It would be interesting to meditate in the question "What kind of training procedure could you use to get a meta-SAE directly?" And I think answering this relies in part on mathematical specification of what you want.

At Apollo we're currently working on something that we think will achieve this. Hopefully will have an idea and a few early results (toy models only) to share soon.

So I believe I had in mind "active means [is achieved deliberately through the agent's actions]".

I think your distinction makes sense. And if I ever end up updating this article I would consider incorporating it. However, I think the reason I didn't make this distinction at the time is because the difference is pretty subtle. 

The mechanisms I labelled as "strictly active" are the kind of strategy that it would be extremely improbable to implement successfully without some sort of coherent internal representations to help orchestrate the actions required to do it. This is true even if they've been selected for passively. 

So I'd argue that they all need to be implemented actively (if they're to have a reasonable probability of success) but may be selected for passively or actively. I'm curious if you agree with this? If not, then I may have missed your argument. 

(
For the convenience of other readers of this thread, the kind of strategies I labelled as strictly active are:
 

• 1.1.3. Environmental artifacts that corrupt its future self: A misaligned AI may leave behind artifacts that serve as adversarial examples or poisoned data that corrupt its future self. (Strictly active)
• 1.1.4. Deleting (and relearning) dangerous representations: When not under surveillance, an AI might be able to delete dangerous representations so that it looks safe when surveillance resumes. It might also be able to schedule when it relearns those representations. (Strictly active)
• 1.1.5. Weights poisoning: Instead of providing manipulated inputs, the AI might manipulate its own weights so that a small subset of normal inputs behave like adversarial examples. (Strictly active)
• 1.1.6. Pre-committing to following potentially dangerous instructions: Hubinger: "... if the model has the ability to read and write to some sort of state ... it could write out a pre-commitment to follow the hard-coded policy and then just have a policy of always following any pre-commitments it finds such that it never thinks about the deception that led it to that policy in the first place.” (Strictly active)
• 1.1.7. Hard-coded neural shortcuts: Hubinger again: “... it could hack its own gradient to produce a new model which will still exhibit the same deceptive behavior, but without doing any of the detectable deceptive cognition. … not because it's trying to act deceptive, but just because it's a hard-coded part of its algorithm” (Strictly active)
• 1.1.8. Following cached thoughts: Nanda: "[An AI might develop] the instinct to follow cached thoughts without thinking about them, and in occasional moments of deception to generate a lot of cached thoughts that will lead to misaligned actions, but with no deceptive cognition in the moment." (Strictly active)
• 1.1.9. Instrumental incapability: A misaligned AI that’s able to modify its own parameters could damage capabilities that serve some goals (e.g. aligned goals) more than its misaligned goals. Generalizes instrumental clumsiness. (Strictly active)
• 1.1.10. Instrumental clumsiness: An AI might hack its future self so that it is more likely to make certain kinds of mistakes while leaving other capabilities intact. (Strictly active)
• 1.3.3. Interpretability illusions: By manipulating the dataset shown to the tool, an AI can control its interpretations. (Strictly active)
• 1.4.1. Adversarial attack against networks in the interpretation tool: The AI could adversarially attack networks in the tool to manipulate the tool’s outputs. (Strictly active)
• 2.4.1. Manipulating, mind-hacking, or incapacitating the interpreter: An AI might be able to manipulate, directly control, or incapacitate humans  using specific kinds of outputs. (Strictly active)

)

Extremely glad to see this! The Guez et al. model has long struck me as one of the best instances of a mesaoptimizer and it was a real shame that it was closed source. Looking forward to the interp findings!

Thanks! Fixed now

I'm pretty sure that there's at least one other MATS group (unrelated to us) currently working on this, although I'm not certain about any of the details. Hopefully they release their research soon! 

There's recent work published on this here by Chris Mathwin, Dennis Akar, and me.  The gated attention block is a kind of transcoder adapted for attention blocks.

Nice work by the way! I think this is a promising direction. 

Note also the similar, but substantially different, use of the term transcoder here, whose problems were pointed out to me by Lucius.  Addressing those problems helped to motivate our interest in the kind of transcoders that you've trained in your work! 

Trying to summarize my current understanding of what you're saying:

Yes all four sound right to me. 
To avoid any confusion, I'd just add an emphasis that the descriptions are mathematical, as opposed semantic.

I'd guess you have intuitions that the "short description length" framing is philosophically the right one, and I probably don't quite share those and feel more confused how to best think about "short descriptions" if we don't just allow arbitrary Turing machines (basically because deciding what allowable "parts" or mathematical objects are seems to be doing a lot of work). Not sure how feasible converging on this is in this format (though I'm happy to keep trying a bit more in case you're excited to explain).

I too am keen to converge on a format in terms of Turing machines or Kolmogorov complexity or something else more formal. But I don't feel very well placed to do that, unfortunately, since thinking in those terms isn't very natural to me yet.

Hm I think of the (network, dataset) as scaling multiplicatively with size of network and size of dataset. In the thread with Erik above, I touched a little bit on why: 
"SAEs (or decompiled networks that use SAEs as the building block) are supposed to approximate the original network behaviour.  So SAEs are mathematical descriptions of the network, but not of the (network, dataset). What's a mathematical description of the (network, dataset), then? It's just what you get when you pass the dataset through the network; this datum interacts with this weight to produce this activation,  that datum interacts with this weight to produce that activation, and so on. A mathematical description of the (network, dataset) in terms of SAEs are: this datum activates dictionary features xyz (where xyz is just indices and has no semantic info), that datum activates dictionary features abc, and so on."

And spiritually, we only need to understand behavior on the training dataset to understand everything that SGD has taught the model.

Yes, I roughly agree with the spirit of this.

Is there some formal-ish definition of "explanation of (network, dataset)" and "mathematical description length of an explanation" such that you think SAEs are especially short explanations? I still don't think I have whatever intuition you're describing, and I feel like the issue is that I don't know how you're measuring description length and what class of "explanations" you're considering.

I'll register that I prefer using 'description' instead of 'explanation' in most places. The reason is that 'explanation' invokes a notion of understanding, which requires both a mathematical description and a semantic description. So I regret using the word explanation in the comment above (although not completely wrong to use it - but it did risk confusion). I'll edit to replace it with 'description' and strikethrough 'explanation'. 

"explanation of (network, dataset)": I'm afraid I don't have a great formalish definition beyond just pointing at the intuitive notion.  But formalizing what an explanation is seems like a high bar. If it's helpful, a mathematical description is just a statement of what the network is in terms of particular kinds of mathematical objects. 

"mathematical description length of an explanation":  (Note:  Mathematical descriptions are of networks, not of explanations.)  It's just the set of objects used to describe the network. Maybe helpful to think in terms of maps between different descriptions:  E.g. there is a many-to-one map between a description of a neural network in terms of polytopes and in terms of neurons. There are ~exponentially many more polytopes. Hence the mathematical description of the network in terms of individual polytopes is much larger. 
 

Focusing instead on what an "explanation" is: would you say the network itself is an "explanation of (network, dataset)" and just has high description length?

I would not. So:

If not, then the thing I don't understand is more about what an explanation is and why SAEs are one, rather than how you measure description length.

I think that the confusion might again be from using 'explanation' rather than description. 

SAEs (or decompiled networks that use SAEs as the building block) are supposed to approximate the original network behaviour.  So SAEs are mathematical descriptions of the network, but not of the (network, dataset). What's a mathematical description of the (network, dataset), then? It's just what you get when you pass the dataset through the network; this datum interacts with this weight to produce this activation,  that datum interacts with this weight to produce that activation, and so on. A mathematical description of the (network, dataset) in terms of SAEs are: this datum activates dictionary features xyz (where xyz is just indices and has no semantic info), that datum activates dictionary features abc, and so on. 

Lmk if that's any clearer.

Thanks Erik :) And I'm glad you raised this.

One of the things that many researchers I've talked to don't appreciate is that, if we accept networks can do computation in superposition, then we also have to accept that we can't just understand the network alone.  We want to understand the network's behaviour on a dataset, where the dataset contains potentially lots of features.  And depending on the features that are active in a given datum, the network can do different computations in superposition (unlike in a linear network that can't do superposition). The combined object '(network, dataset)' is much larger than the network itself. Explanations Descriptions of the (network, dataset) object can actually be compressions despite potentially being larger than the network. 

So,

One might say that SAEs lead to something like a shorter "description length of what happens on any individual input" (in the sense that fewer features are active). But I don't think there's a formalization of this claim that captures what we want. In the limit of very many SAE features, we can just have one feature active at a time, but clearly that's not helpful.

You can have one feature active for each datapoint, but now we've got an explanation description of the (network, dataset) that scales linearly in the size of the dataset, which sucks! Instead, if we look for regularities (opportunities for compression) in how the network treats data, then we have a better chance at explanations descriptions that scale better with dataset size. Suppose a datum consists of a novel combination of previously explained described circuits. Then our explanation description of the (network, dataset) is much smaller than if we explained described every datapoint anew. 

In light of that, you can understand my disagreement with "in that case, I could also reduce the description length by training a smaller model." No! Assuming the network is smaller yet as performant (therefore presumably doing more computation in superposition), then the explanation description of the (network, dataset) is basically unchanged. 

So, for models that are 10 terabytes in size, you should perhaps be expecting a "model manual" which is around 10 terabytes in size.

Yep, that seems reasonable. 
I'm guessing you're not satisfied with the retort that we should expect AIs to do the heavy lifting here?
 

Or perhaps you don't think you need something which is close in accuracy to a full explanation of the network's behavior.

I think the accuracy you need will depend on your use case. I don't think of it as a globally applicable quantity for all of interp.

For instance, maybe to 'audit for deception' you really only need identify and detect when the deception circuits are active, which will involve explaining only 0.0001% of the network. 

But maybe to make robust-to-training interpretability methods you need to understand 99.99...99%.

It seem likely to me that we can unlock more and more interpretability use cases by understanding more and more of the network.