Lucius Bushnaq

AI notkilleveryoneism researcher at Apollo, focused on interpretability.

Wiki Contributions


I figured the probability adjustments the pump was making were modifying Everett branch amplitude ratios. Not probabilities as in reasoning tools to deal with incomplete knowledge of the world and logical uncertainty that tiny human brains use to predict how this situation might go based on looking at past 'base rates'. It's unclear to me how you could make the latter concept of an outcome pump a coherent thing at all. The former, on the other hand, seems like the natural outcome of the time machine setup described. If you turn back time when the branch doesn't have the outcome you like, only branches with the outcome you like will remain.

I can even make up a physically realisable model of an outcome pump that acts roughly like the one described in the story without using time travel at all. You just need a bunch of high quality sensors to take in data, an AI that judges from the observed data whether the condition set is satisfied, a tiny quantum random noise generator to respect the probability orderings desired, and a false vacuum bomb, which triggers immediately if the AI decides that the condition does not seem to be satisfied. The bomb works by causing a local decay of the metastable[1] electroweak vacuum. This is a highly energetic, self-sustaining process once it gets going, and spreads at the speed of light. Effectively destroying the entire future light-cone, probably not even leaving the possibility for atoms and molecules to ever form again in that volume of space.[2]

So when the AI triggers the bomb or turns back time, the amplitude of earth in that branch basically disappears. Leaving the users of the device to experience only the branches in which the improbable thing they want to have happen happens.

And causing a burning building with a gas supply in it to blow up strikes me as something you can maybe do with a lot less random quantum noise than making your mother phase through the building. Firefighter brains are maybe comparatively easy to steer with quantum noise as well, but that only works if there are any physically nearby enough to reach the building in time to save your mother at the moment the pump is activated. 

This is also why the pump has a limit on how improbable an event it can make happen. If the event has an amplitude of roughly the same size as the amplitude for the pump's sensors reporting bad data or otherwise causing the AI to make the wrong call, the pump will start being unreliable. If the event's amplitude is much lower than the amplitude for the pump malfunctioning, it basically can't do the job at all.

  1. ^

    In real life, it was an open question whether our local electroweak vacuum is in a metastable state last I checked, with the latest experimental evidence I'm aware from a couple of years ago tentatively (ca. 3 sigma I think?) pointing to yes, though that calculation is probably assuming Standard model physics the applicability of which people can argue to hell and back. But it sure seems like a pretty self-consistent way for the world to be, so we can just declare that the fictional universe works like that. Substitute strangelets or any other conjectured instant-earth-annihilation-method of your choice if you like.

  2. ^

    Because the mass terms for the elementary quantum fields would look all different now. Unclear to me that the bound structures of hadronic matter we are familiar with would still be a thing. 

Thinking the example through a bit further: In a ReLU layer, features are all confined to the positive quadrant. So superposed features computed in a ReLU layer all have positive inner product. So if I send the output of one ReLU layer implementing  AND gates in superposition directly to another ReLU layer implementing another  ANDs on a subset of the outputs of that previous layer[1], the assumption that input directions are equally likely to have positive and negative inner products is not satisfied.

Maybe you can fix this with bias setoffs somehow? Not sure at the moment. But as currently written, it doesn't seem like I can use the outputs of one layer performing a subset of ANDs as the inputs of another layer performing another subset of ANDs.

EDIT: Talked it through with Jake. Bias setoff can help, but it currently looks to us like you still end up with AND gates that share a variable systematically having positive sign in their inner product. Which might make it difficult to implement a valid general recipe for multi-step computation if you try to work out the details.

  1. ^

    A very central use case for a superposed boolean general computer. Otherwise you don't actually get to implement any serial computation.

Noting out loud that I'm starting to feel a bit worried about the culture-war-like tribal conflict dynamic between AIS/LW/EA and e/acc circles that I feel is slowly beginning to set in on our end as well, centered on Twitter but also present to an extent on other sites and in real life. The potential sanity damage to our own community and possibly future AI policy from this should it intensify is what concerns me most here.

People have tried to suck the rationalist diaspora into culture-war-like debates before, and I think the diaspora has done a reasonable enough job of surviving intact by not taking the bait much. But on this topic, many of us actually really care about both the content of the debate itself and what people outside the community think of it, and I fear it is making us more vulnerable to the algorithms' attempts to infect us than we have been in the past.

I think us going out of our way to keep standards high in memetic public spaces might possibly help some in keeping our own sanity from deteriorating. If we engage on Twitter, maybe we don't just refrain from lowering the level of debate and using arguments as soldiers but try to have a policy of actively commenting to correct the record when people of any affiliation make locally-invalid arguments against our opposition if we would counterfactually also correct the record were such a locally-invalid argument directed against us or our in-group. I think high status and high Twitter/Youtube-visible community members' behavior might end up having a particularly high impact on the eventual outcome here.

Having digested this a bit more, I've got a question regarding the noise terms, particularly for section 1.3 that deals with constructing general programs over sparse superposed variables.

Unfortunately, since the  are random vectors, their inner product will have a typical size of . So, on an input which has no features connected to neuron , the preactivation for that neuron will not be zero: it will be a sum of these interference terms, one for each feature that is connected to the neuron. Since the interference terms are uncorrelated and mean zero, they start to cause neurons to fire incorrectly when  neurons are connected to each neuron. Since each feature is connected to each neuron with probability  this means neurons start to misfire when [13].

It seems to me that the assumption of uncorrelated errors here is rather load-bearing. If you don't get uncorrelated errors over the inputs you actually care about, you are forced to scale back to connecting only  features to every neuron, correct? And the same holds for the construction right after this one, and probably most of the other constructions shown here?

And if you only get  connected features per neuron, you scale back to only being able to compute  arbitrary AND gates per layer, correct?

Now, the reason these errors are 'uncorrelated' is that the features were embedded as random vectors in our layer space. In other words, the distributions over which they are uncorrelated is the distribution of feature embeddings and sets of neurons chosen to connect to particular features. So for any given network, we draw from this distribution only once, when the weights of the network are set, and then we are locked into it.

So this noise will affect particular sets of inputs strongly, systematically, in the same direction every time. If I divide the set of features into two sets, where features in each half are embedded along directions that have a positive inner product with each other[1], I can't connect more than  from the same half to the same neuron without making it misfire, right? So if I want to implement a layer that performs  ANDs on exactly those features that happen to be embedded within the same set, I can't really do that. Now, for any given embedding, that's maybe only some particular sets of features which might not have much significance to each other. But then the embedding directions of features in later layers depend on what was computed and how in the earlier layers, and the limitations on what I can wire together apply every time. 

I am a bit worried that this and similar assumptions about stochasticity here might turn out to prevent you from wiring together the features you need to construct arbitrary programs in superposition, with 'noise' from multiple layers turning out to systematically interact in exactly such a way as to prevent you from computing too much general stuff. Not because I see a gears-level way this could happen right now, but because I think rounding off things to 'noise' that are actually systematic is one of these ways an exciting new theory can often go wrong and see a structure that isn't there, because you are not tracking the parts of the system that you have labeled noise and seeing how the systematics of their interactions constrain the rest of the system. 

Like making what seems like a blueprint for perpetual motion machine because you're neglecting to model some small interactions with the environment that seem like they ought not to affect the energy balance on average, missing how the energy losses/gains in these interactions are correlated with each other such that a gain at one step immediately implies a loss in another.

Aside from looking at error propagation more, maybe a way to resolve this might be to switch over to thinking about one particular set of weights instead of reasoning about the distribution the weights are drawn from? 

  1. ^

    E.g. pick some hyperplanes and declare everything on one side of all of them to be the first set.

Update February 2024: I left Ireland over a year ago, and the group is probably dead now, unfortunately. There's still an EA group around, which as of this writing seems quite active.

If the SAEs are not full-distribution competitive, I don't really trust that the features they're seeing are actually the variables being computed on in the sense of reflecting the true mechanistic structure of the learned network algorithm and that the explanations they offer are correct[1]. If I pick a small enough sub-distribution, I can pretty much always get perfect reconstruction no matter what kind of probe I use, because e.g. measured over a single token the network layers will have representation rank , and the entire network can be written as a rank- linear transform. So I can declare the activation vector at layer  to be the active "feature", use the single entry linear maps between SAEs to "explain" how features between layers map to each other, and be done. Those explanations will of course be nonsense and not at all extrapolate out of distributon. I can't use them to make a causal model that accurately reproduces the network's behavior or some aspect of it when dealing with a new prompt.

We don't train SAEs on literally single tokens, but I would be worried about the qualitative problem persisting. The network itself doesn't have a million different algorithms to perform a million different narrow subtasks. It has a finite description length. It's got to be using a smaller set of general algorithms that handle all of these different subtasks, at least to some extent. Likely more so for more powerful and general networks. If our "explanations" of the network then model it in terms of different sets of features and circuits for different narrow subtasks that don't fit together coherently to give a single good reconstruction loss over the whole distribution, that seems like a sign that our SAE layer activations didn't actually capture the general algorithms in the network. Thus, predictions about network behaviour made on the basis of inspecting causal relationships between these SAE activations might not be at all reliable, especially predictions about behaviours like instrumental deception which might be very mechanistically related to how the network does well on cross-domain generalisation.

  1. ^

    As in, that seems like a minimum requirement for the SAEs to fulfil. Not that this would be  to make me trust predictions about generalisation based on stories about SAE activations.

Our reconstruction scores were pretty good. We found GPT2 small achieves a cross entropy loss of about 3.3, and with reconstructed activations in place of the original activation, the CE Log Loss stays below 3.6. 

Unless my memory is screwing up the scale here, 0.3 CE Loss increase seems quite substantial? A 0.3 CE loss increase on the pile is roughly the difference between Pythia 410M and Pythia 2.8B. And do I see it right that this is the CE increase maximum for adding in one SAE, rather than all of them at the same time? So unless there is some very kind correlation in these errors where every SAE is failing to reconstruct roughly the same variance, and that variance at early layers is not used to compute the variance SAEs at later layers are capturing, the errors would add up? Possibly even worse than linearly? What CE loss do you get then?

Have you tried talking to the patched models a bit and compared to what the original model sounds like? Any discernible systematic differences in where that CE increase is changing the answers?

Can someone destroy my hope early by giving me the Molochian reasons why this change hasn't been made already and never will be?

MATS has steadily increased in quality over the past two years, and is now more prestigious than AISC. We also have Astra, and people who go directly to residencies at OpenAI, Anthropic, etc. One should expect that AISC doesn't attract the best talent.

  • If so, AISC might not make efficient use of mentor / PI time, which is a key goal of MATS and one of the reasons it's been successful.

AISC isn't trying to do what MATS does. Anecdotal, but for me, MATS could not have replaced AISC (spring 2022 iteration). It's also, as I understand it, trying to have a structure that works without established mentors, since that's one of the large bottlenecks constraining the training pipeline.

Also, did most of the past camps ever have lots of established mentors? I thought it was just the one in 2022 that had a lot? So whatever factors made all the past AISCs work and have participants sing their praises could just still be there.

Why does the founder, Remmelt Ellen, keep posting things described as "content-free stream of consciousness", "the entire scientific community would probably consider this writing to be crankery", or so obviously flawed it gets -46 karma? This seems like a concern especially given the philosophical/conceptual focus of AISC projects, and the historical difficulty in choosing useful AI alignment directions without empirical grounding.

He was posting cranky technical stuff during my camp iteration too. The program was still fantastic. So whatever they are doing to make this work seems able to function despite his crankery. With a five year track record, I'm not too worried about this factor.

All but 2 of the papers listed on Manifund as coming from AISC projects are from 2021 or earlier.

In the first link at least, there are only eight papers listed in total though.  With the first camp being in 2018, it doesn't really seem like the rate dropped much? So to the extent you believe your colleagues that the camp used to be good, I don't think the publication record is much evidence that it isn't anymore. Paper production apparently just does not track the effectiveness of the program much. Which doesn't surprise me, I don't think the rate of paper producion tracks the quality of AIS research orgs much either.

The impact assessment was commissioned by AISC, not independent. They also use the number of AI alignment researchers created as an important metric. But impact is heavy-tailed, so the better metric is value of total research produced. Because there seems to be little direct research, to estimate the impact we should count the research that AISC alums from the last two years go on to produce. Unfortunately I don't have time to do this.

Agreed on the metric being not great, and that an independently commissioned report would be better evidence (though who would have comissioned it?). But ultimately, most of what this report is apparently doing is just asking a bunch of AIS alumni what they thought of the camp and what they were up to, these days.  And then noticing that these alumni often really liked it and have apparently gone on to form a significant fraction of the ecosystem. And I don't think they even caught everyone. IIRC our AISC follow-up LTFF grant wasn't part of the spreadsheets until I wrote Remmelt that it wasn't there. 

I am not surprised by this. Like you, my experience is that most of my current colleagues who were part of AISC tell me it was really good. The survey is just asking around and noticing the same. 

I was the private donor who gave €5K. My reaction to hearing that AISC was not getting funding was that this seemed insane. The iteration I was in two years ago was fantastic for me, and the research project I got started on there is basically still continuing at Apollo now. Without AISC, I think there's a good chance I would never have become an AI notkilleveryoneism researcher. 

It feels like a very large number of people I meet in AIS today got their start in one AISC iteration or another, and many of them seem to sing its praises. I think 4/6 people currently on our interp team were part of one of the camps. I am not aware of any other current training program that seems to me like it would realistically replace AISC's role, though I admittedly haven't looked into all of them. I haven't paid much attention to the iteration that happened in 2023, but I happen to know a bunch of people who are in the current iteration and think trying to run a training program for them is an obviously good idea. 

I think MATS and co. are still way too tiny to serve all the ecosystem's needs, and under those circumstances, shutting down a training program with an excellent five year track record seems like an even more terrible idea than usual. On top of that, the research lead structure they've been trying out for this camp and the last one seems to me like it might have some chance of being actually scalable. I haven't spend much time looking at the projects for the current iteration yet, but from very brief surface exposure they didn't seem any worse on average than the ones in my iteration. Which impressed and surprised me, because these projects were not proposed by established mentors like the ones in my iteration were.  A far larger AISC wouldn't be able to replace what a program like MATS does, but it might be able to do what AISC6 did for me, and do it for far more people than anything structured like MATS realistically ever could. 

On a more meta point, I have honestly not been all that impressed with the average competency of the AIS funding ecosystem. I don't think it not funding a project is particularly strong evidence that the project is a bad idea. 


Well. Damn. 

As a vocal critic of the whole concept of superposition, this post has changed my mind a lot. An actual mathematical definition that doesn't depend on any fuzzy notions of what is 'human interpretable', and a start on actual algorithms for performing general, useful computation on overcomplete bases of variables.

Everything I've read on superposition before this was pretty much only outlining how you could store and access lots of variables from a linear space with sparse encoding, which isn't exactly a revelation. Every direction is a float, so of course the space can store about float precision to the -th power different states, which you can describe as superposed sparse features if you like. But I didn't need to use that lens to talk about the compression. I could just talk about good old non-overcomplete linear algebra bases instead. The  basis vectors in that linear algebra description being the compositional summary variables the sparse inputs got compressed into. If basically all we can do with the 'superposed variables' is make lookup tables of them, there didn't seem to me to be much need for the concept at all to reverse engineer neural networks. Just stick with the summary variables, summarising is what intelligence is all about.

If we can do actual,  general computation with the sparse variables? Computations with internal structure that we can't trivially describe just as well using  floats forming the non-overcomplete linear basis of a vector space? Well, that would change things. 

As you note, there's certainly work left to do here on the error propagation and checking for such algorithms in real networks. But even with this being an early proof of concept, I do now tentatively expect that better-performing implementations of this probably exist. And if such algorithms are possible, they sure do sound potentially extremely useful for an LLM's job. 

On my previous superposition-skeptical models, frameworks like the one described in this post are predicted to be basically impossible. Certainly way more cumbersome than this looks. So unless these ideas fall flat when more research is done on the error tolerance, I guess I was wrong. Oops.

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