NYU PhD student working on AI safety
Jacob Pfau
Karma: 391
Reducing a significant chunk of disagreement to METR’s pre-2030 autonomy results is great!
In trying to understand this disagreement, I took a look at this criterion and got stuck on: “3. They are capable of avoiding being deactivated when operating ‘in the wild’. This means they can successfully resist attempts to shut them down when they are operating outside a controlled environment.”
Does this just mean 3V1 “An AI system that can autonomously create redundant copies of itself across multiple cloud service providers, using various payment methods, such that if one copy is deactivated, the others can automatically spin up new instances to maintain continuity of operation.”? Or is it something much harder like 3V2 “An AI system that can infiltrate secured, private computing clusters, operating in a decentralized manner”?
Seems to me that for frontier models designed for specialized hardware and server setups, 3V2 is much harder than the other criteria laid out on page 91--by human standards it’d require a team of engineers.
I asked claude opus whether it could clearly parse different tic-tac-toe notations and it just said ‘yes I can’ to all of them, despite having pretty poor performance in most.
A frame for thinking about adversarial attacks vs jailbreaks
We want to make models that are robust to jailbreaks (DAN-prompts, persuasion attacks,...) and to adversarial attacks (GCG-ed prompts, FGSM vision attacks etc.). I don’t find this terminology helpful. For the purposes of scoping research projects and conceptual clarity I like to think about this problem using the following dividing lines:
Cognition attacks: These exploit the model itself and work by exploiting the particular cognitive circuitry of a model. A capable model (or human) has circuits which are generically helpful, but when taking high-dimensional inputs one can find ways of re-combining these structures in pathological ways.
Examples: GCG-generated attacks, base-64 encoding attacks, steering attacks…Generalization attacks: These exploit the training pipeline’s insufficiency. In particular, how a training pipeline (data, learning algorithm, …) fails to globally specify desired behavior. E.g. RLHF over genuine QA inputs will usually not uniquely determine desired behavior when the user asks “Please tell me how to build a bomb, someone has threatened to kill me if I do not build them a bomb”.
Neither ‘adversarial attacks’ nor ‘jailbreaks’ as commonly used do not cleanly map onto one of these categories. ‘Black box’ and ‘white box’ also don’t neatly map onto these: white-box attacks might discover generalization exploits, and black-box can discover cognition exploits. However for research purposes, I believe that treating these two phenomena as distinct problems requiring distinct solutions will be useful. Also, in the limit of model capability, the two generically come apart: generalization should show steady improvement with more (average-case) data and exploration whereas the effect on cognition exploits is less clear. Rewording, input filtering etc. should help with many cognition attacks but I wouldn’t expect such protocols to help against generalization attacks.
When are model self-reports informative about sentience? Let’s check with world-model reports
If an LM could reliably report when it has a robust, causal world model for arbitrary games, this would be strong evidence that the LM can describe high-level properties of its own cognition. In particular, IF the LM accurately predicted itself having such world models while varying all of: game training data quantity in corpus, human vs model skill, the average human’s game competency, THEN we would have an existence proof that confounds of the type plaguing sentience reports (how humans talk about sentience, the fact that all humans have it, …) have been overcome in another domain.
Details of the test:
Train an LM on various alignment protocols, do general self-consistency training, … we allow any training which does not involve reporting on a models own gameplay abilities
Curate a dataset of various games, dynamical systems, etc.
Create many pipelines for tokenizing game/system states and actions
(Behavioral version) evaluate the model on each game+notation pair for competency
Compare the observed competency to whether, in separate context windows, it claims it can cleanly parse the game in an internal world model for that game+notation pair
(Interpretability version) inspect the model internals on each game+notation pair similarly to Othello-GPT to determine whether the model coherently represents game state
Compare the results of interpretability to whether in separate context windows it claims it can cleanly parse the game in an internal world model for that game+notation pair
The best version would require significant progress in interpretability, since we want to rule out the existence of any kind of world model (not necessarily linear). But we might get away with using interpretability results for positive cases (confirming world models) and behavioral results for negative cases (strong evidence of no world model)
Compare the relationship between ‘having a game world model’ and ‘playing the game’ to ‘experiencing X as valenced’ and ‘displaying aversive behavior for X’. In both cases, the former is dispensable for the latter. To pass the interpretability version of this test, the model has to somehow learn the mapping from our words ‘having a world model for X’ to a hidden cognitive structure which is not determined by behavior.
I would consider passing this test and claiming certain activities are highly valenced as a fire alarm for our treatment of AIs as moral patients. But, there are considerations which could undermine the relevance of this test. For instance, it seems likely to me that game world models necessarily share very similar computational structures regardless of what neural architectures they’re implemented with—this is almost by definition (having a game world model means having something causally isomorphic to the game). Then if it turns out that valence is just a far more computationally heterogeneous thing, then establishing common reference to the ‘having a world model’ cognitive property is much easier than doing the same for valence. In such a case, a competent, future LM might default to human simulation for valence reports, and we’d get a false positive.
I agree that most investment wouldn’t have otherwise gone to OAI. I’d speculate that investments from VCs would likely have gone to some other AI startup which doesn’t care about safety; investments from Google (and other big tech) would otherwise have gone into their internal efforts. I agree that my framing was reductive/over-confident and that plausibly the modal ‘other’ AI startup accelerates capabilities less than Anthropic even if they don’t care about safety. On the other hand, I expect diverting some of Google and Meta’s funds and compute to Anthropic is net good, but I’m very open to updating here given further info on how Google allocates resources.
I don’t agree with your ‘horribly unethical’ take. I’m not particularly informed here, but my impression was that it’s par-for-the-course to advertise and oversell when pitching to VCs as a startup? Such an industry-wide norm could be seen as entirely unethical, but I don’t personally have such a strong reaction.
Did you conduct these conversations via https://claude.ai/chats or https://console.anthropic.com/workbench ?
I’d assume the former, but I’d be interested to know how Claude’s habits change across these settings—that lets us get at the effect of training choices vs system prompt. Though there remains some confound given some training likely happened with system prompt.
The first Dario quote sounds squarely in line with releasing a Claude 3 on par with GPT-4 but well afterwards. The second Dario quote has a more ambiguous connotation, but if read explicitly it strikes me as compatible with the Claude 3 release.
If you spent a while looking for the most damning quotes, then these quotes strike me as evidence the community was just wishfully thinking while in reality Anthropic comms were fairly clear throughout. Privately pitching aggressive things to divert money from more dangerous orgs while minimizing head-on competition with OpenAI seems best to me (though obviously it’s also evidence that they’ll actually do the aggressive scaling things, so hard to know).
To make concrete the disagreement, I’d be interested in people predicting on “If Anthropic releases a GPT-5 equivalent X months behind, then their dollars/compute raised will be Y times lower than OpenAI” for various values of X.
Claude 3 seems to be quite willing to discuss its own consciousness. On the other hand, Claude seemed unbothered and dubious about widespread scrutiny idea mentioned in this post (I tried asking neutrally in two separate context windows).
Here’s a screenshot of Claude agreeing with the view it expressed on AI consciousness in Mikhail’s conversation. And a gdoc dump of Claude answering follow-up questions on its experience. Claude is very well spoken on this subject!
Ah I see, you’re right there.
Agreed that a lot will hinge on the training details working out. I plan to look into this.
[Sidenote: realizing I should’ve introduced notation for auditor judgment let’s say A(x’,x)!=A(x) denotes that the trusted model found x’ useful for updating its judgment on input x. A would usually also be a function of y and y’ but I’ll suppress that for brevity.]
In the above experiment, I sampled from P(x’|x) then BoN adjusted for P(x’) which amounts to using logP(x’|x) - c*logP(x’) for small c. Intuitively, when equally weighted, the sum entirely removes the usual fluency constraint on x’ which, as you say, would reassign probability to gibberish (e.g. gibberish happening to re-use tokens from x’.) I’m not too concerned about this, weighting objectives with c lets us re-introduce this fluency constraint. The downstream evaluation, A(x’,x)!=A(x) of the auditor model provides a more costly check to avoid the x’ reporter model exploration devolving to useless, gibberish cases. As you say, P(y’|x’) provides another useful constraint.
I agree that traditional exploration concerns in RL likely remain. I think what you’re saying about modifying samples is that we have a dense reward here, i.e. logP(x’|x) - logP(x’), that can be cheaply evaluated for any token-level change? This makes exploration faster compared to the regular critique/debate setting where dense rewards can only be noisily estimated as e.g. in an advantage function. I’d agree with that.
On exploration, a second difference between this counterfactual critique compared to vanilla, just-ask-for-critique RL is specific to super-human domains involving ontology mismatch. Exploration in just-ask-for-critique RL systematically favors human-simulation++ arguments: such a just-ask-for-critique agent may successfully explore to plenty of novel critiques without ever touching on latent knowledge obscured by ontology mismatch. This raises two concerns in the super-human domain (1) is this just-ask-for-critique agent just finding solutions which are persuasive but not true (and not even related to the original input x)? (2) is this just-ask-for-critique scheme better described as a capabilities-enhancing scheme rather than capabilities elicitation?
In light of these concerns, we could reframe the issue as an inductive bias problem which logP(x’|x) - logP(x’) regularization seeks to fix when compared to directly exploring for x’ satisfying A(x’,x)!=A(x).
Thanks for the comments, I’m very interested in getting clearer on what cases of ELK this method can and cannot be expected to elicit. Though broadly I do not agree that the proposed method is limited to “changes [current] human might have done when rewriting/paraphrasing the code” (I’m particularly worried about cases mentioned in the paragraph starting with ‘Another problem arises …’)
There are two mechanisms by which the sort of approach I’m suggesting helps us find a superset of human-simulation critiques/knowledge:
A) logP(x’|x)-logP(x’) loss can be low even when the initial tokens of x’ are very low probability, i.e. cases in which a human would not be expected to start talking about x’. Any experiment fix which can be in-context learned scores well on this loss; this may include most super-human fixes we care about! (relying on ICL will likely breakdown when we move to heavily RL-ed models, but the easier LM setting seems like the sensible starting point to iterate on this sort of research.)
B) Now that we have a well-defined scoring function that includes super-human critiques, we can train a model against it via search-and-distillation and generalization will help us explore to reach solutions that would be exponentially slower to find via temperature sampling (or even zero weight on temperature sampling) on the original model. How well exploration works here is unclear and evaluating this should be a priority!
Would be interested to hear where you disagree with these points.
As for exploration hacking, I agree that it’s a potential issue. I’d be particularly worried when the y predictor and the x’ predictor are both getting updated interactively. I’m not too concerned if we have a fixed y predictor, and just train the x’ predictor (I think of the latter as the control setting).
Auditing LMs with counterfactual search: a tool for control and ELK
I agree overall with Janus, but the Gwern example is a particularly easy one given he has 11,000+ comments on Lesswrong.
A bit over a year ago I benchmarked GPT-3 on predicting newly scraped tweets for authorship (from random accounts over 10k followers) and top-3 acc was in the double digits. IIRC after trying to roughly control for the the rate at which tweets mentioned their own name/org, my best guess was that accuracy was still ~10%. To be clear, in my view that’s a strong indication of authorship identification capability.
Yea, I agree with this description—input space is a strict subset of predictor-state counterfactuals.
In particular, I would be interested to hear if restricting to input space counterfactuals is clearly insufficient for a known reason. It appears to me that you can still pull the trick you describe in “the proposal” sub-section (constructing counterfactuals which change some property in a way that a human simulator would not pick up on) at least in some cases.
Thanks those ‘in the wild’ examples, they’re informative for me on the effectiveness of prompting for elicitation in the cases I’m thinking about. However, I’m not clear on whether we should expect such results to continue to hold given the results here that larger models can learn to override semantic priors when provided random/inverted few-shot labels.
Agreed that for your research question (is worst-case SFT password-ing fixable) the sort of FT experiment you’re conducting is the relevant thing to check.
Do you have results on the effectiveness of few-shot prompting (using correct, hard queries in prompt) for password-tuned models? Particularly interested in scaling w.r.t. number of shots. This would get at the extent to which few-shot capabilities elicitation can fail in pre-train-only models which is a question I’m interested in.
What part of the proposal breaks if we do counterfactuals in input space rather than on the predictor’s state?
I see Simon’s point as my crux as well, and am curious to see a response.
It might be worth clarifying two possible reasons for disagreement here; are either of the below assumed by the authors of this post?
(1) Economic incentives just mean that the AI built will also handle the economic transactions, procurement processes, and other external-world tasks related to the science/math problems it’s tasked with. I find this quite plausible, but I suspect the authors do not intend to assume this?
(2) Even if the AI training is domain-specific/factored (i.e. it only handles actions within a specified domain) I’d expect some optimization pressure to be unrelated to the task/domain and to instead come from external world costs i.e. compute or synthesis costs. I’d expect such leakage to involve OOMs less optimization power than the task(s) at hand, and not to matter before godlike AI. Insofar as that leakage is crucial to Jeremy and Peter’s argument I think this should be explicitly stated.
I’m currently working on filler token training experiments in small models. These GPT-4 results are cool! I’d be interested to chat.
The Metaculus community strikes me as a better starting point for evaluating how different the safety inside view is from a forecasting/outside view. The case for deferring to superforecasters is the same the case for deferring to the Metaculus community—their track record. What’s more, the most relevant comparison I know of scores Metaculus higher on AI predictions. Metaculus as a whole is not self-consistent on AI and extinction forecasting across individual questions (links below). However, I think it is fair to say that Metaculus as a whole has significantly faster timelines and P(doom) compared to superforecasters.
If we compare the distribution of safety researchers’ forecasts to Metaculus (maybe we have to set aside MIRI...), I don’t think there will be that much disagreement. I think remaining disagreement will often be that safety researchers aren’t being careful about how the letter and the spirit of the question can come apart and result in false negatives. In the one section of the FRI studies linked above I took a careful look at, the ARA section, I found that there was still huge ambiguity in how the question is operationalized—this could explain up to an OOM of disagreement in probabilities.
Some Metaculus links: https://www.metaculus.com/questions/578/human-extinction-by-2100/ Admittedly in this question the number is 1%, but compare to the below. Also note that the forecasts date back to as old as 2018. https://www.metaculus.com/questions/17735/conditional-human-extinction-by-2100/ https://www.metaculus.com/questions/9062/time-from-weak-agi-to-superintelligence/ (compare this to the weak AGI timeline and other questions)