creative-but-imprecise (deep learning based) idea fountain, a la AlphaGeometry and FunSearch.
Today, in a paper published in Nature, we introduce FunSearch, a method to search for new solutions in mathematics and computer science. FunSearch works by pairing a pre-trained LLM, whose goal is to provide creative solutions in the form of computer code, with an automated “evaluator”, which guards against hallucinations and incorrect ideas. By iterating back-and-forth between these two components, initial solutions “evolve” into new knowledge. The system searches for “functions” written in computer code; hence the name FunSearch.
Perhaps we're getting close to making the valuable box you hypothesize.
Upon reflection, the only way this would work is if verification were easier than deception, so to speak. It's not obvious that this is the case. Among humans, for instance, it seems very difficult for a more intelligent person to tell, in the general case, whether a less intelligent person is lying or telling the truth (unless the verifier is equipped with more resources and can collect evidence and so on, which is very difficult to do about some topics such as the verified's internal state) so, in the case of humans, in general, deception seems easier than verification.
So perhapst the daisy-chain only travels down the intelligence scale, not up.
To be sure, let's say we're talking about something like "the entirety of published material" rather than the subset of it that comes from academia. This is meant to very much include the open source community.
Very curious, in what way are most CS experiments not replicable? From what I've seen in deep learning, for instance, it's standard practice to include a working github repo along with the paper (I'm sure you know lots more about this than I do). This is not the case in economics, for instance, just to pick a field I'm familiar with.
I wonder how much of the tremendously rapid progress of computer science in the last decade owes itself to structurally more rapid truth-finding, enabled by:
- the virtual nature of the majority of the experiments, making them easily replicable
- the proliferation of services like github, making it very easy to replicate others' experiments
- (a combination of the points above) the expectation that one would make one's experiments easily available for replication by others
There are other reasons to expect rapid progress in CS (compared to, say, electrical engineering) but I wonder how much is explained by this replication dynamic.
It feels like (at least in the West) the majority of our ideation about the future is negative, e.g.
- popular video games like Fallout
- zombie apocalypse themed tv
- shows like Black Mirror (there's no equivalent White Mirror)
Are we at a historically negative point in the balance of "good vs bad ideation about the future" or is this type of collective pessimistic ideation normal?
If the balance towards pessimism is typical, is the promise of salvation in the afterlife in e.g. Christianity a rare example of a powerful and salient positive ideation about our futures (conditioned on some behavior)?
From personal observation, kids learn text (say, from a children's book, and from songs) back-to-front. That is, the adult will say all but the last word in the sentence, and the kid will (eventually) learn to chime in to complete the sentence.
This feels correlated to LLMs learning well when tasked with next-token prediction, and those predictions being stronger (less uniform over the vocabulary) when the preceding sequences get longer.
I wonder if there's a connection to having rhyme "live" in the last sound of each line, as opposed to the first.
Kind of related Quanta article from a few days ago: https://www.quantamagazine.org/what-your-brain-is-doing-when-youre-not-doing-anything-20240205/
For what it's worth (perhaps nothing) in private experiments I've seen that in certain toy (transformer) models, task B performance gets wiped out almost immediately when you stop training on it, in situations where the two tasks are related in some way.
I haven't looked at how deep the erasure is, and whether it is far easier to revive than it was to train it in the first place.
Reflecting on the particular ways that perfectionism differs from the optimal policy (as someone who suffers from perfectionism) and looking to come up with simple definitions, I thought of this:
- perfectionism looks to minimize the distance between an action and the ex-post optimal action but heavily dampening this penalty for the particular action "do nothing"
- optimal policy says to pick the best ex-ante action out of the set of all possible actions, which set includes "do nothing"
So, perfectionism will be maximally costly in an environment where you have lots of valuable options of new things you could do (breaking from status quo) but you're unsure whether you can come close to the best one, like you might end up choosing something that's half as good as the best you could have done. Optimal policy would say to just give it your best, and that you should be happy since this is an amazingly good problem to have, whereas perfectionism will whisper in your ear how painful it might be to only get half of this very large chunk of potential utility, and wouldn't it be easier if you just waited.
The parallel to athlete pre game rituals is an interesting one, but I guess I'd be interested in seeing the comparison between the following two groups:
group A: is told to meditate the usual way for 30 minutes / day, and does
group B: is told to just sit there for 30 minutes / day, and does
So both of the groups considered are sitting quietly for 30 minutes, but one group is meditating while the other is just sitting there. In this comparison, we'd be explicitly ignoring the benefit from meditation which acts via the channel of just making it more likely you actually sit there quietly for 30 minutes.
Is meditation provably more effective than "forcing yourself to do nothing"?
Much like sleep is super important for good cognitive (and, of course, physical) functioning, it's plausible that waking periods of not being stimulated (i.e. of boredom) are very useful for unlocking increased cognitive performance. Personally I've found that if I go a long time without allowing myself to be bored, e.g. by listening to podcasts or audiobooks whenever I'm in transition between activities, I'm less energetic, creative, sharp, etc.
The problem is that as a prescription "do nothing for 30 minutes" would be rejected as unappealing by most. So instead of "do nothing" it's couched as "do this other thing" with a focus on breathing and so on. Does any of that stuff actually matter or does the benefit just come from doing nothing?
To be sure, I'm not an expert on the topic.
Declines in male fertility I think are regarded as real, though I haven't examined the primary sources.
Regarding female fertility, this report from Norway outlines the trend that I vaguely thought was representative of most of the developed world over the last 100 years.
Female fertility is trickier to measure, since female fertility and age are strongly correlated, and women have been having kids later, so it's important (and likely tricky) to disentangle this confounder from the data.
Infertility rates are rising and nobody seems to quite know why. Below is what feels like a possible (trivial) explanation that I haven't seen mentioned anywhere.
I'm not in this field personally so it's possible this theory is out there, but asking GPT about it doesn't yield the proposed explanation: https://chat.openai.com/share/ab4138f6-978c-445a-9228-674ffa5584ea
Toy model:
- a family is either fertile or infertile, and fertility is hereditary
- the modal fertile family can have up to 10 kids, the modal infertile family can only have 2 kids
- in the olden days families aimed to have as many kids as they could
- now families aim to have 2 kids each
Under this model, in the olden days we would find a high proportion of fertile people in the gene pool, but in the modern world we wouldn't. Put differently, the old convention lead to a strong positive correlation between fertility and participation in the gene pool, and the new convention leads to 0 correlation. This removes the selective pressure on fertility, hence we should expect fertility to drop / infertility to rise.
Empirical evidence for this would be something like an analysis of the time series of family size variance and infertility -- is lower variance followed by increased infertility?
Thanks for the thoughtful reply. I read the fuller discussion you linked to and came away with one big question which I didn't find addressed anywhere (though it's possible I just missed it!)
Looking at the human social instinct, we see that it indeed steers us towards not wanting to harm other humans, but it weakens when extended to other creatures, somewhat in proportion to their difference from humans. We (generally) have lots of empathy for other humans, less so for apes, less so for other mammals (who we factory farm by the billions without most people particularly minding it) probably less so for octopi (who are bright but quite different) and almost none to the zillion microorganisms, some of which we allegedly evolved from. I would guess that even canonical Good Person Paul Christiano probably doesn't lose much sleep over his impact on microorganisms.
This raises the question of whether the social instinct we have, even if fully reverse engineered, can be deployed separately from the identity of the entity to which it is attached. In other words, if the social instinct circuitry humans have is "be nice to others in proportion to how similar to yourself they are", which seems to match the data, then we would need more than just the ability to place that circuitry in AGIs (which would presumably make the AGIs want to be nice to other similar AGIs). We would in fact need to be able to tease apart the object of empathy, and replace it with something that is very different than how humans operate, since no human is nice to microorganisms, so I see no evidence that the existing social instincts ever make any person be nice to something very different, and much weaker, than them, so I would expect it to work similarly in an AGI.
This is speculative, but it seems reasonably likely to me to turn out to be an actual problem. Curious if you have thoughts on it.
This is drifting a bit far afield from the neurobio aspect of this research, but do you have an opinion about the likelihood that a randomly sampled human, if endowed with truly superhuman powers, would utilize those powers in a way that we'd be pleased to see from an AGI?
It seems to me like we have many salient examples of power corrupting, and absolute power corrupting to a great degree. Understanding that there's a distribution of outcomes, do you have an opinion about the likelihood of benevolent use of great power, among humans?
This is not to say that this understanding can't still be usefully employed, but somehow it seems like a relevant question. E.g. if it turns out that most of what keeps humans acting pro-socially is the fear that anti-social behavior will trigger their punishment by others, that's likely not as juicy a mechanism since it may be hard to convince a comparatively omniscient and omnipotent being that it will somehow suffer if it does anti-social things.
Understood, and agreed, but I'm still left wondering about my question as it pertains to the first sigmoidal curve that shows STEM-capable AGI. Not trying to be nitpicky, just wondering how we should reason about the likelihood that the plateau of that first curve is not already far above the current limit of human capability.
A reason to think so may be something to do with irreducible complexity making things very hard for us at around the same level that it would make them hard for a (first-gen) AGI. But a reason to think the opposite would be that we have line of sight to a bunch of amazing tech already, it's just a question of allocating the resources to support sufficiently many smart people working out the details.
Another reason to think the opposite is that having a system that's (in some sense) directly optimized to be intelligent might just have a plateau drawn from a higher-meaned distribution than one that's optimized for fitness, and develops intelligence as a useful tool in that direction, since the pressure-on-intelligence for that sort of caps out at whatever it takes to dominate your immediate environment.
As a result, rather than indefinite and immediate exponential growth, I expect real-world AI growth to follow a series of sigmoidal curves, each eventually plateauing before different types of growth curves take over to increase capabilities based on different input resources (with all of this overlapping).
Hi Andy - how are you gauging the likely relative proportions of AI capability sigmoidal curves relative to the current ceiling of human capability? Unless I'm misreading your position, it seems like you are presuming that the sigmoidal curves will (at least initially) top out at a level that is on the same order as human capabilities. What informs this prior?
Due to the very different nature of our structural limitations (i.e. a brain that's not too big for a mother's hips to safely carry and deliver, specific energetic constraints, the not-very-precisely-directed nature of the evolutionary process) vs an AGI's system's limitations (which are simply different) it's totally unclear to me why we should expect the AGI's plateaus to be found at close-to-human levels.
Might LLMs help with this? You could have a 4.3 million word conversation with an LLM (with longer context windows than what's currently available) which could then, in parallel, have similarly long conversations with arbitrarily many members of the organization, adequately addressing specific confusions individually, and perhaps escalating novel confusions to you for clarification. In practice, until the LLMs become entertaining enough, members of the organization may not engage for long enough, but perhaps this lack of seductiveness is temporary.