I follow Crocker’s rules.
niplav
Karma: 843
An AI Race With China Can Be Better Than Not Racing
Huh, the convergent lines of thought are pretty cool!
Your suggested solution is indeed what I’m also gesturing towards. A “barbell strategy” works best if we only have few dimensions we don’t want to make comparable, I think.
(AFAIU It grows only linearly, but we still want to perform some sampling of the top options to avoid the winners curse?)
I think this link is informative: Charitable interventions appear to be (weakly) lognormally distributed in cost-effectiveness. In general, my intuition is that “charities are lognormal, markets are normal”, but I don’t have a lot of evidence for the second part of the sentence.
Fat Tails Discourage Compromise
My current understanding is that he believes extinction or similar from AI is possible, at 5% probability, but that this is low enough that concerns about stable totalitarianism are slightly more important. Furthermore, he believes that AI alignment is a technical but solvable problem. More here.
I am far more pessimistic than him about extinction from misaligned AI systems, but I think it’s quite sensible to try to make money from AI even in worlds from high probability of extinction, since the market signal provided counterfactually moves the market far less than the realizable benefit from being richer in such a crucial time.
Thanks for tagging me! I’ll read the post and your comment with care.
Progress in Qualia
Not central to EA, but there’s Gwerns open questions.
I’d be curious about a list of topics they would like others to investigate/continue investigating, or a list of the most important open questions.
Due to the sudden work of unsung heroes, he was cryopreserved despite not having been signed up at the time of his deänimation.
I wonder whether the lives of those moths were net negative. If the population was rising, then the number of moths dying as larvae might’ve been fairly small. I assume that OPs apartment doesn’t have many predatory insects or animals that eat insects, so the risk of predation was fairly small. That leaves five causes of death: old age, hunger, thirst, disease and crushing.
Death by old age for moths is probably not that bad? They don’t have a very long life, so their duration of death also doesn’t seem very long to me, and couldn’t offset the quality of their life.
Hunger and thirst are likely worse, but I don’t know by how much, do starved moths die from heart problems? (Do moths have hearts?)
Disease in house moth colonies is probably fairly rare.
Crushing can be very fast or lead to long painful death. Seems the worst of those options.
I think those moths probably had a better life than outside, just given the number of predatory insects; but I don’t think that this was enough to make their lives net-positive. But it’s been a while since I’ve read into insect welfare, so if most young insects die by predation, I’d increase my credence in those moths having had net-positive lives.
More:
[Question] How Often Does ¬Correlation ⇏ ¬Causation?
Forecasters: What Do They Know? Do They Know Things?? Let’s Find Out!
Encountered while logged in. Now it doesn’t happen anymore. Maybe it was because I’d opened a bunch of tabs before dismissing the notification, which had still pre-loaded on other pages? Anyway, now it’s fixed, at least for me.
Basically a bug report: The popup “Sign up for the weekly EA Forum Digest” appears on every new page, even when I’ve already clicked “No thanks” on other pages. I highly dislike this.
Yep, seems true that useful advice comes from people who were in a similar situation and then solved the problem.
Does it happen often in EA that unqualified people give a lot of advice? 80,000 hours comes to mind, but you would hope they’re professional enough to having thought of this failure mode.
Ideally, I would include at this point some readings on how aggregation might work for building a utopia, since this seems like an obvious and important point. For instance, should the light cone be divided such that every person (or every moral patient more broadly, perhaps with the division taking moral weight into account) gets to live in a sliver of the light cone that’s optimized to fit their preferences? Should everybody’s preferences be aggregated somehow, so that everyone can live together happily in the overall light cone? Something else? However, I was unable to find any real discussion of this point. Let me know in the comments if there are writings I’m missing. For now, I’ll include the most relevant thing I could find as well as a more run-of-the-mill reading on preference aggregation theory.
It would probably be worth if for someone to write out the ethical implications of K-complexity-weighted utilitarianism/UDASSA on how to think about far-future ethics.
A few things that come to mind about this question (these are all ~hunches and maybe only semi-related, sorry for the braindump):
The description length of earlier states of the universe is probably shorter, which means that the “claw” that locates minds earlier in a simple universe is also shorter. This implies that lives earlier in time in the universe would be more important, and that we don’t have to care about exact copies as much.
This is similar to the reasons why not to care too much about Boltzmann brains.
We might have to aggregate preferences of agents with different beliefs (possible) and different ontologies/metaphysical stances (not sure about this), probably across ontological crises.
I have some preliminary writings on this, but nothing publishable yet.
The outcomes of UDASSA is dependent on the choice of Turing machine. (People say it’s only up to a constant, but that constant can be pretty big).
So we either find a way of classifying Turing machines by simplicity without relying on a single Turing machine to give us that notion, or we start out with some probability distribution over Turing machines and do some “2-level-Solomonoff induction”, where we update both the probability of each Turing machine and the probabilities of each hypothesis for Turing machine.
This leads to selfishness for whoever is computing Solomonoff induction, because the Turing machine where the empty program just outputs their observations receives the highest posterior probability.
If we use UDASSA/K-ultilitarianism to weigh minds there’s a pressure/tradeoff to simplify one’s preferences to be simpler.
If we endorse some kind of total utilitarianism, and there are increasing marginal returns to energymatter or spacetime investment into minds with respect to degree of moral patienthood then we’d expect to end up with very few large minds, if there are decreasing marginal returns we end up with many small minds.
Theorems like Gibbard-Satterthwaite and Hylland imply that robust preference aggregation that resists manipulation is really hard. You can circumvent this by randomly selecting a dictator, but I think this would become unnecesary if we operate in an open-source game theory context, where algorithms can inspect each others’ reasons for a vote.
I’m surprised you didn’t mention reflective equilibrium! Formalising reflective equilibrium and value formation with meta-preferences would be major steps in a long reflection.
I have the intuition that Grand Futures talks about this problem somewhere[1], but I don’t remember/know where.
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Which, given its length, isn’t that out there.
I’ve thought a bit about this and updated to include a (admittedly minor) discount for impactful or interesting work, “$20 for impactful or interesting projects, $35 for work with a public result, $50 otherwise”.
Thanks for the comment! The reasoning looks good, and was thought-provoking.
I think I disagree with you here. I model being bad at choosing good interventions as randomly sampling from the top n% (e.g. 30%) from the distribution when I’m trying to choose the best thing along the axis of e.g. non-x-risk impact. If this is a good way of thinking about it, then I don’t think that things change a lot—because of the concavity of the frontier, things I choose from that set are still going to be quite good from a non-x-risk perspective, and pretty middling from the x-risk perspective.
I am very unsure about this, but I think it might look like in this image:
When you choose from the top 30% on popularity, you get options from the purple box at random, and same for options in the green box for effectiveness.
If you want to push axes, I guess you’re going to aim for selecting from the intersection of both boxes, but I’m suspicious you actually can do that, or whether you end up selecting from the union of the boxes instead60%. Because if you can select from the intersection, you get options that are pretty good along both axes, pretty much by definition.
I could use my code to quantify how good this would be, though a concrete use case might be more illuminating.