Before someone points this out: Non-disclosure-by-default is a negative incentive for the academic side, if they care about publication metrics.
It is not a negative incentive for Conjecture in such an arrangement, at least not in an obvious way.
Daniel Paleka
Karma: 231
I do not think the ratio of the “AI solves hardest problem” and “AI has Gold” probabilities is right here. Paul was at the IMO in 2008, but he might have forgotten some details...
(My qualifications here: high IMO Silver in 2016, but more importantly I was a Jury member on the Romanian Master of Mathematics recently. The RMM is considered the harder version of the IMO, and shares a good part of the Problem Selection Committee with it.)
The IMO Jury does not consider “bashability” of problems as a decision factor, in the regime where the bashing would take good contestants more than a few hours. But for a dedicated bashing program, it makes no difference.
It is extremely likely that an “AI” solving most IMO geometry problems is possible today—the main difficulty being converting the text into an algebraic statement. Given that, polynomial system solvers should easily tackle such problems.
Say the order of the problems is (Day 1: CNG, Day 2: GAC). The geometry solver gives you 14 points. For a chance on IMO Gold, you have to solve the easiest combinatorics problem, plus one of either algebra or number theory.
Given the recent progress on coding problems as in AlphaCode, I place over 50% probability on IMO #1/#4 combinatorics problems being solvable by 2024. If that turns out to be true, then the “AI has Gold” event becomes “AI solves a medium N or a medium A problem, or both if contestants find them easy”.Now, as noted elsewhere in the thread, there are various types of N and A problems that we might consider “easy” for an AI. Several IMOs in the last ten years contain those.
In 2015, the easiest five problems consisted out of: two bashable G problems (#3, #4), an easy C (#1), a diophantine equation N (#2) and a functional equation A (#5). Given such a problemset, a dedicated AI might be able to score 35 points, without having capabilities remotely enough to tackle the combinatorics #6.
The only way the Gold probability could be comparable to “hardest problem” probability is if the bet only takes general problem-solving models into account. Otherwise, inductive bias one could build into such a model (e.g. resort to a dedicated diophantine equation solver) helps much more in one than in the other.
My SERI MATS Application
They test on the basic (Poziom podstawowy) Matura tier for testing on math problems.
In countries with Matura-based education, the basic tier math test is not usually taken by mathematically inclined students—it is just the law that anyone going to a public university has to pass some sort of math exam beforehand. Students who want to study anything where mathematics skills are needed would take the higher tier (Poziom rozszezony).
Can someone from Poland confirm this?
A quick estimate of the percentage of high-school students taking the Polish Matura exams is 50%-75%, though. If the number of students taking the higher tier is not too large, then average performance on the basic tier corresponds to essentially average human-level performance on this kind of test.
Note that many students taking the basic math exam only want to pass and not necessarily perform well; and some of the bottom half of the 270k students are taking the exam for the second or third time after failing before.
I am eager to see how the mentioned topics connect in the end—this is like the first few chapters in a book, reading the backstories of the characters which are yet to meet.
On the interpretability side—I’m curious how you do causal mediation analysis on anything resembling “values”? The ROME paper framework shows where the model recalls “properties of an object” in the computation graph, but it’s a long way from that to editing out reward proxies from the model.
The context window will still be much smaller than human; that is, single-run performance on summarization of full-length books will be much lower than of <=1e4 token essays, no matter the inherent complexity of the text.
Braver prediction, weak confidence: there will be no straightforward method to use multiple runs to effectively extend the context window in the three months after the release of GPT-4.
I think the timelines (as in, <10 years vs 10-30 years) are very correlated with the answer to “will first dangerous models look like current models”, which I think matters more for research directions than what you allow in the second paragraph.
For example, interpretability in transformers might completely fail on some other architectures, for reasons that have nothing to do with deception. The only insight from the 2022 Anthropic interpretability papers I see having a chance of generalizing to non-transformers is the superposition hypothesis / SoLU discussion.
because LW/AF do not have established standards of rigor like ML, they end up operating more like a less-functional social science field, where (I’ve heard) trends, personality, and celebrity play an outsized role in determining which research is valorized by the field.
In addition, the AI x-safety field is now rapidly expanding.
There is a huge amount of status to be collected by publishing quickly and claiming large contributions.
In the absence of rigor and metrics, the incentives are towards:
- setting new research directions, and inventing new cool terminology;
- using mathematics in a way that impresses, but is too low-level to yield a useful claim;
- and vice versa, relying too much on complex philosophical insights without empirical work;
- getting approval from alignment research insiders.
See also the now ancient Troubling Trends in Machine Learning Scholarship.
I expect the LW/AF community microcosm will soon reproduce many of of those failures.
On the other hand, the current community believes that getting AI x-safety right is the most important research question of all time. Most people would not publish something just for their career advancement, if it meant sucking oxygen from more promising research directions.
This might be a mitigating factor for my comment above. I am curious about what happened research fields which had “change/save the world’ vibes. Was environmental science immune to similar issues?
I am very sorry that you feel this way. I think it is completely fine for you, or anyone else, to have internal conflicts about your career or purpose. I hope you find a solution to your troubles in the following months.
Moreover, I think you did an useful thing, raising awareness about some important points:“The amount of funding in 2022 exceeded the total cost of useful funding opportunities in 2022.”
“Being used to do everything in Berkeley, on a high budget, is strongly suboptimal in case of sudden funding constraints.”
“Why don’t we spend less money and donate the rest?”
Epistemic status for what follows: medium-high for the factual claims, low for the claims about potential bad optics. It might be that I’m worrying about nothing here.
However, I do not think this place should be welcoming of posts displaying bad rhetoric and epistemic practices.Posts like this can hurt hurt the optics of the research done in the LW/AF extended universe. What does a prospective AI x-safety researcher think when they get referred to this site and see this post above several alignment research posts?
EDIT: The above paragraph was off. See Ben’s excellent reply for a better explanation of why anyone should care.
I think this place should be careful about maintaining:the epistemic standard of talking about falsifiable things;
the accepted rhetoric being fundamentally honest and straightforward, and always asking “compared to what?” before making claims;
the aversion to present uncertainties as facts.
For some examples:
My hotel room had the nightly price written on the inside of the door: $500. Shortly afterwards, I found out that the EA-adjacent community had bought the entire hotel complex.
I tried for 15 minutes to find a good faith reading of this, but I could not.
Most people would read this as “the hotel room costs $500 and the EA-adjacent community bought the hotel complex in which that hotel is a part of”, while being written in a way that only insinuates and does not commit to meaning exactly that. Insinuating bad optics facts while maintaining plausible deniability, without checking the facts, is a horrible practice, usually employed by politicians and journalists.
The poster does not deliberately lie, but this is not enough when making a “very bad optics” statement that sounds like this one. At any point, they could have asked for the actual price of the hotel room, or about the condition of the actual hotel that might be bought.I have never felt so obliged, so unpressured. If I produce nothing, before Christmas, then nothing bad will happen. Future funds will be denied, but no other punishment will ensue.
This is true. But it is not much different from working a normal software job. The worst thing that can happen is getting fired after not delivering for several months. Some people survive years coasting until there is a layoff round.
An important counterfactual for a lot of people reading this is a PhD degree.
There is no punishment for failing to produce good research, except getting dropping out of the program after a few years.
After a while I work out why: every penny I’ve pinched, every luxury I’ve denied myself, every financial sacrifice, is completely irrelevant in the face of the magnitude of this wealth. I expect I could have easily asked for an extra 20%, and received it.
This might be true. Again, I think it would be useful to ask: what is the counterfactual?
All of this is applicable for anyone that starts working for Google or Facebook, if they were poor beforehand.
This feeling (regretting saving and not spending money) is incredibly common in all people that have good careers.
I would suggest going through the post with a cold head and removing parts which are not up to the standards.
Again, I am very sorry that you feel like this.
I somewhat agree, athough I obviously put a bit less weight on your reason than you do. Maybe I should update my confidence of the importance of what I wrote to medium-high.
Let me raise the question of continuously rethinking incentives on LW/AF, for both Ben’s reason and my original reason.
The upvote/karma system does not seem like it incentivizes high epistemic standards and top-rigor posts, although I would need more datapoints to make a proper judgement.
Do you intend for the comments section to be a public forum on the papers you collect?
I definitely endorse reading the ROME paper, although the popular-culture claims about what the second part of the paper actually shows seem a bit overblown.
They do not seem to claim “changing facts in a generalizable way” (it’s likely not robust to synonyms at all)”. I am also vary of “editing just one MLP for a given fact” being the right solution, given that the causal tracing shows the fact being stored in several consecutive layers. Refer to a writeup by Thibodeau et al. sometime in the future.
That being said, if you are into interpretability, you have to at least skim the paper. It has a whole bunch of very cool stuff in it, from the causal tracing to the testing of whether making Eistein a physician changes the meaning of the word “physics” itself. Just don’t overfit on the methods there being exactly the methods that will solve interpretability of reasoning in transformers.
This is a mistake on my own part that actually changes the impact calculus, as most people looking into AI x-safety on this place will not actually ever see this post. Therefore, the “negative impact” section is retracted.[1] I point to Ben’s excellent comment for a correct interpretation of why we still care.
I do not know why I was not aware of this “block posts like this” feature, and I wonder if my experience of this forum was significantly more negative as a result of me accidentally clicking “Show Personal Blogposts” at some point. I did not even know that button existed.
No other part of my post is retracted. In fact, I’d like to reiterate a wish for the community to karma-enforce [2] the norms of:the epistemic standard of talking about falsifiable things;
the accepted rhetoric being fundamentally honest and straightforward, and always asking “compared to what?” before making claims;
the aversion to present uncertainties as facts.
Thank you for improving my user experience of this site!
Git Re-Basin: Merging Models modulo Permutation Symmetries [Ainsworth et al., 2022] and the cited The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks [Entezari et al., 2021] seem several years ahead.
I cannot independently verify that their claims about SGD are true, but the paper makes sense on the first glance.
Opinion:
Symmetries in NNs are a mainstream ML research area with lots of papers, and I don’t think doing research “from first principles” here will be productive. This also holds for many other alignment projects.
However I do think it makes sense as an alignment-positive research direction in general.
Let me first say I dislike the conflict-theoretic view presented in the “censorship bad” paragraph. On the short list of social media sites I visit daily, moderation creates a genuinely better experience. Automated censorship will become an increasingly important force for good as generative models start becoming more widespread.
Secondly, there is a danger of AI safety becoming less robust—or even optimising for deceptive alignment—in models using front-end censorship.[3]
This one is interesting, but only in the counterfactual: “if AI ethics technical research focused on actual value alignment of models as opposed to front-end censorship, this would have higher-order positive effects for AI x-safety”. But it doesn’t directly hurt AI x-safety research right now: we already work under the assumption that that output filtering is not a solution for x-risk.
It is clear improved technical research norms on AI non-x-risk safety can have positive effects on AI x-risk. If we could train a language model to robustly align to any set of human-defined values at all, this would be an improvement to the current situation.
But, there are other factors to consider. Is “making the model inherently non-racist” a better proxy for alignment than some other technical problems? Could interacting with that community weaken the epistemic norms in AI x-safety?
Calling content censorship “AI safety” (or even “bias reduction”) severely damages the reputation of actual, existential AI safety advocates.
I would need to significantly update my prior if this turns out to be a very important concern. Who are people, whose opinions will be relevant at some point, that understand both what AI non-x-safety and AI x-safety are about, dislike the former, are sympathetic to the latter, but conflate them?
I don’t know why it sent only the first sentence; I was drafting a comment on this. I wanted to delete it but I don’t know how.
EDIT: wrote the full comment now.
Cool results! Some of these are good student project ideas for courses and such.
The “Let’s think step by step” result about the Hindsight neglect submission to the Inverse Scaling Prize contest is a cool demonstration, but a few more experiments would be needed before we call it surprising. It’s kind of expected that breaking the pattern helps break the spurious correlation.
1. Does “Let’s think step by step” help when “Let’s think step by step” is added to all few-shot examples?
2. Is adding some random string instead of “Let’s think step by step” significantly worse?
Epistemic status: I’d give >10% on Metaculus resolving the following as conventional wisdom[1] in 2026.
Autoregressive-modeling-of-human-language capabilities are well-behaved, scaling laws can help us predict what happens, interpretability methods developed on smaller models scale up to larger ones, …
Models-learning-from-themselves have runaway potential, how a model changes after [more training / architecture changes / training setup modifications] is harder to predict than in models trained on 2022 datasets.
Replacing human-generated data with model-generated data was a mistake[2].
- ^
In the sense that e.g. chain of thought improves capabilities is conventional wisdom in 2022.
- ^
In the sense of x-safety. I have no confident insight either way on how abstaining from very large human-generated datasets influences capabilities long-term. If someone has, please refrain from discussing that publicly, of course.
There was an critical followup on Twitter, urelated to the instinctive Tromp-Taylor criticism[1]:
The failure of naive self play to produce unexploitable policies is textbook level material (Multiagent Systems, http://masfoundations.org/mas.pdf), and methods that produce less exploitable policies have been studied for decades.
and
Hopefully these pointers will help future researchers to address interesting new problems rather than empirically rediscovering known facts.
Reply by authors:
I can see why a MAS scholar would be unsurprised by this result. However, most ML experts we spoke to prior to this paper thought our attack would fail! We hope our results will motivate ML researchers to be more interested in the work on exploitability pioneered by MAS scholars.
...
Ultimately self-play continues to be a widely used method, with high-profile empirical successes such as AlphaZero and OpenAI Five. If even these success stories are so empirically vulnerable we think it’s important for their limitations to become established common knowledge.
My understanding is that the author’s position is reasonable for mainstream ML community standards; in particular there’s nothing wrong with the original tweet thread. “Self-play exploitable” is not new, but the practical demonstration of how easy it’s to do the exploit in Go engines is a new and interesting result.
I hope the “Related work” section gets fixed as soon as possible, though.
The question is at which level of scientific standards do we want alignment-adjacent work to be on. There are good arguments for aiming to be much better than mainstream ML research (which is very bad at not rediscovering prior work) in this respect, since the mere existence of a parallel alignment research universe by default biases towards rediscovery.
- ^
...which I feel is is not valid at all? If the policy was made aware of a weird rule in training, then it losing by this kind of rule is a valid adversarial example. For research purposes, it doesn’t matter what the “real” rules of Go are.
I don’t play Go, so don’t take this judgement for granted.
- ^
Do you ever plan on collaborating with researchers in academia, like DeepMind and Google Brain often do? What would make you accept or seek such external collaboration?