Depopulation is Bad
Population has soared from 1 to 8 billion in 200 years and is set to rise to 10. There is no depopulation, there is a population boom. That population boom is partially responsible for the climate crisis, biodiversity loss, and many other problems. It would be quite healthy to have a bit more moderate amounts of people.
If you’re right, I think that would point to xrisk space funders trusting individuals way too much and institutions way too little. Thomas is a great guy but one guy losing belief in his work (which happens all the time mostly for private reasons, and mostly independent of the actual meaning of the work) should never be a reason to defund an otherwise functioning org, doing seemingly crucial work.
If the alternative theory is correct and the hit pieces are to blame, that still seems like an incorrect decision. When you’re lobbying for something important you can expect some pushback, that shouldn’t be a reason to pull out immediately.
I love this post, I think this is a fundamental issue for intent-alignment. I don’t think value-alignment or CEV are any better though, mostly because they seem irreversible to me, and I don’t trust the wisdom of those implementing them (no person is up to that task).
I agree it would be good to I implement these recommendations, although I also think they might prove insufficient. As you say, this could be a reason to pause that might be easier to grasp by the public than misalignment. (I think currently, the reason some do not support a pause is perceived lack of capabilities though, not (mostly) perceived lack of misalignment).
I’m also worried about a coup, but I’m perhaps even more worried about the fate of everyone not represented by those who will have control over the intent-aligned takeover-level AI (IATLAI). If IATLAI is controlled by e.g. a tech CEO, this includes almost everyone. If controlled by government, even if there is no coup, this includes everyone outside that country. Since control over the world of IATLAI could be complete (way more intrusive than today) and permanent (for >billions of years), I think there’s a serious risk that everyone outside the IATLAI country does not make it eventually. As a data point, we can see how much empathy we currently have for citizens from starving or war-torn countries. It should therefore be in the interest of everyone who is on the menu, rather than at the table, to prevent IATLAI from happening, if capabilities awareness would be present. This means at least the world minus the leading AI country.
The only IATLAI control that may be acceptable to me, could be UN-controlled. I’m quite surprised that every startup is now developing AGI, but not the UN. Perhaps they should.
Thank you for the compliment, and interesting approach!
I’m aware and I don’t disagree. However, in xrisk, many (not all) of those who are most worried are also most bullish about capabilities. Reversely, many (not all) who are not worried are unimpressed with capabilities. Being aware of the concept of AGI, that it may be coming soon, and of how impactful it could be, is in practice often a first step towards becoming concerned about the risks, too. This is not true for everyone unfortunately. Still, I would say that at least for our chances to get an international treaty passed, it is perhaps hopeful that the power of AGI is on the radar of leading politicians (although this may also increase risk through other paths).
Otto Barten here, director of the Existential Risk Observatory.
We reduce AI existential risk by informing the public debate. Concretely, we do media work, organize events, do research, and give policy advice.
Currently, public awareness of AI existential risk among the US public is around 15% according to our measurements. Low problem awareness is a major reason why risk-reducing regulation such as SB-1047, or more ambitious federal or global proposals, do not get passed. Why solve a problem one does not see in the first place?
Therefore, we do media work to increase awareness of AI existential risk and propose helpful regulation. Today, we published our fourth piece in TIME Magazine, arguing AI is an existential risk and proposing the Conditional AI Safety Treaty. According to survey-based measurements (n=50 per media item), our ‘conversion rate’, measuring how many readers newly connect AI to human extinction after reading our articles, is between 34% and 50%, of which about half remains over time. We have published four TIME pieces and around 20 other media items in the last two years. Although we cannot cleanly separate media work from other work, we could estimate that $35k should get a funder roughly two leading media pieces, plus 10 supporting ones.
In addition to media work, we also organize events. Our track record contains four debates with leading existential risk voices such as Yoshua Bengio, Stuart Russell, Max Tegmark, and Jaan Tallinn on one hand, and journalists from e.g. TIME and The Economist and MPs on the other. Our events aim to inform leading voices of the societal debate and policymakers about existential risk and give experts the chance to propose helpful policy. We have organized events ahead of the AI Safety Summits in Bletchley Park, Korea/remote, and will do so again in Paris. These events have helped and will help to shape the summits’ narratives towards concern for existential risk. We can organize one event for around $20k, including venue costs, traveling/hotel costs, and organization hours.
We are also doing policy research. In the coming year, we will focus on what the optimal Conditional AI Safety Treaty should look like exactly, and how we can get it implemented. We are uniquely positioned to not only do leading research, but also communicate this directly to a large audience, including e.g. MPs and leading journalists. We are planning to write a paper on what the optimal shape should be for the Conditional AI Safety Treaty, working together with other institutes. We can produce such a paper for around $18k.
As an organization, we are heavily funding constrained. We have been supported by established funders such as SFF, LTFF, and ICFG in the past, but only for relatively modest amounts. Our current runway is therefore about five months. Additional funding would mostly enable us to keep doing what we are doing (and get even better at it!): media work, organizing events, and doing research. Within these three focus areas, we are also open to receiving earmarked funding, or additional funding to scale up our work.
For donations, best to contact us by email. Your support is much appreciated!
Thanks for your comment.
I changed the title, the original one came from TIME. Still, we do believe there is a solution to existential risk. What we want to do is outlining the contours of such a solution. A lot has to be filled in by others, including the crucial question of when to pause. We acknowledge this in the piece.
Nice study!
At first glance, results seem pretty similar to what we found earlier (https://www.existentialriskobservatory.org/papers_and_reports/Trends%20in%20Public%20Attitude%20Towards%20Existential%20Risk%20And%20Artificial%20Intelligence.pdf), giving confidence in both studies. The question you ask is the same as well, great for comparison! Your study seems a bit more extensive than what we did, which seems very useful.
Would be amazing to know whether a tipping point in awareness, according to (non xrisk) literature expected to occur somewhere between 10% and 25% awareness, will also occur for AI xrisk!
I sympathize with working on a topic you feel in your stomach. I worked on climate and switched to AI because I couldn’t get rid of a terrible feeling about humanity going to pieces without anyone really trying to solve the problem (~4 yrs ago, but I’d say this is still mostly true). If your stomach feeling is in climate instead, or animal welfare, or global poverty, I think there is a case to be made that you should be working in those fields, both because your effectiveness will be higher there and because it’s better for your own mental health, which is always important. I wouldn’t say this cannot be AI xrisk: I have this feeling about AI xrisk, and I think many eg. PauseAI activists and others do, too.
Skimmed it and mostly agree, thanks for writing. Especially takeover and which capabilities are needed for that is a crux for me, rather than human-level. Still, one realistically needs a shorthand for communication and AGI/human-level AI is time tested and understood relatively easily. For policy and other more advanced comms, and as more details become available on what capabilities are and aren’t important for takeover, making messaging more detailed is a good next step.
The recordings of our event are now online!
High impact startup idea: make a decent carbon emissions model for flights.
Current ones simply use flight emissions which makes direct flights look low-emission. But in reality, some of these flights wouldn’t even be there if people could be spread over existing indirect flights more efficiently, which is why they’re cheaper too. Emission models should be relative to counterfactual.
The startup can be for-profit. If you’re lucky, better models already exist in scientific literature. Ideal for the AI for good-crowd.
My guess is that a few man-years work could have a big carbon emissions impact here.
Great work, thanks a lot for doing this research! As you say, this is still very neglected. Also happy to see you’re citing our previous work on the topic. And interesting finding that fear is such a driver! A few questions:
- Could you share which three articles you’ve used? Perhaps this is in the dissertation, but I didn’t have the time to read that in full.
- Since it’s only one article per emotion (fear, hope, mixed), perhaps some other article property (other than emotion) could also have led to the difference you find?
- What follow-up research would you recommend?
- Is there anything orgs like ours (Existential Risk Observatory) (or, these days, MIRI, that also focuses on comms) should do differently?
As a side note, we’re conducting research right now on where awareness has gone after our first two measurements (that were 7% and 12% in early/mid ’23, respectively). We might also look into the existence and dynamics of a tipping point.
Again, great work, hope you’ll keep working in the field in the future!
Congratulations on a great prioritization!
Perhaps the research that we (Existential Risk Observatory) and others (e.g. @Nik Samoylov, @KoenSchoen) have done on effectively communicating AI xrisk, could be something to build on. Here’s our first paper and three blog posts (the second includes measurement of Eliezer’s TIME article effectiveness—its numbers are actually pretty good!). We’re currently working on a base rate public awareness update and further research.
Best of luck and we’d love to cooperate!
Recordings are now available!
Nice post! Yet another path to impact could be to influence international regulation processes, such as the AI Safety Summit, through influencing the EU and member states positions. In a positive scenario, the EU could even take a mediation role between the US and China.
It’s definitely good to think about whether a pause is a good idea. Together with Joep from PauseAI, I wrote down my thoughts on the topic here.
Since then, I have been thinking a bit on the pause and comparing it to a more frequently mentioned option, namely to apply model evaluations (evals) to see how dangerous a model is after training.
I think the difference between the supposedly more reasonable approach of evals and the supposedly more radical approach of a pause is actually smaller than it seems. Evals aim to detect dangerous capabilities. What will need to happen when those evals find that, indeed, a model has developed such capabilities? Then we’ll need to implement a pause. Evals or a pause is mostly a choice about timing, not a fundamentally different approach.
With evals, however, we’ll move precisely to the brink, look straight into the abyss, and then we plan to halt at the last possible moment. Unfortunately, though, we’re in thick mist and we can’t see the abyss (this is true even when we apply evals, since we don’t know which capabilities will prove existentially dangerous, and since an existential event may already occur before running the evals).
And even if we would know where to halt: we’ll need to make sure that the leading labs will practically succeed in pausing themselves (there may be thousands of people working there), that the models aren’t getting leaked, that we’ll implement the policy that’s needed, that we’ll sign international agreements, and that we gain support from the general public. This is all difficult work that will realistically take time.
Pausing isn’t as simple as pressing a button, it’s a social process. No one knowns how long that process of getting everyone on the same page will take, but it could be quite a while. Is it wise to start that process at the last possible moment, namely when the evals turn red? I don’t think so. The sooner we start, the higher our chance of survival.
Also, there’s a separate point that I think is not sufficiently addressed yet: we don’t know how to implement a pause beyond a few years duration. If hardware and algorithms improve, frontier models could democratize. While I believe this problem can be solved by international (peaceful) regulation, I also think this will be hard and we will need good plans (hardware or data regulation proposals) for how to do this in advance. We currently don’t have these, so I think working on them should be a much higher priority.
Thanks for the comment. I think the ways an aligned AGI could make the world safer against unaligned AGIs can be divided in two categories: preventing unaligned AGIs from coming into existence or stopping already existing unaligned AGIs from causing extinction. The second is the offense/defense balance. The first is what you point at.
If an AGI would prevent people from creating AI, this would likely be against their will. A state would be the only actor who could do so legally, assuming there is regulation in place, and also most practically. Therefore, I think your option falls under what I described in my post as “Types of AI (hardware) regulation may be possible where the state actors implementing the regulation are aided by aligned AIs”. I think this is indeed a realistic option and it may reduce existential risk somewhat. Getting the regulation in place at all, however, seems more important at this point than developing what I see as a pretty far-fetched and—at the moment—intractable way to implement it more effectively.
Hi Peter, thanks for your comment. We do think the conclusions we draw are robust based on our sample size. If course it depends on the signal: if there’s a change in e.g. awareness from 5% to 50%, a small sample size should be plenty to show that. However, if you’re trying to measure a signal of only 1% difference, your sample size should be much larger. While we stand by our conclusions, we do think there would be significant value in others doing similar research, if possible with larger sample sizes.
Again, thanks for your comments, we take the input into account.
Founder of the Existential Risk Observatory here. We’ve focused on informing the public about xrisk for the last four years. We mostly focused on traditional media, perhaps that’s a good addition to the social media work discussed here.
We also focused on measuring our impact from the beginning. Here are a few of our EA forum posts detailing AI xrisk comms effectiveness.
We did not only measure exposure, but also effectiveness of our interventions, using surveys. Our main metric was the conversion rate (called Human Extinction Events indicator in our first paper), basically the percentage of people who changed their mind about whether AI is an existential risk after being exposed to our media intervention. Our average persistent conversion rate was 22%. I think this methodology would also be suitable to apply to social media work (and we applied it to some youtube videos already—results in the links above).
Our total conversion was around 1.8 million people (spreadsheet here). Using engagement times of 57 sec for short-form articles and 123 sec for long-form ones, we yield an effectiveness rate of 254 minute/$ (uncorrected for quality). I do think our views estimates here, that are mostly based on circulation figures, may be on the high side. On the other hand, I’d say quality of e.g. TIME, SCMP, or NRC articles should be expected to be better than average youtube content, but there may be outliers, and this will remain, to an extent, a matter of taste.
I hope it’s useful to share these numbers and calculation methods publicly. I’m a big fan of trying to spend money on the most effective channels.
In the end, I think a strategy to reduce xrisk should hedge risks by not betting on one communication method only. I think it makes sense to spend some funding on the most effective social media work, some on the most effective traditional media work, and some on direct lobbying.