Mark Xu

I do alignment research at the Alignment Research Center. Learn more about me at markxu.com/about

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ELK prize results

From my perspective, ELK is currently very much "A problem we don't know how to solve, where we think rapid progress is being made (as we're still building out the example-counterexample graph, and are optimistic that we'll find an example without counterexamples)" There's some question of what "rapid" means, but I think we're on track for what we wrote in the ELK doc: "we're optimistic that within a year we will have made significant progress either towards a solution or towards a clear sense of why the problem is hard."

We've spent ~9 months on the problem so far, so it feels like we've mostly ruled out it being an easy problem that can be solved with a "simple trick", but it very much doesn't feel like we've hit on anything like a core obstruction. I think we still have multiple threads that are still live and that we're still learning things about the problem as we try to pull on those threads.

I'm still pretty interested in aiming for a solution to the entire problem (in the worst case), which I currently think is still plausible (maybe 1/3rd chance?). I don't think we're likely to relax the problem until we find a counterexample that seems like a fundamental reason why the original problem wasn't possible. Another way of saying this is that we're working on ELK because of a set of core intuitions about why it ought to be possible and we'll probably keep working on it until those core intuitions have been shown to be flawed (or we've been chugging away for a long time without any tangible progress).

Prizes for ELK proposals

The official deadline for submissions is "before I check my email on the 16th", which I tend to do around 10 am PST.

Prizes for ELK proposals

Before I check my email on Feb 16th, which I will do around 10am PST.

Prizes for ELK proposals

The high-level reason is that the 1e12N model is not that much better at prediction than the 2N model. You can correct for most of the correlation even with only a vague guess at how different the AI and human probabilities are, and most AI and human probabilities aren't going to be that different in a way that produces a correlation the human finds suspicious. I think that the largest correlations are going to be produced by the places the AI and the human have the biggest differences in probabilities, which are likely also going to be the places where the 2N model has the biggest differences in probabilities, so they should be not that hard to correct.

I'm curious whether you think this is the main obstacle. If we had a version of the correlation-consistency approach that always gave the direct translator minimal expected consistency loss, do we as-of-yet lack a counterexample for it?

I think it wouldn't be clear that extending the counterexample would be possible, although I suspect it would be. It might require exhibiting more concrete details about how the consistency check would be defeated, which would be interesting. In some sense, maintaining consistency across many inputs is something that you expect to be pretty hard for the human simulator to do because it doesn't know what set of inputs it's being checked for. I would be excited about a consistency check that gave the direct translator minimal expected consistency loss. Note that I would also be interested in basically any concrete proposal for a consistency check that seemed like it was actually workable.

Prizes for ELK proposals

I agree that i does slightly worse than t on consistency checks, but i also does better on other regularizers you're (maybe implicitly) using like speed/simplicity, so as long as i doesn't do too much worse it'll still beat out the direct translator.

One possible thing you might try is some sort of lexicographic ordering of regularization losses. I think this rapidly runs into other issues with consistency checks, like the fact that the human is going to be systematically wrong about some correlations, so i potentially is more consistent than t.

Alex Ray's Shortform

I feel mostly confused by the way that things are being framed. ELK is about the human asking for various poly-sized fragments and the model reporting what those actually were instead of inventing something else. The model should accurately report all poly-sized fragments the human knows how to ask for.

Like the thing that seems weird to me here is that you can't simultaneously require that the elicited knowledge be 'relevant' and 'comprehensible' and also cover these sorts of obfuscated debate like scenarios.

I don't know what you mean by "relevant" or "comprehensible" here.

Does it seem right to you that ELK is about eliciting latent knowledge that causes an update in the correct direction, regardless of whether that knowledge is actually relevant?

This doesn't seem right to me.

Alex Ray's Shortform

I don’t think I understand your distinction between obfuscated and non-obfuscated knowledge. I generally think of non-obfuscated knowledge as NP or PSPACE. The human judgement of a situation might only theoretically require a poly sized fragment of a exp sized computation, but there’s no poly sized proof that this poly sized fragment is the correct fragment, and there are different poly sized fragments for which the human will evaluate differently, so I think of ELK as trying to elicit obfuscated knowledge.

Prizes for ELK proposals

We would prefer submissions be private until February 15th.

Prizes for ELK proposals

We generally assume that we can construct questions sufficiently well that there's only one unambiguous interpretation. We also generally assume that the predictor "knows" which world it's in because it can predict how humans would respond to hypothetical questions about various situations involving diamonds and sensors and that humans would say in theory Q1 and Q2 could be different.

More concretely, our standard for judging proposals is exhibiting an unambiguous failure. If it was plausible you asked the wrong question, or the AI didn't know what you meant by the question, then the failure exhibited would be ambiguous. If humans are unable to clarify between two possible interpretations of their question, then the failure would be ambiguous.

Alex Ray's Shortform

I think we would be trying to elicit obfuscated knowledge in ELK. In our examples, you can imagine that the predictor's Bayes net works "just because", so an argument that is convincing to a human for why the diamond in the room has to be arguing that the Bayes net is a good explanation of reality + arguing that it implies the diamond is in the room, which is the sort of "obfuscated" knowledge that debate can't really handle.

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