If the balance of opinion of scientists and policymakers (or those who had briefly heard arguments) was that AI catastrophic risk is high, and that this should be a huge social priority, then you could do a lot of things. For example, you could get budgets of tens of billions of dollars for interpretability research, the way governments already provide tens of billions of dollars of subsidies to strengthen their chip industries. Top AI people would be applying to do safety research in huge numbers. People like Bill Gates and Elon Musk who nominally take AI risk seriously would be doing stuff about it, and Musk could have gotten more traction when he tried to make his case to government.

My perception based on many areas of experience is that policymakers and your AI expert survey respondents on the whole think that these risks are too speculative and not compelling enough to outweigh the gains from advancing AI rapidly (your survey respondents state those are much more likely than the harms). In particular, there is much more enthusiasm for the positive gains from AI than your payoff matrix suggests (particularly among AI researchers), and more mutual fear (e.g. the CCP does not wan... (read more)

Thank you, this is a good post.

My main point of disagreement is that you point to successful coordination in things like not eating sand, or not wearing weird clothing. The upside of these things is limited, but you say the upside of superintelligence is also limited because it could kill us.

But rephrase the question to "Should we create an AI that's 1% better than the current best AI?" Most of the time this goes well - you get prettier artwork or better protein folding prediction, and it doesn't kill you. So there's strong upside to building slightly better AIs, as long as you don't cross the "kills everyone" level. Which nobody knows the location of. And which (LW conventional wisdom says) most people will be wrong about.

We successfully coordinate a halt to AI advancement at the first point where more than half of the relevant coordination power agrees that the next 1% step forward is in expectation bad rather than good. But "relevant" is a tough qualifier, because if 99 labs think it's bad, and one lab thinks it's good, then unless there's some centralizing force, the one lab can go ahead and take the step. So "half the relevant coordination power" has to include either every la... (read more)

Katja, many thanks for writing this, and Oliver, thanks for this comment pointing out that everyday people are in fact worried about AI x-risk.  Since around 2017 when I left MIRI to rejoin academia, I have been trying continually to point out that everyday people are able to easily understand the case for AI x-risk, and that it's incorrect to assume the existence of AI x-risk can only be understood by a very small and select group of people.  My arguments have often been basically the same as yours here: in my case, informal conversations with Uber drivers, random academics, and people at random public social events.  Plus, the argument is very simple: If things are smarter than us, they can outsmart us and cause us trouble.  It's always seemed strange to say there's an "inferential gap" of substance here.

However, for some reason, the idea that people outside the LessWrong community might recognize the existence of AI x-risk — and therefore be worth coordinating with on the issue — has felt not only poorly received on LessWrong, but also fraught to even suggest.  For instance, I tried to point it out in this previous post:

“Pivotal Act” Intentions: Negative... (read more)

Survey about this question (I have a hypothesis, but I don't want to say what it is yet): https://forms.gle/1R74tPc7kUgqwd3GA

'Nuclear power' seems to me like a weird example because we selectively halted the development of productive use of nuclear power while having comparatively little standing in the way of development of destructive use of nuclear power. If a similar story holds, then we'll still see militarily relevant AIs (deliberately doing adversarial planning of the sort that could lead to human extinction) while not getting many of the benefits along the way.

That... doesn't seem like much of a coordination success story, to me.

The main concrete proposals / ideas that are mentioned here or I can think of are:

1. Work to spread good knowledge regarding AGI risk / doom stuff among AI researchers.
   1. I think everyone is in favor of this, particularly when it’s tied to the less-adversarial takeaway message “there is a big problem, and more safety research is desperately needed”. When it’s tied to the more-adversarial takeaway message “capabilities research should be slowed down”, I think that can be tactically bad, as I think people generally don’t tend to be receptive to people telling them that they and all their scientific idols are being reckless. I think it’s good to be honest and frank, but in the context of outreach, we can be strategic about what we emphasize, and I think emphasizing the “there is a big problem, and more safety research is desperately needed” message is generally the better approach. It also helps that the “there is a big problem, and more safety research is desperately needed” message is pretty clearly correct and easy to argue whereas “capabilities research is harmful” has comparatively more balanced arguments on both sides, and moreover if people can be sufficiently bought into the “there

you are underestimating the degree of unilateralist's curse driving ai progress by quite a bit. it looks like scaling is what does it, but that isn't actually true, the biggest-deal capabilities improvements come from basic algorithms research that is somewhat serially bottlenecked until we improve on a scaling law, and then the scaling is what makes a difference. progress towards dethroning google by strengthening underlying algorithms until they work on individual machines has been swift, and the next generations of advanced basic algorithms are already here, eg https://github.com/BlinkDL/RWKV-LM - most likely, a single 3090 can train a GPT3 level model in a practical amount of time. the illusion that only large agents can train ai is a falsehood resulting from how much easier the current generation of models are to train than previous ones, such that simply scaling them up works at all. but it is incredibly inefficient - transformers are a bad architecture, and the next things after them are shockingly stronger. if your safety plan doesn't take this into account, it won't work.

that said - there's no reason to think we're doomed just because we can't slow down. we need simply speed up safety until it has caught up with the leading edge of capabilities. safety should always be thinking first about how to make the very strongest model's architecture fit in with existing attempts at safety, and safety should take care not to overfit on individual model architectures.

My experience in talking about slowing down AI with people is that they seem to have a can’t do attitude. They don’t want it to be a reasonable course: they want to write it off.

There are a lot of really good reasons why someone would avoid touching the concept of "suppressing AI research" with a ten-foot pole; depending on who they are and where they work, it's tantamount to advocating for treason. Literal treason, with some people. It's the kind of thing where merely associating with people who advocate for it can cost you your job, and certainly promotions.

A lot of this is considered infohazardous, and frankly, I've already said too much here. But it's very legitimate, and even sensible, to have very, very strong and unquestioned misgivings about large numbers of people they're associated with being persuaded to do something as radical as playing a zero-sum game against the entire AI industry.

There are things that are robustly good in the world, and things that are good on highly specific inside-view models and terrible if those models are wrong. Slowing dangerous tech development seems like the former, whereas forwarding arms races for dangerous tech between world superpowers seems more like the latter.

It may seem the opposite to some people. For instance, my impression is that for many adjacent to the US government, "being ahead of China in every technology" would be widely considered robustly good, and nobody would question you at all if you said that was robustly good. Under this perspective the idea that AI could pose an existential risk is a "highly specific inside-view model" and it would be terrible if we acted on the model and it is wrong.

I don't think your readers will mostly think this, but I actually think a lot of people would, which for me makes this particular argument seem entirely subjective and thus suspect.

Thanks for writing!

I want to push back a bit on the framing used here. Instead of the framing "slowing down AI," another framing we could use is, "lay the groundwork for slowing down in the future, when extra time is most needed." I prefer this latter framing/emphasis because:

• An extra year in which the AI safety field has access to pretty advanced AI capabilities seems much more valuable for the field's progress (say, maybe 10x) than an extra year with current AI capabilities, since the former type of year would give the field much better opportunities to test safety ideas and more clarity about what types of AI systems are relevant.
  • One counterargument is that AI safety will likely be bottlenecked by serial time, because discarding bad theories and formulating better ones takes serial time, making extra years early on very useful. But my very spotty understanding of the history of science suggests that it doesn't just take time for bad theories to get replaced by better ones--it takes time along with the accumulation of lots of empirical evidence. This supports the view that late-stage time is much more valuable than early-stage time.
• Slowing down in the future seems much mor

I’ve copied over and lightly edited some comments I left on a draft. Note I haven’t reread the post in detail; sorry if these were addressed somewhere.

Writing down quick thoughts after reading the intro and before reading the rest:

I have two major reasons to be skeptical of actively slowing down AI (setting aside feasibility):

1. It makes it easier for a future misaligned AI to take over by increasing overhangs, both via compute progress and algorithmic efficiency progress. (This is basically the same sort of argument as "Every 18 months, the minimum IQ necessary to destroy the world drops by one point.")

2. Such strategies are likely to disproportionately penalize safety-conscious actors.

(As a concrete example of (2), if you build public support, maybe the public calls for compute restrictions on AGI companies and this ends up binding the companies with AGI safety teams but not the various AI companies that are skeptical of “AGI” and “AI x-risk” and say they are just building powerful AI tools without calling it AGI.)

For me personally there's a third reason, which is that (to first approximation) I have a limited amount of resources and it seems better to spend that on the "use good... (read more)

The conversation near me over the years has felt a bit like this: 

Some people: AI might kill everyone. We should design a godlike super-AI of perfect goodness to prevent that.

Others: wow that sounds extremely ambitious

Some people: yeah but it’s very important and also we are extremely smart so idk it could work

[Work on it for a decade and a half]

 

Some people: ok that’s pretty hard, we give up

Others: oh huh shouldn’t we maybe try to stop the building of this dangerous AI? 

Some people: hmm, that would involve coordinating numerous people—we may be arrogant enough to think that we might build a god-machine that can take over the world and remake it as a paradise, but we aren’t delusional

There's a sleight of hand going on with the "we" here. "We" as in LessWrong are not building the godlike AI, the trillion dollar technocapitalist machine is doing that. "We" are a bunch of nerds off to the side, some of whom are researching ways to point said AIs at specific targets. If "we" tried to start an AGI company we'd indeed end up in fifth place and speed up timelines by six weeks (generously).

I think there's a major internal tension in the picture you present (though the tension is only there with further assumptions). You write:

Obstruction doesn’t need discernment

[...]
I don’t buy it. If all you want is to slow down a broad area of activity, my guess is that ignorant regulations do just fine at that every day (usually unintentionally). In particular, my impression is that if you mess up regulating things, a usual outcome is that many things are randomly slower than hoped. If you wanted to speed a specific thing up, that’s a very different story, and might require understanding the thing in question.

The same goes for social opposition. Nobody need understand the details of how genetic engineering works for its ascendancy to be seriously impaired by people not liking it. Maybe by their lights it still isn’t optimally undermined yet, but just not liking anything in the vicinity does go a long way.

And you write:

Technological choice is not luddism

Some technologies are better than others [citation not needed]. The best pro-technology visions should disproportionately involve awesome technologies and avoid shitty technologies, I claim. If you think AGI is highly likely to dest

The world has a lot of experience slowing down technological progress already, no need to invent new ways. 

Fusion research slowed to a crawl in the 1970s and so we don't have fusion power. Electric cars have been delayed by a century. IRB is successful at preventing many promising avenues. The FDA/CDC killed most of the novel drug and pandemic prevention research. The space industry is only now catching up to where it was 50 years ago. 

Basically, stigma/cost/red tape reliably and provably does the trick. 

The world has actually coordinated on some impressive things, e.g. nuclear non-proliferation

Given the success of North Korea, I am both impressed by the world's coordination on nuclear weapon and depressed that even the impressive coordination is not enough. I feel similarly for the topic the nuclear weapon is metaphor for.

Continuing to think about the metaphor: in terms of existential risk, North Korean nuclear weapon is probably less damaging than United States missile defense, due to reasons related to second strike. It is probable people feel bad about... (read more)

Your own examples of technologies that aren't currently pursued but have huge upsides are a strong case against this proposition. These lines of research have some risks, but if there was sufficient funding and coordination, they could be tremendously valuable. Yet the status quo is to simply ban them without investing much at all in building a safe infrastructure to pursue them.

If you should succeed in achieving the political will needed to "slow down tech," it will come from idiots, fundamentalists, people with useless jobs, etc. It will not be a coaliti... (read more)

Someone used CRISPR on babies in China

Nitpick: the CRISPR was on the embryos, not the babies.

Although I don't think what He Jiankui did was scientifically justified (the benefit of HIV resistance wasn't worth the risk – he should have chosen PCSK9 instead of CCR5), I think the current norms against human genetic enhancement really are stifling a lot of progress. 

Broadly agree, but: I think we're very confused about the social situation. (Again, I agree this argues to work on deconfusing, not to give up!) For example, one interpretation of the propositions claimed in this thread

https://twitter.com/soniajoseph_/status/1597735163936768000 

if  they are true, is that AI being dangerous is more powerful in terms of moving money and problem-solving juice as a recruitment tool rather than a dissuasion tool. I.e. in certain contexts, it's beneficial towards the goal of getting funding to include in your pitch "th... (read more)

I see one critical flaw here.

Why does anyone assume ANY progress will be made on alignment if we don't have potentially dangerous AGIs in existence to experiment with?

A second issue is that at least the current model for chatGPT REQUIRES human feedback to get smarter, and the greater the scale of userbase the smarter it can potentially become.

Other systems designed to scale to AGI may have to be trained this way: initial training from test environments and static human text, but refinement from interaction with live humans, where the company with the most ... (read more)

I believe this post is one of the best to grace the front page of the LW forum this year. It provides a reasoned counterargument to prevailing wisdom in an accessible way, and has the potential to significantly update views. I would strongly support pinning this post to the front page to increase critical engagement.