Some back of the envelope calculations trying to make sense out of the number of subscribers.
The EA survey gets about ~2500 responses per year from self identified EAs and I expect it represents between 10% and 70% of the EA community, so a fair estimate is that the EA community is about 1e4 people.They ask about top priorities. About 16% of respondents consider AI risk a top priority.Assuming representativeness, that means about 2e3 EAs who consider AI risk a priority.Of those I would expect about half to be considering actively pursuing a career in the field, for 1e3 people.This checks out with the newsletter number of subscribers.
Typo: Tegmarck should be Tegmark
At risk of stating the obvious, have you considered attending a CFAR workshop in person?
I found them to be a really great experience, and now that they have started organizing events in Europe they are more accessible than ever!
Check out their page.
The movement I was going through when thinking about the RC is something akin to “huh, happiness/utility is not a concept that I have an intuitive feeling for, so let me substitute happiness/utility for resources. Now clearly distributing the resources so thinly is very suboptimal. So let’s substitute back resources for utility/happiness and reach the conclusion that distributing the utility/happiness so thinly is very suboptimal, so I find this scenario repugnant.”
Yeah, the simple model you propose beats my initial intuition. It feels very off though. Maybe its missing diminishing returns and I am rigged to expect diminishing returns?
I actually got directed to your article by another person before this! Congrats on creating something that people actually reference!
On hindsight, yeah, project based learning is nor what I meant nor a good alternative to traditional learning; if you can use cheat codes to speed up your learning using the experience from somebody else you should do so without a doubt.
The generator of this post is a combination of the following observations:
1) I see a lot of people who keep waiting for a call to adventure
2) Most knowledge I have acquired through life has turned out to be useless, non transferable and/or fades out very quickly
3) It makes sense to think that people get a better grasp of what skills they need to solve a problem (such as producing high quality AI Alignment research) after they have grappled with the problem. This feels specially true when you are in the edge of a new field, because there is no one else you can turn to who would be able to compress their experience in a digestible format.
4) People (specially in mathematics) have a tendency to wander around aimlessly picking up topics, and then use very few of what they learn. Here I am standing on not very solid ground, because conventional wisdom is that you need to wander around to “see the connections”, but I feel like that might be just confirmation bias creeping in.
It dissolves the RC for me, because it answers the question “What kind of cognitive algorithm, as felt from the inside, would generate the observed debate about “the Repugnant Conclusion”?” [grabbed from your link, substituted “free will” for “repugnant conclusion”].
I feel after reading that post that I do no longer feel that the RC is counterintuitive, and instead it feels self evident; I can channel the repugnancy to aberrant distributions of resources.
But granted, most people I have talked to do not feel the question is dissolved through this. I would be curious to see how many people stop being intuitively confused about RC after reading a similar line of reasoning.
The point about more workers ⇒ more resources is also an interesting thought. We could probably expand the model to vary resources with workers, and I would expect a similar conclusion for a reasonable model to hold: optimal sum of utility is not achieved in the extremes, but in a happy medium. Either that or each additional worker produces so much that even utility per capita grows as workers goes to infinity.
As I understand it, the idea behind this post dissolves the paradox because it allows us to reframe it in terms of possibility: for a fixed level of resources, there is a number of people for which equal distribution of resources produces optimal sum of utility.
Sure, you could get a greater sum from an enormous repugnant population at subsistence level, but that will take more resources than what you have to be created.
And what is more; even in that situation there is always another non-aberrant distribution of resources, that uses in total the same quantity of resources as the repugnant distribution, and produces greater sum of utility.
This has shifted my views very positively in favor of total log utilitarianism, as it dissolves quite cleanly the Repugnant Conclussion. Great post!
I have been thinking about this research direction for ~4 days.
No interesting results, though it was a good exercise to calibrate how much do I enjoy researching this type of stuff.
In case somebody else wants to dive into it, here are some thoughts I had and resources I used:
The definition of depth given in the post seems rather unnatural to me. This is because I expected it would be easy to relate the depth of two agents to the rank of the world of a Kripke chain where the fixed points representing their behavior will stabilize. Looking at Zachary Gleit’s proof of the fixed point theorem (see The Logic of Provability, chapter 8, by G. Boolos) we can relate the modal degree of a fixed point to the number of modal operators that appear in the modalized formula to be fixed. I thought I could go through Gleit’s proof counting the number of boxes that appear in the fixed points, and then combine that with my proof of the generalized fixed point theorem to derive the relationship between the number of boxes appearing in the definition of two agents and the modal degree of the fixed points that appear during a match. This ended up being harder than what I anticipated, because naively counting the number of boxes that appear in Gleit’s proof makes really fast growing formulas appear and its hard to combine them through the induction of the generalized theorem proof.
The Logic of Provability, by G. Boolos. Has pretty much everything you need to know about modal logic. Recommended reading chapters 1,3,4,5,6,7,8.
Fixed point theorem of provability logic, by J. Sevilla. An in depth explanation I wrote in Arbital some years ago.
Modal Logic in the Wolfram Language, by J. Sevilla. A working implementation of Modal Combat, with some added utilities. It is hugely inefficient and Wolfram is not a good choice because license issues, but may be useful to somebody who wants to compute the result of a couple combats or read about modal combat at introductory level. You can open the attached notebook in the Wolfram Programming Lab.
Thank you Scott for writing this post, it has been useful to get a glimpse of how to do research.
How much video proof evidence is used today is thus an upper bound for how useful it will be in the future, as video proof becomes easier to fake its credibility and thus usefulness diminishes.
If today we do not rely on video proofs that much, then this is not a big deal. If on the contrary we rely a lot in video proofs then this becomes a huge deal.
Could you sport a guess of how often does that happen, and which is thus the number of people affected each year for things like these? :)
But we also have the opposite narrative: people have more control over which parts of their life are shown in social media to their friends, so it’s easier for them to selectively create mask values.
And since in real life you are incentivized to not remain anonymous, it seems like this effect should prevail IRL, relegating ‘true’ values to anonimous social interaction.
Im not endorsing either view, just signalling confusion about narratives that I see as equally persuasive.
What do you think?