The typical mind fallacy is really useful for learning things about other people, because the things they assume of others often generalize surprisingly well to themselves

Burnout often doesn't look like lack of motivation / lack of focus / fatigue as people usually describe it. At least in my experience, it's often better described as a set of aversive mental triggers that fire whenever a burnt out person goes to do a sort of work they spent too much energy on in the past. (Where 'too much energy' has something to do with time and effort, but more to do with a bunch of other things re how people interface with their work).

I would be curious to hear you discuss what good, stable futures might look like and how they might be governed (mostly because I haven't heard your takes on this before and it seems quite important)

'wallow in it rather than do anything about it'

This is mostly the thing I mean when I use the word ambition above. I think you're using the word to mean something overlapping but distinct; I'm trying to capture the overarching thing that contains both 'wallow in it' and the 'underlying driver' of your disgust/disappointment reaction.

First, a warning that I think this post promotes a harmful frame that probably makes the lives of both the OP and the people around him worse. I want to suggest that people engage with this post, consider this frame, and choose to move in the opposite direction.

On the object level, it is possible to look at unambitious people and decide that while you do not want to be like them in this way. They may not be inherently ambitious, have values that lead to them rejecting ambition, or have other reasons for being unambitious (eg, personal problems). Regardless, I'm confused why this is what the OP is choosing to focus his empathy on, rather than the wide variety of other traits and feelings that a person can posses. I'm also confused why this is the metric someone would use to judge a person, value them, or seek to understand them by. 

if I empathize more, put myself in other peoples’ shoes, try to feel what they’re feeling, see things from their perspective, etc, then I’ll feel kinder toward them. I’ll feel more sympathetic, be gentler, more compassionate or generous.

Tbc, the problem isn't that the OP is disappointed when considering lack of ambition (if you care a lot about being ambitious yourself, maybe this is the right reaction, though you should still seek to understand why others are not). The problem here is that the main thing the OP sees when he tries to empathize is a lack of ambition. And not, you know, any of the normal things that would make you more compassionate towards someone, like their emotional state, desires, personality, good-heartedness, etc. 

Another reason this could be misleading is that it's possible that sampling from the model pre-mode-collapse performs better at high k even if RL did teach the model new capabilities. In general, taking a large number of samples from a slightly worse model before mode collapse often outperforms taking a large number of samples from a better model after mode collapse — even if the RL model is actually more powerful (in the sense that its best reasoning pathways are better than the original model's best reasoning pathways). In other words, the RL model can both be much smarter and still perform worse on these evaluations due to diversity loss. But it's possible that if you're, say, trying to train a model to accomplish a difficult/novel reasoning task, it's much better to start with the smarter RL model than the more diverse original model.

I think rule-following may be fairly natural for minds to learn, much more so than other safety properties, and I think it might be worth more research into this direction.

Some pieces of weak evidence:
1. Most humans seem to learn and follow broad rules, even when they are capable of more detailed reasoning
2. Current LLMs seem to follow rules fairly well (and training often incentivizes them to learn broad heuristics rather than detailed, reflective reasoning)

While I do expect rule-following to become harder to instill as AIs become smarter, I think that if we are sufficiently careful, it may well scale to human-level AGIs. I think trying to align reflective, utilitarian-style AIs is probably really hard, as these agents are much more prone to small unavoidable alignment failures (like slight misspecification or misgeneralization) causing large shifts in behavior. Conversely, if we try our best to instill specific simpler rules, and then train these rules to take precedence over consequentialist reasoning whenever possible, this seems a lot safer.

I also think there is a bunch of tractable, empirical research that we can do right now about how to best do this.

I often hear people dismiss AI control by saying something like, "most AI risk doesn't come from early misaligned AGIs." While I mostly agree with this point, I think it fails to engage with a bunch of the more important arguments in favor of control— for instance, the fact that catching misaligned actions might be extremely important for alignment. In general, I think that control has a lot of benefits that are very instrumentally useful for preventing misaligned ASIs down the line, and I wish people would more thoughtfully engage with these.

Thanks for the response! I still think that most of the value of SAEs comes from finding a human-interpretable basis, and most of these problems don't directly interfere with this property. I'm also somewhat skeptical that SAEs actually do find a human-interpretable basis, but that's a separate question.

All the model's features will be linear combinations of activations in the standard basis, so it does have 'the right underlying features, but broken down differently from how the model is actually thinking about them'. 

I think this is a fair point. I also think there's a real sense in which it's useful to know that the model is representing the concepts "red" and "square," even if it's thinking of them in terms of the Red feature and the Square feature and your SAE found the "red square" feature. It's much harder to figure out what concepts the model is representing in human interpretable terms by staring at activations in a standard basis. There's a big difference between "we know what human-interpretable concepts the model is representing but not exactly what structure it uses to think of them" and "we just don't know what concepts the model is representing to begin with." I think if we could do the former well, that would already be amazing.

Put slightly more strongly:
The question of whether the model thinks in terms of "red square" or "red and "square" is moot, because the model does not actually think in terms of these concepts to begin with. The model thinks in its own language, and our job is to translate that language to our own. In this {red, blue} X {square, circle} space, looking at the attribution of "red square" and "red circle" to downstream features should give us the same result as looking at the attribution of "red" to downstream features, since red square and red circle encompass the full range of possibilities of things that can be red. It might be more convenient for us if we find the features that make the models attribution graph as simple as possible across a wide range of input examples, but there's no real sense in which we're misrepresenting its thought process.

Same for all your other points. Theoretically, can you solve problems 1, 2 and 3 with the standard basis by taking information about how the model is computing downstream into account in the right way? Sure. You'd 'take it into account' by finding some completely new basis.

Solving problem 1 could just entail adjusting the SAE basis while retaining most of its value! Solving problems 2 and 3 would require finding a basis which represents the same human-interpretable features but in a somewhat different way. Insofar as SAE features are actually human-interpretable (which I'm often skeptical of), I think this basis adds a ton of value. 

I am also often skeptical of SAEs, but I feel that the biggest problem is that they don't actually capture the sum of the concepts the model is representing in a human-interpretable way. If they actually did this correctly, I would happily forgo knowing the exact structure the model uses to think of them, and I would be alright if there were extra artifacts that made them harder to analyze.

(Also, oops, I didn't realize that by activation-space you meant one layer's activations only). 

This was a really thought-provoking post; thanks for writing it! I thought this was an unusually good attempt to articulate problems with the current interpretability paradigm and do some high-level thinking about what we could do differently. However, I think a few of the specific points are weaker than you make them seem in a way that somewhat contradicts the title of the post. I also may be misunderstanding parts, so please let me know if that’s the case. 

Problems 2 and 3 (the learned feature dictionary may not match the model’s feature dictionary, and activation space interpretability can fail to find compositional structure) both seem to be specific instances of ‘you are finding the right underlying features, but broken down differently from how the model is actually thinking about them’. This seems like a double edged sword to me. On one hand, it would be nice to know what level of abstraction the model is using. On the other hand, it’s useful to be able to analyze the computation at different levels of abstraction. And, if the model is breaking things down differently from the exact features you find, you may be able to piece this together from its downstream computation. I think these problems could just as easily be seen as a good thing, and they definitely don’t doom activation-space interpretability.

Problem 1, activations can contain structure of the data distribution that the models themselves don’t ‘know’ about, seems correct. However, this largely seems solvable by taking into account the effect of features on the output. Eg, as you later mention, attribution dictionary learning and E2E SAEs both seem like great attempts to tackle this problem. 

Re problem 4, function approximation creates artefacts: in general, it’s never possible to distinguish orthogonal directions without figuring out what class of input examples the feature fires on. For instance, in the $x^2$ example, you might end up with 5-10 different features activating to reconstruct something as simple as a representation of $x^2$. But these features would faithfully activate on inputs where the model needs to compute $x^2$, at least as well as any other normal feature activates on related inputs. Additionally, insofar as these 5-10 features are confusing, you can still find the x^2 feature in the network’s downstream computation, e.g. by using transcoders.

One problem here is that your SAEs could get overwhelmed with too many function approximation features, making it much more difficult to analyze. I don’t have strong priors on whether or not this is true, but from the empirical results, I tentatively don’t think it is?

Again, thanks for writing the post, and please let me know if I’m missing anything / if you have general thoughts on this comment (: