Interested in AI safety talent search and development.
Peter
Karma: 595
[Question] Has anyone done research on why Covid-19 didn’t become a bigger warning shot for future pandemic prevention?
So it seems like you’re saying there are at least two conditions: 1) someone with enough resources would have to want to release a frontier model with open weights, maybe Meta or a very large coalition of the opensource community if distributed training continues to scale, 2) it would need at least enough dangerous capability mitigations like unlearning and tamper resistant weights or cloud inference monitoring, or be behind the frontier enough so governments don’t try to stop it. Does that seem right? What do you think is the likely price range for AGI?
I’m not sure the government is moving fast enough or interested in trying to lock down the labs too much given it might slow them down more than it increases their lead or they don’t fully buy into risk arguments for now. I’m not sure what the key factors to watch here are. I expected reasoning systems next year, but it seems like even open weight ones were released this year that seem around o1 preview level just a few weeks after, indicating that multiple parties are pursuing similar lines of AI research somewhat independently.
This is a thoughtful post so it’s unfortunate it hasn’t gotten much engagement here. Do you have cruxes around the extent to which centralization is favorable or feasible? It seems like small models that could be run on a phone or laptop (~50GB) are becoming quite capable and decentralized training runs work for 10 billion parameter models which are close to that size range. I don’t know its exact size, but Gemini Flash 2.0 seems much better than I would have expected a model of that size to be in 2024.
Do you think there’s a way to tell the former group apart from people who are closer to your experience (hearing earlier would be beneficial)?
Over 4 billion people don’t have access to clean drinking water at home, more than 2x previous estimates
Interesting. People probably aren’t at peak productivity or even working at all for some part of those hours, so you could probably cut the hours by 1⁄4. This narrows the gap between what GPT2030 can achieve in a day and what all humans can together.
Assuming 9 billion people work 8 hours that’s ~8.22 million years of work in a day. But given slowdowns in productivity throughout the day we might want to round that down to ~6 million years.
Additionally, GPT2030 might be more effective than even the best human workers at their peak hours. If it’s 3x as good as a PhD student at learning, which it might be because of better retention and connections, it would be learning more than all PhD students in the world every day. The quality of its work might be 100x or 1000x better, which is difficult to compare abstractly. In some tasks like clearing rubble, more work time might easily translate into catching up on outcomes.
With things like scientific breakthroughs, more time might not result in equivalent breakthroughs. From that perspective, GPT2030 might end up doing more work than all of humanity since huge breakthroughs are uncommon.
This is a pretty interesting idea. I wonder if what we perceive as clumps of ‘dark matter’ might be or contain silent civilizations shrouded from interference.
Maybe there is some kind of defense dominant technology or strategy that we don’t yet comprehend.
Interesting post—I particularly appreciated the part about the impact of Szilard’s silence not really affecting Germany’s technological development. This was recently mentioned in Leopold Aschenbrenner’s manifesto as an analogy for why secrecy is important, but I guess it wasn’t that simple. I wonder how many other analogies are in there and elsewhere that don’t quite hold. Could be a useful analysis if anyone has the background or is interested.
Huh had no idea this existed
I think it’s good to critically interrogate this kind of analysis. I don’t want to discourage that. But as someone who publicly expressed skepticism about Flynn’s chances, I think there are several differences that mean it warrants closer consideration. The polls are much closer for this race, Biden is well known and experienced at winning campaigns, and the differences between the candidates in this race seem much larger. Based on that it at least seems a lot more reasonable to think Biden could win and that it will be a close race worth spending some effort on.
Interesting. Are there any examples of what we might consider a relatively small policy changes that received huge amounts of coverage? Like for something people normally wouldn’t care about. Maybe these would be informative to look at compared to more hot button issues like abortion that tend to get a lot of coverage. I’m also curious if any big issues somehow got less attention than expected and how this looks for pass/fail margins compared to other states where they got more attention. There are probably some ways to estimate this that are better than others.
I see.
I was interpreting it as “a referendum increases the likelihood of the policy existing later.” My question is about the assumptions that lead to this view and the idea that it might be more effective to run a campaign for a policy ballot initiative once and never again. Is this estimate of the referendum effect only for the exact same policy (maybe an education tax but the percent is slightly higher or lower) or similar policies (a fee or a subsidy or voucher or something even more different)? How similar do they have to be? What is the most different policy that existed later that you think would still count?
“Something relevant to EAs that I don’t focus on in the paper is how to think about the effect of campaigning for a policy given that I focus on the effect of passing one conditional on its being proposed. It turns out there’s a method (Cellini et al. 2010) for backing this out if we assume that the effect of passing a referendum on whether the policy is in place later is the same on your first try is the same as on your Nth try. Using this method yields an estimate of the effect of running a successful campaign on later policy of around 60% (Appendix Figure D20).
I’d be curious to hear about potential plans to address any of these, especially talent development and developing the pipeline of AI safety and governance.
Very interesting.
1. Did you notice an effect of how large/ambitious the ballot initiative was? I remember previous research suggesting consecutive piecemeal initiatives were more successful at creating larger change than singular large ballot initiatives.
2. Do you know how much the results vary by state?
3. How different do ballot initiatives need to be for the huge first advocacy effect to take place? Does this work as long as the policies are not identical or is it more of a cause specific function or something in between? Does it have a smooth gradient or is it discontinuous after some tipping point?
This is an inspiring amount of research. I really appreciate it and am enjoying reading it.
That’s a good point. Although 1) if people leave a company to go to one that prioritizes AI safety, then this means there are fewer workers at all the other companies who feel as strongly. So a union is less likely to improve safety there. 2) It’s common for workers to take action to improve safety conditions for them, and much less common for them to take action on issues that don’t directly affect their work, such as air pollution or carbon pollution, and 3) if safety inclined people become tagged as wanting to just generally slow down the company, then hiring teams will likely start filtering out many of the most safety minded people.
I’ve thought about this before and talked to a couple people in labs about it. I’m pretty uncertain if it would actually be positive. It seems possible that most ML researchers and engineers might want AI development to go as quickly or more than leadership if they’re excited about working on cutting edge technologies or changing the world or for equity reasons. I remember some articles about how people left Google for companies like OpenAI because they thought Google was too slow, cautious, and lost its “move fast and break things” ethos.
Really appreciate this post. Recently I’ve felt less certain about whether slowing down AI is feasible or helpful in the near future.
I think how productive current alignment and related research is at the moment is a key crux for me. If it’s actually quite valuable at the moment, maybe having more time would seem better.
It does seem easier to centralize now when there are fewer labs and entrenched ways of doing things, though it’s possible that exponentially rising costs could lead to centralization through market dynamics anyway. Though maybe that would be short lived and some breakthrough after would change the cost of training dramatically.
Hmm maybe it could still be good to try things in case timelines are a bit longer or an unexpected opportunity arises? For example, what if you thought it was 2 years but actually 3-5?