There seems to be a clear disanalogy in that if every individual stopped eating meat tomorrow, factory farming would be history very quickly. On the other hand, if everyone tried very hard to reduce their personal CO2 consumption, the effect seems more limited (unless people are really serious about it, in which case this would probably lead to major economic damage).
The key difference seems to be that CO2 emissions are embedded in our current societies and economies in such a deep way, that we can only get out via long-term investment into replacement infrastructure (such as renewables, electric cars, public transportation etc.), which is not necessarily influenced that strongly by individual consumption. On the other hand, meat eating is exclusively about the sum of personal demand, even though measures to reduce supply via policy or decrease demand via investment into viable substitutes would still be highly valuable. (I imagine that I might change my mind if this line of thought was convincingly refuted).
While I also disagree with the top level post, this seems overly hostile.
Thank you! 5% does sound very alarming to me, and is definitely a lot higher than I would have said at the beginning of the crisis (without having thought about it much, then).
Also, beyond the purely personal, are there any actions that could be taken by individuals right now that would have a positive impact on humanity’s chances to recover, conditional on nuclear war? Some (probably naive) ideas:
Downloading and printing vital information about how to rebuild food supply and other vital infrastructure (so that it can easily be accessed despite varying degrees of infrastructure collapse); I guess ALLFED’s articles might be a good starting point (even though I could not quickly find any distilled strategy document/user guide for their research)?
Increase the likelihood that you will be able to distribute this information: Ensure survival, build local networks, practice leadership skills etc.
Make sure others do the same: While there already seem to be a lot of preppers, I do not know whether their culture emphasizes strategies for rebuilding a flourishing civilization over mere survival. “Altruistic prepping” might be a relatively neglected niche (or not, these are just off the cuff thoughts...)
Putin seems to have ordered deterrence forces (which include nuclear arms) to be on high alert, roughly an hour ago. https://www.reuters.com/world/europe/biden-says-russian-attack-ukraine-unfolding-largely-predicted-2022-02-24/Can someone weigh in about how unprecedented this is? Some media coverage has compared the severity of the current situation to the Cuba Crisis, which would be extremely alarming if remotely true.
Miscalibration might cut both ways…
On one hand, It seems quite plausible for forecasts like this to usually be underconfident about the likelihood of the null event, but on the other hand recent events should probably have substantially increased forecasters’ entropy for questions around geopolitical events in the next few days and weeks.
(This risk is a greater risk than the risk of >50% of the public advocating for unethical policies out of self-interest, because in expectation, unethical policies in the self-interest of “>50% of the public” would be good for more people than unethical policies in the self-interest of experts)
This seems to have a bunch of hidden assumptions, including both about the relative capabilities of experts vs. the public to assess the effects of policies, as well as about the distribution of potential policies: While constitutions are not really a technocratic constraint on public opinion, one of their functions appears to be to protect minorities from a majority blatantly using policies to suppress them; in a world where the argument fully went through, this function would not be necessary.
The fact that ‘technocracy’ gets named so infrequently by EAs may be a sign that many are advocating for more technocracy without realising it or without realising that the term exists, along with pre-existing criticism of the idea.
While this might certainly be true, the negative connotations of the term “technocracy” might play an important role here as well: Someone who is aware of the concepts and its criticisms might nevertheless be prompted not to use the term in order to avoid knee-jerk reactions, similar to how someone arguing for more “populist” positions might not use that term, depending on the audience. While I am not sure I agree about the strong language regarding urgent priorities, and would also like to find more neutral terms for both sides, I agree that a better understanding of the balance between expert-driven policy and public opinion would be quite useful; I could imagine that which one is better can strongly depend on specific details of a particular policy problem, and that there might be ways of integrating parts of both sides productively: While I do think that Futarchy is unlikely to work, some form of “voting on values” and relying on expertise for predicting how policies would affect values still appears appealing, especially if experts’ incentives can be designed to clearly favor prediction accuracy, while avoiding issues with self-fulfilling prophecies.
Do you have thoughts on how potentially rising inflation could affect emission pathways and the relative cost of renweables? I have heard the argument that associated rises in the cost of capital could be pretty bad, because most costs associated with renewables are capital costs, while fuel costs dominate for fossil energy.
Huh? I did not like the double-page style for the non-mobile pdf, as it required some manual rescaling on my PC.
And the mobile version has the main table cut between two pages in a pretty horrible way. I think I would have much preferred a single pdf in the mobile/single page style that is actually optimized for that style, rather than this.
Maybe I should have used the HTML version instead?
More detailed action points on safety from page 32: The Office for AI will coordinate cross-government processes to accurately assess long term AI safety and risks, which will include activities such as evaluating technical expertise in government and the value of research infrastructure. Given the speed at which AI developments are impacting our world, it is also critical that the government takes a more precise and timely approach to monitoring progress on AI, and the government will work to do so.
The government will support the safe and ethical development of these technologies as well as using powers through the National Security & Investment Act to mitigate risks arising from a small number of potentially concerning actors. At a strategic level, the National Resilience Strategy will review our approach to emerging technologies; the Ministry of Defence will set out the details of the approaches by which Defence AI is developed and used; the National AI R&I Programme’s emphasis on AI theory will support safety; and central government will work with the national security apparatus to consider narrow and more general AI as a top-level security issue.
I don’t think I get your argument for why the approximation should not depend on the downstream task. Could you elaborate? I am also a bit confused about the relationship between spread and resiliency: a larger spread of forecasts does not seem to necessarily imply weaker evidence: It seems like for a relatively rare event about which some forecasters could acquire insider information, a large spread might give you stronger evidence.
Imagine E is about the future enactment of a quite unusual government policy, and one of your forecasters is a high ranking government official. Then, if all of your forecasters are relatively well calibrated and have sufficient incentive to report their true beliefs, a 90% forecast for E by the government official and a 1% forecast by everyone else should likely shift your beliefs a lot more towards E than a 10% forecast by everyone.
This seems to connect to the concept of—fmeans: If the utility for an option is proportional to f(p), then the expected utility of your mixture model is equal to the expected utility using the f-mean of the expert’s probabilities p1 and p2 defined as f−1(f(p1)+f(p2)2), as the f in the utility calculation cancels out the f−1. If I recall correctly, all aggregation functions that fulfill some technical conditions on a generalized mean can be written as a f-mean. In the first example, f is just linear, such that the f-mean is the arithmetic mean. In the second example, f is equal to the expected lifespan of 11−(1−p)=1p which yields the harmonic mean. As such, the geometric mean would correspond to the mixture model if and only if utility was logarithmic in p, as the geometric mean is the f-mean corresponding to the logarithm.
For a binary event with “true” probability q, the expected log-score for a forecast of p is qlog(p)∗(1−q)log(1−p)=log(pq(1−p)1−q), which equals log(√p1−p)=0.5log(p1−p) for q=0.5. So the geometric mean of odds would optimize yield the correct utility for the log-score according to the mixture model, if all the events we forecast were essentially coin tosses (which seems like a less satisfying synthesis than I hoped for).
Further questions that might be interesting to analyze from this point of view:
Is there some kind of approximate connection between the Brier score and the geometric mean of odds that could explain the empirical performance of the geometric mean on the Brier score? (There might very well not be anything, as the mixture model might not be the best way to think about aggregation).
What optimization target (under the mixture model) does extremization correspond to? Edit: As extremization is applied after the aggregation, it cannot be interpreted in terms of mixture models (if all forecasters give the same prediction, any f-mean has to have that value, but extremization yields a more extreme prediction.)
Note: After writing this, I noticed that UnexpectedValue’s comment on the top-level post essentially points to the same concept. I decided to still post this, as it seems more accessible than their technical paper while (probably) capturing the key insight.
Edit: Replaced “optimize” by “yield the correct utility for” in the third paragraph.
I wanted to flag that many PhD programs in Europe might require you to have a Master’s degree, or to essentially complete the coursework for Master’s degree during your PhD (as seems to be the case in the US), depending on the kind of undergraduate degree you hold. Obviously, the arguments regarding funding might still partially hold in that case.
Do you have a specific definition of AI Safety in mind? From my (biased) point of view, it looks like large fractions of work that is explicitly branded “AI Safety” is done by people who are at least somewhat adjacent to the EA community. But this becomes a lot less true if you widen the definition to include all work that could be called “AI Safety” (so anything that could conceivably help with avoiding any kind of dangerous malfunction of AI systems, including small scale and easily fixable problems).
Relatedly, what is the likelihood that future iterations of the fellowship might be less US-centric, or include Visa sponsorship?
The job posting states:
“All participants must be eligible to work in the United States and willing to live in Washington, DC, for the duration of their fellowship. We are not able to sponsor US employment visas for participants; US permanent residents (green card holders) are eligible to apply, but fellows who are not US citizens may be ineligible for placements that require a security clearance.”
So my impression would be that it would be pretty difficult to participate for non-US citizens who do not already live in the US.
https://en.wikipedia.org/wiki/Technological_transitions might be relevant.
The Geels book cited in the article (Geels, F.W., 2005. Technological transitions and system innovations. Cheltenham: Edward Elgar Publishing.) has a bunch of interesting case studies I read a while ago and a (I think popular) framework for technological change, but I am not sure the framework is sufficiently precise to be very predictive (and thus empirically validatable).
I don’t have any particular sources on this, but the economic literature on the effects of regulation might be quite relevant. In particular, I do remember attending a lecture arguing that limited liability played an important role for innovation during the industrial revolution.
Facebook has at least experimented with using deep reinforcement learning to adjust its notifications according to https://arxiv.org/pdf/1811.00260.pdf . Depending on which exact features they used for the state space (i.e. if they are causally connected to preferences), the trained agent would at least theoretically have an incentive to change user’s preferences.
The fact that they use DQN rather than a bandit algorithm seems to suggest that what they are doing involves at least some short term planning, but the paper does not seem to analyze the experiments in much detail, so it is unclear whether they could have used a myopic bandit algorithm instead. Either way, seeing this made me update quite a bit towards being more concerned about the effect of recommender systems on preferences.
Depending on your intended audience, it might make sense to add more details for some of the proposals. For example, why is scenario planning a good idea compared to other methods of decision making? Is there a compelling story, or strong empirical evidence for its efficacy?
Some small nitpicks:
There seems to be a mistake here:
“Bostrom argues in The Fragile World Hypothesis that continuous technological development will increase systemic fragility, which can be a source of catastrophic or existential risk. In the Precipice, he estimates the chances of existential catastrophe within the next 100 years at one in six.”
I also find this passage a bit odd:
“One example of moral cluelessness is the repugnant conclusion, which assumes that by adding more people to the world, and proportionally staying above a given average in happiness, one can reach a state of minimal happiness for an infinitely large population.”
The repugnant conclusion might motivate someone to think about cluelessness, but it does not really seem to be an example of cluelessness (the question whether we should accept it might or might not be).