Thanks for cross-posting this, I probably wouldn’t hear about this otherwise.
I am very interested in Open Phil’s model regarding the best time to donate for such causes. If anyone is aware of similar models for large donors, I would love to hear about them.
Thanks for sharing that, that sounds like an interesting plan.
A while ago I was trying to think about potential ways to have large impact via formal verification (after reading this post). I didn’t give it much attention, but it looks like others and I don’t see a case for this career path to be highly impactful, but I’d to love be proven wrong. I would appreciate it if you could elaborate on your perspective on this.
I should probably mention that I couldn’t find a reference to formal verification at agent foundations (but I didn’t really read it), and Vanessa seemed to reference it as a tangential point, but I might be wrong about both.
I’m interested in formal verification from a purely mathematical point of view. That is, I think it’s important for math (but I don’t think that formalizing [mainstream] math is likely to be very impactful outside of math). Additionally, I am interested in ideas developed in homotopy type theory, because of their connections to homotopy theory, rather than because I think it is impactful.
With regards to FIRE, I myself still haven’t figured out how this fits with my donations. In any case, I think that giving money to beggars sums up to less than $5 per month in my case (and probably even less on average), but I guess that also depends on where you live etc.
I would like to reiterate Edo’s answer, and add my perspective.
First and foremost, I believe that one can follow EA perspectives (e.g. donate effectively) AND be kind and helpful to strangers, rather than OR (repeating an argument I made before in another context).In particular, I personally don’t write giving a couple of dollars in my donation sheet, and it does not affect my EA-related giving (at least not intentionally).
Additionally, they constitute such a little fraction of my other spending, that I don’t notice them financially.Despite that, I truly believe that being kind to strangers, giving a few coins, or trying to help in other ways, can meaningfully help the other person (even if not as cost-effectively as donating to, say, GiveWell).
I don’t view this and my other donations as means to achieve the exact same goal, but rather as two distinct and non-competing ways to achieve the purpose of making the world better.
Thank you for following up and clarifying that.
I see, thanks for the teaser :)
I was under the impression that you have rough estimate for some charities (e.g. StrongMinds). Looking forward to see your future work on that.
Thanks for posting that. I’m really excited about HLI’s work in general, and especially the work on the kinds of effects you are trying to estimate in this post!
I personally don’t have a clear picture of how much $ / WELLBY is considered good (whereas GiveWell’s estimates for their leading charities is around 50-100 $ / QALY). Do you have a table or something like that on your website, summarizing your results for charities you found to be highly effectively, for reference?
I recently made a big career change, and I am planning to write a detailed post on this soon. In particular, it will touch this point.
I did use use Fermi calculation to estimate my impact in my career options.In some areas it was fairly straightforward (the problem is well defined, it is possible to meaningfully estimate the percentage of problem expected to be solved, etc.). However, in other areas I am clueless as to how to really estimate this (the problem is huge and it isn’t clear where I will fit in, my part in the problem is not very clear, there are too many other factors and actors, etc.).
In my case, I had 2 leading options, one of which was reasonable to amenable to these kind of estimates, and the other—not so much. The interesting thing was that in the first case, my potential impact turned out to be around the same order of magnitude as EtG, maybe a little bit more (though there is a big confidence interval).
All in all, I think this is a helpful method to gain some understanding of the things you can expect to achieve, though, as usual, these estimates shouldn’t be taken too seriously in my opinion.
I think another interesting example to compare to (which also relates to Asaf Ifergan’s comment) is private research institutes and labs. I think they are much more focused on specific goals, and give their researchers different incentives than academia, although the actual work might be very similar. These kinds of organizations span a long range between academia and industry.
There are of course many such example, some of which are successful and somre are probably not that much. Here are some examples that come to my mind: OpenAI, DeepMind, The Institute for Advanced Study, Bell Labs, Allen Institute for Artificial Intelligence, MIGAL (Israel).
I just wanted to say that I really like your idea, and at least at the intuitive level it sounds like it could work. Looking forward to the assessment of real-world usage!
Also, the website itself looks great, and very easy to use.
Thanks for the response.I believe this answers the first part, why GPT-3 poses an x-risk specifically.
Did you or anyone else ever write what aligning a system like GPT-3 looks like? I have to admit that it’s hard for me to even have a definition of being (intent) aligned for a system GPT-3, which is not really an agent on its own. How do you define or measure something like this?
Thanks for posting this!
Here is a link to the full report: The Oxford Principles for Net Zero Aligned Carbon Offsetting(I think it’s a good practice to include a link to the original reference when possible.)
Quick question—are these positions relevant as remote positions (not in the US)?
(I wrote this comment separately, because I think it will be interesting to a different, and probably smaller, group of people than the other one.)
Thank you for posting this, Paul. I have questions about two different aspects.
In the beginning of your post you suggest that this is “the real thing” and that these systems “could pose an existential risk if scaled up”.I personally, and I believe other members of the community, would like to learn more about your reasoning.In particular, do you think that GPT-3 specifically could pose an existential risk (for example if it falls into the wrong hands, or scaled up sufficiently)? If so, why, and what is a plausible mechanism by which it poses an x-risk?
On a different matter, what does aligning GPT-3 (or similar systems) mean for you concretely? What would the optimal result of your team’s work look like?(This question assumes that GPT-3 is indeed a “prosaic” AI system, and that we will not gain a fundamental understanding of intelligence by this work.)
At some point I tried to estimate this too and got similar results. This raised several of points:
I am not sure what the mortality cost of carbon actually measures:
I believe that the cost of additional ton of carbon depends on the amount of total carbon released already (for example in a 1C warming scenario, it is probably very different than in a 3.5C warming scenario).
The carbon and its effect will stay there and affect people for some unknown time (could be indefinitely, could be until we capture it, or until we got extinct, or some other option). This could highly alter the result, depending on the time span you use.
The solutions offered by top charities of GiveWell are highly scalable. I think the same can not be said about CATF, and perhaps about CfRN as well. Therefore, if you want to compare global dev to climate change, it might be better to compare to something which can absorb at least hundreds of millions of dollars yearly. (That said, it is of course still a fair comparison to compare CATF to a specific GiveWell recommended charity.)
The confidence interval you get (and that I got) is big. In your case it spans 2 order of magnitude, and this does not take into account the uncertainty in the mortality cost of carbon. I imagine that if we followed the previous point and used something larger for comparison, the $/carbon will have higher confidence. However, I believe that the first point at least indicates that the mortality cost of carbon will have a very large confidence interval.This is in contrast with the confidence interval in GiveWell’s estimates, which is (if I recall correctly) much narrower.
I would love to hear any responses to these points (in particular, I guess there are some concrete answers to the first point, which will also shed light on the confidence interval of mortality cost of carbon).
To conclude, I personally believe that climate change interventions could save lives at a cost similar to that of global dev interventions, but I also believe that the confidence interval for those will be much much higher.
I agree that it isn’t easy to quantify all of these.
Here is something you could do, which unfortunately does not take into account the changes in charities operation at different times, but is quite easy to do (all of the figures should be in real terms).
Choose a large interval of time (say 1900 to 2020), and at each point (say every month or year), decide how much you invest vs how much you donate, according to your strategy (and others).
Choose a model for how much money you have (for example, starting with a fixed amount, or say receiving a fixed amount every year, or receiving an amount depending on the return on investment in the previous year).
Sum up the total money donated over the course of that interval, and calculate how money you have in the end.
Then, you can compare for different strategies the two values at the end. You can also sum the total donated and the money left, pretending to donate everything left at the end of the interval. Or you could adjust your strategies such that no money is left at the end.
Thanks for posting this, this is very interesting.
Did you by any chance try to models this? It would be interesting for example to compare different strategies and how would they work given past data.
Thanks for writing this! I really like the way you write, which I found both fun and light and, at the same time, highlighting the important parts vividly. I too was surprised to learn that this is the version of utilitarianism Bentham had in his mind, and I find the views expressed in your summary (Ergo) lovely too.