The first thing I imagine is that nobody asks those questions. But let’s set that aside.
I disagree fwiw
The second thing I imagine is that somebody literally types those questions into a GPT-3 prompt. Obviously that does not result in the AI giving its actual best-guess answers to the questions, but it doesn’t result in the AI thinking about how to deceive humans either.
I agree.
Now, presumably future systems will train for things other than “predict what text typically follows this question”, but I expect the general failure mode to stay the same. When a human asks “Are you an unaligned AI?” or whatever, the AI thinks about a bunch of stuff which is just not particularly related to whether it’s an unaligned AI. The AI wasn’t trained to translate the literal semantics of questions into a query to its own internal world model and then translate the result back to human language; humans have no clue how to train such a thing. Probably the stuff the AI thinks about does not involve intentionally deceiving humans, because why would it? And then the AI gives some answer which is not particularly related to whether it’s an unaligned AI, and the humans interpret that as an answer to their original question, thereby deceiving themselves.
This is where I think the meat of the question lies; I overall disagree and think that the model does have to be thinking about deception in order to be dangerous while also performing well on the tasks we might train it on (eg “answer questions well, as judged by some human labeler”). I don’t have time to say much about what I think is going on here right now; I might come back later.
Any additional or new thoughts on this? Is your last comment saying that you simply don’t think it’s very likely at all for the model to unintentionally leave out information that will kill us if we train it with human labelers and prompt sufficiently? Do you believe it’s way more likely that we’d be unable to prompt things out of the model only if it were deceptive? Could you say more?
Separately: If I have a chain-of-thought model detailing steps it will take to reach x outcome. We’ve fine-tuned on previous chain-of-thoughts while giving process-level feedback. However, even if you are trying to get it to externalize it’s thoughts/reasoning, it could lead to extinction via side-effect. So you might ask the model at each individual thought (or just the entire plan) if we’ll be happy with the outcome. How exactly would the model end up querying its internal world model in the way we would want it to?
Is your last comment saying that you simply don’t think it’s very likely at all for the model to unintentionally leave out information that will kill us if we train it with human labelers and prompt sufficiently?
No, it seems very likely for the model to not say that it’s deceptive, I’m just saying that the model seems pretty likely to think about being deceptive. This doesn’t help unless you’re using interpretability or some other strategy to evaluate the model’s deceptiveness without relying on noticing deception in its outputs.
I disagree fwiw
I agree.
This is where I think the meat of the question lies; I overall disagree and think that the model does have to be thinking about deception in order to be dangerous while also performing well on the tasks we might train it on (eg “answer questions well, as judged by some human labeler”). I don’t have time to say much about what I think is going on here right now; I might come back later.
Any additional or new thoughts on this? Is your last comment saying that you simply don’t think it’s very likely at all for the model to unintentionally leave out information that will kill us if we train it with human labelers and prompt sufficiently? Do you believe it’s way more likely that we’d be unable to prompt things out of the model only if it were deceptive? Could you say more?
Separately: If I have a chain-of-thought model detailing steps it will take to reach x outcome. We’ve fine-tuned on previous chain-of-thoughts while giving process-level feedback. However, even if you are trying to get it to externalize it’s thoughts/reasoning, it could lead to extinction via side-effect. So you might ask the model at each individual thought (or just the entire plan) if we’ll be happy with the outcome. How exactly would the model end up querying its internal world model in the way we would want it to?
No, it seems very likely for the model to not say that it’s deceptive, I’m just saying that the model seems pretty likely to think about being deceptive. This doesn’t help unless you’re using interpretability or some other strategy to evaluate the model’s deceptiveness without relying on noticing deception in its outputs.