To summarize this comment, you've proposed that baseline monitoring systems could reduce risk to an acceptable level. Specifically, the monitoring system would need to correctly identify at least 5% of dangerous queries as dangerous ("5% precision") and avoid incorrectly flagging more than 1 in 1000 safe queries as dangerous ("0.1% FPR"). 

I think this level of reliability is possible today (e.g. Claude 2 would likely meet it), but it's possible that future developments would make defense more difficult. For example, new attack methods have shown LLMs to be less robust to misuse than previously understood. (This is one downside of adversarial robustness research that will become more important as the stakes of adversarial attacks rise.) Perhaps a bigger challenge is the growth of multimodal systems. Defending vision language models is much more difficult than defending pure LLMs. As multimodality becomes standard, we might see adversarial attacks that regularly achieve >95% success rates in bypassing monitoring systems. I'm not particularly confident about how difficult monitoring will be, but it would be beneficial to have monitoring systems which would work even if defense gets much harder in the future. 

Overall, these hypotheticals only offer so much information when none of these defenses has ever been publicly built or tested. I think we agree that simple monitoring strategies might be fairly effective and cheap in identifying misuse, and that progress on adversarial robustness would significantly reduce costs by improving the effectiveness of automated monitoring systems. 

I specifically avoided claiming that adversarial robustness is the best altruistic option for a particular person. Instead, I'd like to establish that progress on adversarial robustness would have significant benefits, and therefore should be included in the set of research directions that "count" as useful AI safety research. 

Over the next few years, I expect AI safety funding and research will (and should) dramatically expand. Research directions that would not make the cut at a small organization with a dozen researchers should still be part of the field of 10,000 people working on AI safety later this decade. Currently I'm concerned that the field focuses on a small handful of research directions (mainly mechinterp and scalable oversight) which will not be able to absorb such a large influx of interest. If we can lay the groundwork for many valuable research directions, we can multiply the impact of this large population of future researchers. 

I don't think adversarial robustness should be more than 5% or 10% of the research produced by AI safety-focused researchers today. But some research (e.g. 1, 2) from safety-minded folks seems very valuable for raising the number of people working on this problem and refocusing them on more useful subproblems. I think robustness should also be included in curriculums that educate people about safety, and research agendas for the field. 

I do think these arguments contain threads of a general argument that causing catastrophes is difficult under any threat model. Let me make just a few non-comprehensive points here: 

On cybersecurity, I'm not convinced that AI changes the offense defense balance. Attackers can use AI to find and exploit security vulnerabilities, but defenders can use it to fix them. 

On persuasion, first, rational agents can simply ignore cheap talk if they expect it not to help them. Humans are not always rational, but if you've ever tried to convince a dog or a baby to drop something that they want, you'll know cheap talk is ineffective and only coercion will suffice. 

Second, AI is far from the first dramatic change in communications technology in human history. Spoken language, written language, the printing press, telephones, radio, TV, and social media all might be bigger changes than e.g.  changed how people can be persuaded. These technologies often contributed to political and social upheaval, including catastrophes for particular ways of life, and AI might do the same. But overall I'm glad these changes occurred, and I wouldn't expect the foreseeable versions of AI persuasion (i.e. personalized chatbots) to be much more impactful than these historical changes. See this comment and thread for more discussion. 

Bioterrorism seems like the biggest threat. The obstacles there have been thoroughly discussed. 

If causing catastrophes is difficult, this should reduce our concern with both misuse and rogue AIs causing sudden extinction. Other concerns like military arms races, lock-in of authoritarian regimes, or Malthusian outcomes in competitive environments would become relatively more important. 

Also, I'd love to see research that simulates the position of a company trying to monitor misuse, and allows for the full range of defenses that you proposed. There could be a dataset of 1 trillion queries containing 100 malicious queries. Perhaps each query is accompanied by a KYC ID. Their first line of defense would be robust refusal to cause harm, and the second line would be automated detection of adversarial attacks. The company could also have a budget which can be spent on "human monitoring," which would give them access to the ground truth label of whether a query is malicious for a fixed price. I'd have to think about which elements would be the most tractable for making research progress, but the fact that AI companies need to solve this problem suggests that perhaps external researchers should work on it too.

Thanks for the detailed thoughts! I like the overall plan, especially using KYC, human monitoring, and a separate model for bio. I'd like to point out that this plan still uses automated monitoring systems, and to the extent these systems aren't adversarially robust, the plan will be more expensive and/or less effective. 

The only plans that wouldn't benefit from adversarial robustness are those without automated monitoring. For example, humans could be hired to manually monitor each and every query for attempted misuse. Let's consider the viability of fully manual monitoring from a financial perspective, and then we can think about how much additional benefit would be provided by automated robust monitoring systems. 

First, let's price out manual monitoring for ChatGPT Plus. The subscription costs $20/month. Suppose the average person makes 10 queries per day, or 300 queries per month, and that it takes 15 seconds for a human monitor to read a query and flag it as misuse. Wages of $5/hour would mean comprehensive human monitoring costs $6.25 per user per month, and wages of $10/hour and $15/hour would translate to monthly per user costs of $12.50 and $18.75 respectively. The cost of full manual monitoring on ChatGPT Plus would therefore amount to much or most of its overall revenue. 

Second, we can anchor on Google Search. This assumes that Google is a reasonable reference point for the eventual volume, expenses, and revenues of an AI provider, which might not be a safe assumption in several ways. Nevertheless, Google conducts a ~3 trillion searches per year (varies by data source). If monitoring a single search costs $0.01 (e.g. someone earning $5/hour who monitors 500 searches per hour), then it would cost ~$30B to monitor every search. Would $30B in monitoring costs be financially acceptable? Google Search had revenues of $162B last year, representing 57% of Google's total revenue. They don't report expenses for Search specifically, but their overall expenses were $207B. If we assume Search comprises 57% of expenses, then Search would have $118B in annual expenses, against $162B in revenue. Manual monitoring would cost $30B, and would therefore eliminate two-thirds of Google's search profits. 

So these costs would not be prohibitive, but they'd be a large share of overall revenues and profits. A safety-minded company like Anthropic might pay for manual monitoring, but other companies and their investors might be strongly opposed to paying such a high price. They could argue that, just as gun manufacturers are not held liable for murders, AI providers should not have to spend billions to prevent deliberate misuse. 

Fortunately, we can reduce the cost of monitoring in many ways. Randomly sampling a small fraction of queries would reduce costs, but also reduce the likelihood of identifying misuse. Flagging keywords like "virus" would catch unsophisticated misuse, but could be evaded (e.g. discussions in a variant of pig latin). 

Ideally, you'd be able to use AI systems to identify suspicious queries for human monitoring, but those systems would only be effective to the extent that they're adversarially robust. If 99% of queries can be reliably discarded as safe, then manual monitoring costs would fall by 99%. But adversarial attacks often succeed in 50% or 100% of attempts against various detection systems. Deploying these unreliable systems would not decrease the costs of manual monitoring much without a corresponding drop in performance. 

Overall, I appreciate your point that there are many layers of defense we can use to detect and prevent misuse Fully manual monitoring might be possible, but it would have a huge financial cost. Many companies would be reluctant or unable to pay that price. Robust automated monitoring systems could reduce the cost of monitoring by 90% or 99%, but this would likely require improvements upon today's state of the art. 

This is a comment from Andy Zou, who led the RepE paper but doesn’t have a LW account:

“Yea I think it's fair to say probes is a technique under rep reading which is under RepE (https://www.ai-transparency.org/). Though I did want to mention, in many settings, LAT is performing unsupervised learning with PCA and does not use any labels. And we find regular linear probing often does not generalize well and is ineffective for (causal) model control (e.g., details in section 5). So equating LAT to regular probing might be an oversimplification. How to best elicit the desired neural activity patterns requires careful design of 1) the experimental task and 2) locations to read the neural activity, which contribute to the success of LAT over regular probing (section 3.1.1).

In general, we have shown some promise of monitoring high-level representations for harmful (or catastrophic) intents/behaviors. It's exciting to see follow-ups in this direction which demonstrate more fine-grained monitoring/control.”

What's the relationship between this method and representation engineering? They seem quite similar, though maybe I'm missing something. You train a linear probe on a model's activations at a particular layer in order to distinguish between normal forward passes and catastrophic ones where the model provides advice for theft. 

Representation engineering asks models to generate both positive and negative examples of a particular kind of behavior. For example, the model would generate outputs with and without theft, or with and without general power-seeking. You'd collect the model's activations from a particular layer during those forward passes, and then construct a linear model to distinguish between positives and negatives. 

Both methods construct a linear probe to distinguish between positive and negative examples of catastrophic behavior.  One difference is that your negatives are generic instruction following examples from the Alpaca dataset, while RepE uses negatives generated by the model. There may also be differences in whether you're examining activations in every token vs. in the last token of the generation. 

Great resource, thanks for sharing! As somebody who's not too deeply familiar with either mechanistic interpretability or the academic field of interpretability, I find myself confused by the fact that AI safety folks usually dismiss the large academic field of interpretability. Most academic work on ML isn't useful for safety because safety studies different problems with different kinds of systems. But unlike focusing on worst-case robustness or inner misalignment, I would expect generating human understandable explanations of what neural networks are doing to be interesting to plenty of academics, and I would think that's what the academics are trying to do. Are they just bad at generating insights? Do they look for the wrong kinds of progress, perhaps motivated by different goals? Why is the large academic field of interpretability not particularly useful for x-risk motivated AI safety?

These professors all have a lot of published papers in academic conferences. It’s probably a bit frustrating to not have their work summarized, and then be asked to explain their own work, when all of their work is published already. I would start by looking at their Google Scholar pages, followed by personal websites and maybe Twitter. One caveat would be that papers probably don’t have full explanations of the x-risk motivation or applications of the work, but that’s reading between the lines that AI safety people should be able to do themselves.

Ah okay. Are there theoretical reasons to think that neurons with lower variance in activation would be better candidates for pruning? I guess it would be that the effect on those nodes is similar across different datapoints, so they can be pruned and their effects will be replicated by the rest of the network.