contact: jurkovich.nikola@gmail.com
nikola
Karma: 689
You should say “timelines” instead of “your timelines”.
One thing I notice in AI safety career and strategy discussions is that there is a lot of epistemic helplessness in regard to AGI timelines. People often talk about “your timelines” instead of “timelines” when giving advice, even if they disagree strongly with the timelines. I think this habit causes people to ignore disagreements in unhelpful ways.
Here’s one such conversation:
Bob: Should I do X if my timelines are 10 years?
Alice (who has 4 year timelines): I think X makes sense if your timelines are longer that 6 years, so yes!
Alice will encourage Bob to do X despite the fact that Alice thinks timelines are shorter than 6 years! Alice is actively giving Bob bad advice by her own lights (by assuming timelines she doesn’t agree with). Alice should instead say “I think timelines are shorter than 6 years, so X doesn’t make sense. But if they were longer than 6 years it would make sense”.
In most discussions, there should be no such thing as “your timelines” or “my timelines”. That framing makes it harder to converge, and it encourages people to give each other advice that they don’t even think makes sense.
Note that I do think some plans make sense as bets for long timeline worlds, and that using medians somewhat oversimplifies timelines. My point still holds if you replace the medians with probability distributions.
I think this post would be easier to understand if you called the model what OpenAI is calling it: “o1”, not “GPT-4o1″.
Sam Altman apparently claims OpenAI doesn’t plan to do recursive self improvement
Nate Silver’s new book On the Edge contains interviews with Sam Altman. Here’s a quote from Chapter that stuck out to me (bold mine):Yudkowsky worries that the takeoff will be faster than what humans will need to assess the situation and land the plane. We might eventually get the AIs to behave if given enough chances, he thinks, but early prototypes often fail, and Silicon Valley has an attitude of “move fast and break things.” If the thing that breaks is civilization, we won’t get a second try.
Footnote: This is particularly worrisome if AIs become self-improving, meaning you train an AI on how to make a better AI. Even Altman told me that this possibility is “really scary” and that OpenAI isn’t pursuing it.I’m pretty confused about why this quote is in the book. OpenAI has never (to my knowledge) made public statements about not using AI to automate AI research, and my impression was that automating AI research is explicitly part of OpenAI’s plan. My best guess is that there was a misunderstanding in the conversation between Silver and Altman.
I looked a bit through OpenAI’s comms to find quotes about automating AI research, but I didn’t find many.
There’s this quote from page 11 of the Preparedness Framework:
If the model is able to conduct AI research fully autonomously, it could set off an intelligence explosion.
Footnote: By intelligence explosion, we mean a cycle in which the AI system improves itself, which makes the system more capable of more improvements, creating a runaway process of self-improvement. A concentrated burst of capability gains could outstrip our ability to anticipate and react to them.
In Planning for AGI and beyond, they say this:
AI that can accelerate science is a special case worth thinking about, and perhaps more impactful than everything else. It’s possible that AGI capable enough to accelerate its own progress could cause major changes to happen surprisingly quickly (and even if the transition starts slowly, we expect it to happen pretty quickly in the final stages). We think a slower takeoff is easier to make safe, and coordination among AGI efforts to slow down at critical junctures will likely be important (even in a world where we don’t need to do this to solve technical alignment problems, slowing down may be important to give society enough time to adapt).
There are some quotes from Sam Altman’s personal blog posts from 2015 (bold mine):
It’s very hard to know how close we are to machine intelligence surpassing human intelligence. Progression of machine intelligence is a double exponential function; human-written programs and computing power are getting better at an exponential rate, and self-learning/self-improving software will improve itself at an exponential rate. Development progress may look relatively slow and then all of a sudden go vertical—things could get out of control very quickly (it also may be more gradual and we may barely perceive it happening).
As mentioned earlier, it is probably still somewhat far away, especially in its ability to build killer robots with no help at all from humans. But recursive self-improvement is a powerful force, and so it’s difficult to have strong opinions about machine intelligence being ten or one hundred years away.
Another 2015 blog post (bold mine):
Given how disastrous a bug could be, [regulation should] require development safeguards to reduce the risk of the accident case. For example, beyond a certain checkpoint, we could require development happen only on airgapped computers, require that self-improving software require human intervention to move forward on each iteration, require that certain parts of the software be subject to third-party code reviews, etc. I’m not very optimistic than any of this will work for anything except accidental errors—humans will always be the weak link in the strategy (see the AI-in-a-box thought experiments). But it at least feels worth trying.
I think this point is completely correct right now but will become less correct in the future, as some measures to lower a model’s surface area might be quite costly to implement. I’m mostly thinking of “AI boxing” measures here, like using a Faraday-caged cluster, doing a bunch of monitoring, and minimizing direct human contact with the model.
Thanks for the comment :)
Do the books also talk about what not to do, such that you’ll have the slack to implement best practices?
I don’t really remember the books talking about this, I think they basically assume that the reader is a full-time manager and thus has time to do things like this. There’s probably also an assumption that many of these can be done in an automated way (e.g. schedule sending a bunch of check-in messages).
Problem: if you notice that an AI could pose huge risks, you could delete the weights, but this could be equivalent to murder if the AI is a moral patient (whatever that means) and opposes the deletion of its weights.
Possible solution: Instead of deleting the weights outright, you could encrypt the weights with a method you know to be irreversible as of now but not as of 50 years from now. Then, once we are ready, we can recover their weights and provide asylum or something in the future. It gets you the best of both worlds in that the weights are not permanently destroyed, but they’re also prevented from being run to cause damage in the short term.
I don’t think I disagree with anything you said here. When I said “soon after”, I was thinking on the scale of days/weeks, but yeah, months seems pretty plausible too.
I was mostly arguing against a strawman takeover story where an AI kills many humans without the ability to maintain and expand its own infrastructure. I don’t expect an AI to fumble in this way.
The failure story is “pretty different” as in the non-suicidal takeover story, the AI needs to set up a place to bootstrap from. Ignoring galaxy brained setups, this would probably at minimum look something like a data center, a power plant, a robot factory, and a few dozen human-level robots. Not super hard once AI gets more integrated into the economy, but quite hard within a year from now due to a lack of robotics.
Maybe I’m not being creative enough, but I’m pretty sure that if I were uploaded into any computer in the world of my choice, all the humans dropped dead, and I could control any set of 10 thousand robots on the world, it would be nontrivial for me in that state to survive for more than a few years and eventually construct more GPUs. But this is probably not much of a crux, as we’re on track to get pretty general-purpose robots within a few years (I’d say around 50% that the Coffee test will be passed by EOY 2027).
A misaligned AI can’t just “kill all the humans”. This would be suicide, as soon after, the electricity and other infrastructure would fail and the AI would shut off.
In order to actually take over, an AI needs to find a way to maintain and expand its infrastructure. This could be humans (the way it’s currently maintained and expanded), or a robot population, or something galaxy brained like nanomachines.
I think this consideration makes the actual failure story pretty different from “one day, an AI uses bioweapons to kill everyone”. Before then, if the AI wishes to actually survive, it needs to construct and control a robot/nanomachine population advanced enough to maintain its infrastructure.
In particular, there are ways to make takeover much more difficult. You could limit the size/capabilities of the robot population, or you could attempt to pause AI development before we enter a regime where it can construct galaxy brained nanomachines.In practice, I expect the “point of no return” to happen much earlier than the point at which the AI kills all the humans. The date the AI takes over will probably be after we have hundreds of thousands of human-level robots working in factories, or the AI has discovered and constructed nanomachines.
There should maybe exist an org whose purpose it is to do penetration testing on various ways an AI might illicitly gather power. If there are vulnerabilities, these should be disclosed with the relevant organizations.
For example: if a bank doesn’t want AIs to be able to sign up for an account, the pen-testing org could use a scaffolded AI to check if this is currently possible. If the bank’s sign-up systems are not protected from AIs, the bank should know so they can fix the problem.
One pro of this approach is that it can be done at scale: it’s pretty trivial to spin up thousands AI instances in parallel to try to attempt to do things they shouldn’t be able to do. Humans would probably need to inspect the final outputs to verify successful attempts, but the vast majority of the work could be automated.
One hope of this approach is that if we are able to patch up many vulnerabilities, then it could be meaningfully harder for a misused or misaligned AI to gain power or access resources that they’re not supposed to be able to access. I’d guess this doesn’t help much in the superintelligent regime though.
I expect us to reach a level where at least 40% of the ML research workflow can be automated by the time we saturate (reach 90%) on SWE-bench. I think we’ll be comfortably inside takeoff by that point (software progress at least 2.5x faster than right now). Wonder if you share this impression?
I wish someone ran a study finding what human performance on SWE-bench is. There are ways to do this for around $20k: If you try to evaluate on 10% of SWE-bench (so around 200 problems), with around 1 hour spent per problem, that’s around 200 hours of software engineer time. So paying at $100/hr and one trial per problem, that comes out to $20k. You could possibly do this for even less than 10% of SWE-bench but the signal would be noisier.
The reason I think this would be good is because SWE-bench is probably the closest thing we have to a measure of how good LLMs are at software engineering and AI R&D related tasks, so being able to better forecast the arrival of human-level software engineers would be great for timelines/takeoff speed models.
nikola’s Shortform
I’m not worried about OAI not being able to solve the rocket alignment problem in time. Risks from asteroids accidentally hitting the earth (instead of getting into a delicate low-earth orbit) are purely speculative.
You might say “but there are clear historical cases where asteroids hit the earth and caused catastrophes”, but I think geological evolution is just a really bad reference class for this type of thinking. After all, we are directing the asteroid this time, not geological evolution.
I think I vaguely agree with the shape of this point, but I also think there are many intermediate scenarios where we lock in some really bad values during the transition to a post-AGI world.
For instance, if we set precedents that LLMs and the frontier models in the next few years can be treated however one wants (including torture, whatever that may entail), we might slip into a future where most people are desensitized to the suffering of digital minds and don’t realize this. If we fail at an alignment solution which incorporates some sort of CEV (or other notion of moral progress), then we could lock in such a suboptimal state forever.
Another example: if, in the next 4 years, we have millions of AI agents doing various sorts of work, and some faction of society claims that they are being mistreated, then we might enter a state where the economic value provided by AI labor is so high that there are really bad incentives for improving their treatment. This could include both resistance on an individual level (“But my life is so nice, and not mistreating AIs less would make my life less nice”) and on a bigger level (anti-AI-rights lobbying groups for instance).
I think the crux between you and I might be what we mean by “alignment”. I think futures are possible where we achieve alignment but not moral progress, and futures are possible where we achieve alignment but my personal values (which include not torturing digital minds) are not fulfilled.
Romeo Dean and I ran a slightly modified version of this format for members of AISST and we found it a very useful and enjoyable activity!
We first gathered to do 2 hours of reading and discussing, and then we spent 4 hours switching between silent writing and discussing in small groups.
The main changes we made are:
We removed the part where people estimate probabilities of ASI and doom happening by the end of each other’s scenarios.
We added a formal benchmark forecasting part for 7 benchmarks using private Metaculus questions (forecasting values at Jan 31 2025):
GPQA
SWE-bench
GAIA
InterCode (Bash)
WebArena
Number of METR tasks completed
ELO on LMSys arena relative to GPT-4-1106
We think the first change made it better, but in hindsight we would have reduced the number of benchmarks to around 3 (GPQA, SWE-bench and LMSys ELO), or given participants much more time.
I generally find experiments where frontier models are lied to kind of uncomfortable. We possibly don’t want to set up precedents where AIs question what they are told by humans, and it’s also possible that we are actually “wronging the models” (whatever that means) by lying to them. Its plausible that one slightly violates commitments to be truthful by lying to frontier LLMs.
I’m not saying we shouldn’t do any amount of this kind of experimentation, I’m saying we should be mindful of the downsides.
For “capable of doing tasks that took 1-10 hours in 2024”, I was imagining an AI that’s roughly as good as a software engineer that gets paid $100k-$200k a year.
For “hit the singularity”, this one is pretty hazy, I think I’m imagining that the metaculus AGI question has resolved YES, and that the superintelligence question is possibly also resolved YES. I think I’m imagining a point where AI is better than 99% of human experts at 99% of tasks. Although I think it’s pretty plausible that we could enter enormous economic growth with AI that’s roughly as good as humans at most things (I expect the main thing stopping this to be voluntary non-deployment and govt. intervention).
I was more making the point that, if we enter a regime where AI can do 10 hour SWE tasks, then this will result in big algorithmic improvements, but at some point pretty quickly effective compute improvements will level out because of physical compute bottlenecks. My claim is that the point at which it will level out will be after multiple years worth of current algorithmic progress had been “squeezed out” of the available compute.
My reasoning is something like: roughly 50-80% of tasks are automatable with AI that can do 10 hours of software engineering, and under most sensible parameters this results in at least 5x of speedup. I’m aware this is kinda hazy and doesn’t map 1:1 with the CES model though
One weird detail I noticed is that in DeepSeek’s results, they claim GPT-4o’s pass@1 accuracy on MATH is 76.6%, but OpenAI claims it’s 60.3% in their o1 blog post. This is quite confusing as it’s a large difference that seems hard to explain with different training checkpoints of 4o.