> My understanding is that we already know that backdoors are hard to remove.We don't actually find that backdoors are always hard to remove!
We did already know that backdoors often (from the title) "Persist Through Safety Training." This phenomenon studied here and elsewhere is being taken as the main update in favor of AI x-risk. This doesn't establish probability of the hazard, but it reminds us that backdoor hazards can persist if present.
I think it's very easy to argue the hazard could emerge from malicious actors poisoning pretraining data, and harder to argue it would arise naturally. AI security researchers such as Carlini et al. have done a good job arguing for the probability of the backdoor hazard (though not natural deceptive alignment). (I think malicious actors unleashing rogue AIs is a concern for the reasons bio GCRs are a concern; if one does it, it could be devastating.)
I think this paper shows the community at large will pay orders of magnitude more attention to a research area when there is, in @TurnTrout's words, AGI threat scenario "window dressing," or when players from an EA-coded group research a topic. (I've been suggesting more attention to backdoors since maybe 2019; here's a video from a few years ago about the topic; we've also run competitions at NeurIPS with thousands of submissions on backdoors.) Ideally the community would pay more attention to relevant research microcosms that don't have the window dressing.
I think AI security-related topics have a very good track record of being relevant for x-risk (backdoors, unlearning, adversarial robustness). It's a been better portfolio than the EA AGI x-risk community portfolio (decision theory, feature visualizations, inverse reinforcement learning, natural abstractions, infrabayesianism, etc.). At a high level its saying power is because AI security is largely about extreme reliability; extreme reliability is not automatically provided by scaling, but most other desiderata are (e.g., commonsense understanding of what people like and dislike).
A request: Could Anthropic employees not call supervised fine-tuning and related techniques "safety training?" OpenAI/Anthropic have made "alignment" in the ML community become synonymous with fine-tuning, which is a big loss. Calling this "alignment training" consistently would help reduce the watering down of the word "safety."
Plug: CAIS is funding constrained.
A brief overview of the contents, page by page.
1: most important century and hinge of history
2: wisdom needs to keep up with technological power or else self-destruction / the world is fragile / cuban missile crisis
3: unilateralist's curse
4: bio x-risk
5: malicious actors intentionally building power-seeking AIs / anti-human accelerationism is common in tech
6: persuasive AIs and eroded epistemics
7: value lock-in and entrenched totalitarianism
8: story about bioterrorism
9: practical malicious use suggestions
10: LAWs as an on-ramp to AI x-risk
11: automated cyberwarfare -> global destablization
12: flash war, AIs in control of nuclear command and control
13: security dilemma means AI conflict can bring us to brink of extinction
14: story about flash war
15: erosion of safety due to corporate AI race
16: automation of AI research; autnomous/ascended economy; enfeeblement
17: AI development reinterpreted as evolutionary process
18: AI development is not aligned with human values but with competitive and evolutionary pressures
19: gorilla argument, AIs could easily outclass humans in so many ways
20: story about an autonomous economy
21: practical AI race suggestions
22: examples of catastrophic accidents in various industries
23: potential AI catastrophes from accidents, Normal Accidents
24: emergent AI capabilities, unknown unknowns
25: safety culture (with nuclear weapons development examples), security mindset
26: sociotechnical systems, safety vs. capabilities
27: safetywashing, defense in depth
28: story about weak safety culture
29: practical suggestions for organizational safety
30: more practical suggestions for organizational safety
31: bing and microsoft tay demonstrate how AIs can be surprisingly unhinged/difficult to steer
32: proxy gaming/reward hacking
33: goal drift
34: spurious cues can cause AIs to pursue wrong goals/intrinsification
35: power-seeking (tool use, self-preservation)
36: power-seeking continued (AIs with different goals could be uniquely adversarial)
37: deception examples
38: treacherous turns and self-awareness
39: practical suggestions for AI control
40: how AI x-risk relates to other risks
but I'm confident it isn't trying to do this
It is. It's an outer alignment benchmark for text-based agents (such as GPT-4), and it includes measurements for deception, resource acquisition, various forms of power, killing, and so on. Separately, it's to show reward maximization induces undesirable instrumental (Machiavellian) behavior in less toyish environments, and is about improving the tradeoff between ethical behavior and reward maximization. It doesn't get at things like deceptive alignment, as discussed in the x-risk sheet in the appendix. Apologies that the paper is so dense, but that's because it took over a year.
I asked for permission via Intercom to post this series on March 29th. Later, I asked for permission to use the [Draft] indicator and said it was written by others. I got permission for both of these, but the same person didn't give permission for both of these requests. Apologies this was not consolidated into one big ask with lots of context. (Feel free to get rid of any undue karma.)
It's a good observation that it's more efficient; does it trade off performance? (These sorts of comparisons would probably be demanded if it was submitted to any other truth-seeking ML venue, and I apologize for consistently being the person applying the pressures that generic academics provide. It would be nice if authors would provide these comparisons.)
Also, taking affine combinations in weight-space is not novel to Schmidt et al either. If nothing else, the Stable Diffusion community has been doing that since October to add and subtract capabilities from models.
It takes months to write up these works, and since the Schmidt paper was in December, it is not obvious who was first in all senses. The usual standard is to count the time a standard-sized paper first appeared on arXiv, so the most standard sense they are first. (Inside conferences, a paper is considered prior art if it was previously published, not just if it was arXived, but outside most people just keep track of when it was arXived.) Otherwise there are arms race dynamics leading to everyone spamming snippets before doing careful, extensive science.
steering the model using directions in activation space is more valuable than doing the same with weights, because in the future the consequences of cognition might be far-removed from its weights (deep deceptiveness)
(You linked to "deep deceptiveness," and I'm going to assume is related to self-deception (discussed in the academic literature and in the AI and evolution paper). If it isn't, then this point is still relevant for alignment since self-deception is another internal hazard.)
I think one could argue that self-deception could in some instances be spotted in the weights more easily than in the activations. Often the functionality acquired by self-deception is not activated, but it may be more readily apparent in the weights. Hence I don't see this as a strong reason to dismiss https://arxiv.org/abs/2212.04089. I would want a weight version of a method and an activation version of a method; they tend to have different strengths.
Note: If you're wanting to keep track of safety papers outside of LW/AF, papers including https://arxiv.org/abs/2212.04089 were tweeted on https://twitter.com/topofmlsafety and posted on https://www.reddit.com/r/mlsafety
Edit: I see passive disagreement but no refutation. The argument against weights was of the form "here's a strength activations has"; for it to be enough to dismiss the paper without discussion, that must be an extremely strong property to outweigh all of its potential merits, or it is a Pareto-improvement. Those don't seem corroborated or at all obvious.
Page 4 of this paper compares negative vectors with fine-tuning for reducing toxic text: https://arxiv.org/pdf/2212.04089.pdf#page=4
In Table 3, they show in some cases task vectors can improve fine-tuned models.
Yes, I'll tend to write up comments quickly so that I don't feel as inclined to get in detailed back-and-forths and use up time, but here we are. When I wrote it, I thought there were only 2 things mentioned in the related works until Daniel pointed out the formatting choice, and when I skimmed the post I didn't easily see comparisons or discussion that I expected to see, hence I gestured at needing more detailed comparisons. After posting, I found a one-sentence comparison of the work I was looking for, so I edited to include that I found it, but it was oddly not emphasized. A more ideal comment would have been "It would be helpful to me if this work would more thoroughly compare to (apparently) very related works such as ..."
In many of my papers, there aren't fairly similar works (I strongly prefer to work in areas before they're popular), so there's a lower expectation for comparison depth, though breadth is always standard. In other works of mine, such as this paper on learning the the right thing in the presence of extremely bad supervision/extremely bad training objectives, we contrast with the two main related works for two paragraphs, and compare to these two methods for around half of the entire paper.
The extent of an adequate comparison depends on the relatedness. I'm of course not saying every paper in the related works needs its own paragraph. If they're fairly similar approaches, usually there also needs to be empirical juxtapositions as well. If the difference between these papers is: we do activations, they do weights, then I think that warrants a more in-depth conceptual comparisons or, preferably, many empirical comparisons.