A thread into which I’ll occasionally post notes on some ML(?) papers I’m reading

I think the world would probably be much better if everyone made a bunch more of their notes public. I intend to occasionally copy some personal notes on ML(?) papers into this thread. While I hope that the notes which I’ll end up selecting for being posted here will be of interest to some people, and that people will sometimes comment with their thoughts on the same paper and on my thoughts (please do tell me how I’m wrong, etc.), I expect that the notes here will not be significantly more polished than typical notes I write for myself and my reasoning will be suboptimal; also, I expect most of these notes won’t really make sense unless you’re also familiar with the paper — the notes will typically be companions to the paper, not substitutes.

I expect I’ll sometimes be meaner than some norm somewhere in these notes (in fact, I expect I’ll sometimes be simultaneously mean and wrong/confused — exciting!), but I should just say to clarify that I think almost all ML papers/posts/notes are trash, so me being mean to a particular paper might not be evidence that I think it’s worse than some average. If anything, the papers I post notes about had something worth thinking/writing about at all, which seems like a good thing! In particular, they probably contained at least one interesting idea!

So, anyway: I’m warning you that the notes in this thread will be messy and not self-contained, and telling you that reading them might not be a good use of your time :)

@misc{radhakrishnan2023mechanism,
title={Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features},
author={Adityanarayanan Radhakrishnan and Daniel Beaglehole and Parthe Pandit and Mikhail Belkin},
year={2023},
url = { https://arxiv.org/pdf/2212.13881.pdf }
}

The ansatz from the paper

Let hi(x)∈Rk denote the activation vector in layer i on input x∈Rd, with the input layer being at index i=1, so h1(x)=x. Let Wi be the weight matrix after activation layer i. Let fi be the function that maps from the ith activation layer to the output. Then their Deep Neural Feature Ansatz says that WTiWi∝∼1|D|∑x∈D∇fi(hi(x))∇fi(hi(x))T
(I’m somewhat confused here about them not mentioning the loss function at all — are they claiming this is reasonable for any reasonable loss function? Maybe just MSE? MSE seems to be the only loss function mentioned in the paper; I think they leave the loss unspecified in a bunch of places though.)

A singular vector version of the ansatz

Letting Wi=UΣVT be a SVD of Wi, we note that this is equivalent to VΣ2VT∝∼1|D|∑x∈D∇fi(hi(x))∇fi(hi(x))T, i.e., that the eigenvectors of the matrix M on the RHS are the right singular vectors. By the variational characterization of eigenvectors and eigenvalues (Courant-Fischer or whatever), this is the same as saying that right singular vectors of Wi are the highest orthonormal vTMv directions for the matrix M on the RHS. Plugging in the definition of M, this is equivalent to saying that the right singular vectors are the sequence of highest-variance directions of the data set of gradients ∇fi(hi(x)).

(I have assumed here that the linearity is precise, whereas really it is approximate. It’s probably true though that with some assumptions, the approximate initial statement implies an approximate conclusion too? Getting approx the same vecs out probably requires some assumption about gaps in singular values being big enough, because the vecs are unstable around equality. But if we’re happy getting a sequence of orthogonal vectors that gets variances which are nearly optimal, we should also be fine without this kind of assumption. (This is guessing atm.))

Getting rid of the Wi dependence on the RHS?

Assuming there isn’t an off-by-one error in the paper, we can pull some Wi term out of the RHS maybe? This is because applying the chain rule to the Jacobians of the transitions i→i+1→end gives ∇fi(hi(x))T=∇fi+1(hi+1(x))TWi, so 1|D|∑x∈D∇fi(hi(x))∇fi(hi(x))T=1|D|∑x∈DWTi∇fi+1(hi+1(x))∇fi+1(hi+1(x))TWi.

Wait, so the claim is just WTiWi∝∼WTi(∑x∈D∇fi+1(hi+1(x))∇fi+1(hi+1(x))T)Wi which, assuming Wi is invertible, should be the same as ∑x∈D∇fi+1(hi+1(x))∇fi+1(hi+1(x))T∝∼I. But also, they claim that it is WTi+1Wi+1? Are they secretly approximating everything with identity matrices?? This doesn’t seem to be the case from their Figure 2 though.

Oh oops I guess I forgot about activation functions here! There should be extra diagonal terms for jacobians of preactivations->activations in ∇fi(hi(x))T=∇fi+1(hi+1(x))TWi, i.e., it should really say ∇fi(hi(x))T=∇fi+1(hi+1(x))TDi+1(x)Wi. We now instead get WTiWi∝∼WTi(∑x∈DDi+1(x)∇fi+1(hi+1(x))∇fi+1(hi+1(x))TDi+1(x))Wi.
This should be the same as ∑x∈DDi+1(x)∇fi+1(hi+1(x))∇fi+1(hi+1(x))TDi+1(x)∝∼I which, with pi denoting preactivations in layer i and fp,i denoting the function from these preactivations to the output, is the same as ∑x∈D∇fp,i+1(pi+1(x))∇fp,i+1(pi+1(x))T∝∼I. This last thing also totally works with activation functions other than ReLU — one can get this directly from the Jacobian calculation. I made the ReLU assumption earlier because I thought for a bit that one can get something further in that case; I no longer think this, but I won’t go back and clean up the presentation atm.

Anyway, a takeaway is that the Deep Neural Feature Ansatz is equivalent to the (imo cleaner) ansatz that the set of gradients of the output wrt the pre-activations of any layer is close to being a tight frame (in other words, the gradients are in isotropic position; in other words still, the data matrix of the gradients is a constant times a semi-orthogonal matrix). (Note that the closeness one immediately gets isn’t in L2 to a tight frame, it’s just in the quantity defining the tightness of a frame, but I’d guess that if it matters, one can also conclude some kind of closeness in L2 from this (related).) This seems like a nicer fundamental condition because (1) we’ve intuitively canceled terms and (2) it now looks like a generic-ish condition, looks less mysterious, though idk how to argue for this beyond some handwaving about genericness, about other stuff being independent, sth like that.

proof of the tight frame claim from the previous condition: Note that ∑x∈D∇fp,i+1(pi+1(x))∇fp,i+1(pi+1(x))T∝∼Iclearly implies that the L2 mass in any direction is the same, but also the L2 mass being the same in any direction implies the above (because then, letting the SVD of the matrix with these gradients in its columns be U′Σ′V′T, the above is U′Σ′Σ′TU′T=σ2I, where we used the fact that Σ=σI).

Some questions

Can one come up with some similar ansatz identity for the left singular vectors of Wi? One point of tension/interest here is that an ansatz identity for WiWTi would constrain the left singular vectors of Wi together with its singular values, but the singular values are constrained already by the deep neural feature ansatz. So if there were another identity for WiWTi in terms of some gradients, we’d get a derived identity from equality between the singular values defined in terms of those gradients and the singular values defined in terms of the Deep Neural Feature Ansatz. Or actually, there probably won’t be an interesting identity here since given the cancellation above, it now feels like nothing about Wi is really pinned down by ‘gradients independent of Wi’ by the DNFA? Of course, some Wi-dependence remains even in the i+1 gradients because the preactivations at which further gradients get evaluated are somewhat Wi-dependent, so I guess it’s not ruled out that the DNFA constrains something interesting about Wi? But anyway, all this seems to undermine the interestingness of the DNFA, as well as the chance of there being an interesting similar ansatz for the left singular vectors of Wi.

Can one heuristically motivate that the preactivation gradients above should indeed be close to being in isotropic position? Can one use this reduction to provide simpler proofs of some of the propositions in the paper which say that the DNFA is exactly true in certain very toy cases?

The authors claim that the DNFA is supposed to somehow elucidate feature learning (indeed, they claim it is a mechanism of feature learning?). I take ‘feature learning’ to mean something like which neuronal functions (from the input) are created or which functions are computed in a layer in some broader sense (maybe which things are made linearly readable?) or which directions in an activation space to amplify or maybe less precisely just the process of some internal functions (from the input to internal activations) being learned of something like that, which happens in finite networks apparently in contrast to infinitely wide networks or NTK models or something like that which I haven’t yet understood? I understand that their heuristic identity on the surface connects something about a weight matrix to something about gradients, but assuming I’ve not made some index-off-by-one error or something, it seems to probably not really be about that at all, since the weight matrix sorta cancels out — if it’s true for one Wi, it would maybe also be true with any other Wi replacing it, so it doesn’t really pin down Wi? (This might turn out to be false if the isotropy of preactivation gradients is only true for a very particular choice of Wi.) But like, ignoring that counter, I guess their point is that the directions which get stretched most by the weight matrix in a layer are the directions along which it would be the best to move locally in that activation space to affect the output? (They don’t explain it this way though — maybe I’m ignorant of some other meaning having been attributed to WTiWi in previous literature or something.) But they say “Informally, this mechanism corresponds to the approach of progressively re-weighting features in proportion to the influence they have on the predictions.”. I guess maybe this is an appropriate description of the math if they are talking about reweighting in the purely linear sense, and they take features in the input layer to be scaleless objects or something? (Like, if we take features in the input activation space to each have some associated scale, then the right singular vector identity no longer says that most influential features get stretched the most.) I wish they were much more precise here, or if there isn’t a precise interesting philosophical thing to be deduced from their math, much more honest about that, much less PR-y.

So, in brief, instead of “informally, this mechanism corresponds to the approach of progressively re-weighting features in proportion to the influence they have on the predictions,” it seems to me that what the math warrants would be sth more like “The weight matrix reweights stuff; after reweighting, the activation space is roughly isotropic wrt affecting the prediction (ansatz); so, the stuff that got the highest weight has most effect on the prediction now.” I’m not that happy with this last statement either, but atm it seems much more appropriate than their claim.

I guess if I’m not confused about something major here (plausibly I am), one could probably add 1000 experiments (e.g. checking that the isotropic version of the ansatz indeed equally holds in a bunch of models) and write a paper responding to them. If you’re reading this and this seems interesting to you, feel free to do that — I’m also probably happy to talk to you about the paper.

typos in the paper

indexing error in the first displaymath in Sec 2: it probably should say WL″, not WL+1″

I will be appropriating terminology from the Waluigi post. I hereby put forward the hypothesis that virtue ethics endorses an action iff it is what the better one of Luigi and Waluigi would do, where Luigi and Waluigi are the ones given by the posterior semiotic measure in the given situation, and “better” is defined according to what some [possibly vaguely specified] consequentialist theory thinks about the long-term expected effects of this particular Luigi vs the long-term effects of this particular Waluigi. One intuition here is that a vague specification could be more fine if we are not optimizing for it very hard, instead just obtaining a small amount of information from it per decision.

In this sense, virtue ethics literally equals continuously choosing actions as if coming from a good character. Furthermore, considering the new posterior semiotic measure after a decision, in this sense, virtue ethics is about cultivating a virtuous character in oneself. Virtue ethics is about rising to the occasion (i.e. the situation, the context). It’s about constantly choosing the Luigi in oneself over the Waluigi in oneself (or maybe the Waluigi over the Luigi if we define “Luigi” as the more likely of the two and one has previously acted badly in similar cases or if the posterior semiotic measure is otherwise malign). I currently find this very funny, and, if even approximately correct, also quite cool.

Here are some issues/considerations/questions that I intend to think more about:

What’s a situation? For instance, does it encompass the agent’s entire life history, or are we to make it more local?

Are we to use the agent’s own semiotic measure, or some objective semiotic measure?

This grounds virtue ethics in consequentialism. Can we get rid of that? Even if not, I think this might be useful for designing safe agents though.

Does this collapse into cultivating a vanilla consequentialist over many choices? Can we think of examples of prompting regimes such that collapse does not occur? The vague motivating hope I have here is that in the trolley problem case with the massive man, the Waluigi pushing the man is a corrupt psycho, and not a conflicted utilitarian.

Even if this doesn’t collapse into consequentialism from these kinds of decisions, I’m worried about it being stable under reflection, I guess because I’m worried about the likelihood of virtue ethics being part of an agent in reflective equilibrium. It would be sad if the only way to make this work would be to only ever give high semiotic measure to agents that don’t reflect much on values.

Wait, how exactly do we get Luigi and Waluigi from the posterior semiotic measure? Can we just replace this with picking the best character from the most probable few options according to the semiotic measure? Wait, is this just quantilization but funnier? I think there might be some crucial differences. And regardless, it’s interesting if virtue ethics turns out to be quantilization-but-funnier.

More generally, has all this been said already?

Is there a nice restatement of this in shard theory language?

A small observation about the AI arms race in conditions of good infosec and collaboration

Suppose we are in a world where most top AI capabilities organizations are refraining from publishing their work (this could be the case because of safety concerns, or because of profit motives) + have strong infosec which prevents them from leaking insights about capabilities in other ways. In this world, it seems sort of plausible that the union of the capabilities insights of people at top labs would allow one to train significantly more capable models than the insights possessed by any single lab alone would allow one to train. In such a world, if the labs decide to cooperate once AGI is nigh, this could lead to a significantly faster increase in capabilities than one might have expected otherwise.

(I doubt this is a novel thought. I did not perform an extensive search of the AI strategy/governance literature before writing this.)

I’m updating my estimate of the return on investment into culture wars from being an epsilon fraction compared to canonical EA cause areas to epsilon+delta. This has to do with cases where AI locks in current values extrapolated “correctly” except with too much weight put on the practical (as opposed to the abstract) layer of current preferences. What follows is a somewhat more detailed status report on this change.

For me (and I’d guess for a large fraction of autistic altruistics multipliers), the general feels regarding [being a culture war combatant in one’s professional capacity] seem to be that while the questions fought over have some importance, the welfare-produced-per-hour-worked from doing direct work is at least an order of magnitude smaller than the same quantities for any canonical cause area (also true for welfare/USD). I’m fairly certain one can reach this conclusion from direct object-level estimates, as I imagine e.g. OpenPhil has done, although I admit I haven’t carried out such calculations with much care myself. Considering the incentives of various people involved should also support this being a lower welfare-per-hour-worked cause area (whether an argument along these lines gives substantive support to the conclusion that there is an order-of-magnitude difference appears less clear).

So anyway, until today part of my vague cloud of justification for these feels was that “and anyway, it’s fine if this culture war stuff is fixed in 30 years, after we have dealt with surviving AGI”. The small realization I had today was that maybe a significant fraction of the surviving worlds are those where something like corrigibility wasn’t attainable but AI value extrapolation sort of worked out fine, i.e. with the values that got locked in being sort of fine, but the relative weights of object-level intuitions/preferences was kinda high compared to the weight on simplicity/[meta-level intuitions], like in particular maybe the AI training did some Bayesian-ethics-evidential-double-counting of object-level intuitions about 10^10 similar cases (I realize it’s quite possible that this last clause won’t make sense to many readers, but unfortunately I won’t provide an explanation here; I intend to write about a few ideas on this picture of Bayesian ethics at some later time, but I want to read Beckstead’s thesis first, which I haven’t done yet; anyway the best I can offer is that I estimate a 75% of you understanding the rough idea I have in mind (which does not necessarily imply that the idea can actually be unfolded into a detailed picture that makes sense), conditional on understanding my writing in general and conditional on not having understood this clause yet, after reading Beckstead’s thesis; also: woke: Bayesian ethics, bespoke: INFRABAYESIAN ETHICS, am I right folks).

So anyway, finally getting to the point of all this at the end of the tunnel, in such worlds we actually can’t fix this stuff later on, because all the current opinions on culture war issues got locked in.

(One could argue that we can anyway be quite sure that this consideration matters little, because most expected value is not in such kinda-okay worlds, because even if these were 99% percent of the surviving worlds, assuming fun theory makes sense or simulated value-bearing minds are possible, there will be amazingly more value in each world where AGI worked out really well, as compared to a world tiled with Earth society 2030. But then again, this counterargument could be iffy to some, in sort of the same way in which fanaticism (in Bostrom’s sense) or the St. Petersburg paradox feel iffy to some, or perhaps in another way. I won’t be taking a further position on this at the moment.)

## A thread into which I’ll occasionally post notes on some ML(?) papers I’m reading

I think the world would probably be much better if everyone made a bunch more of their notes public. I intend to occasionally copy some personal notes on ML(?) papers into this thread. While I hope that the notes which I’ll end up selecting for being posted here will be of interest to some people, and that people will sometimes comment with their thoughts on the same paper and on my thoughts (please do tell me how I’m wrong, etc.), I expect that the notes here will not be significantly more polished than typical notes I write for myself and my reasoning will be suboptimal; also, I expect most of these notes won’t really make sense unless you’re also familiar with the paper — the notes will typically be companions to the paper, not substitutes.

I expect I’ll sometimes be meaner than some norm somewhere in these notes (in fact, I expect I’ll sometimes be simultaneously mean and wrong/confused — exciting!), but I should just say to clarify that I think almost all ML papers/posts/notes are trash, so me being mean to a particular paper might not be evidence that I think it’s worse than some average. If anything, the papers I post notes about had something worth thinking/writing about at all, which seems like a good thing! In particular, they probably contained at least one interesting idea!

So, anyway: I’m warning you that the notes in this thread will be messy and not self-contained, and telling you that reading them might not be a good use of your time :)

## The Deep Neural Feature Ansatz

@misc{radhakrishnan2023mechanism, title={Mechanism of feature learning in deep fully connected networks and kernel machines that recursively learn features}, author={Adityanarayanan Radhakrishnan and Daniel Beaglehole and Parthe Pandit and Mikhail Belkin}, year={2023}, url = { https://arxiv.org/pdf/2212.13881.pdf } }

## The ansatz from the paper

Let hi(x)∈Rk denote the activation vector in layer i on input x∈Rd, with the input layer being at index i=1, so h1(x)=x. Let Wi be the weight matrix after activation layer i. Let fi be the function that maps from the ith activation layer to the output. Then their

Deep Neural Feature Ansatzsays that WTiWi∝∼1|D|∑x∈D∇fi(hi(x))∇fi(hi(x))T (I’m somewhat confused here about them not mentioning the loss function at all — are they claiming this is reasonable for any reasonable loss function? Maybe just MSE? MSE seems to be the only loss function mentioned in the paper; I think they leave the loss unspecified in a bunch of places though.)## A singular vector version of the ansatz

Letting Wi=UΣVT be a SVD of Wi, we note that this is equivalent to VΣ2VT∝∼1|D|∑x∈D∇fi(hi(x))∇fi(hi(x))T, i.e., that the eigenvectors of the matrix M on the RHS are the right singular vectors. By the variational characterization of eigenvectors and eigenvalues (Courant-Fischer or whatever), this is the same as saying that right singular vectors of Wi are the highest orthonormal vTMv directions for the matrix M on the RHS. Plugging in the definition of M, this is equivalent to saying that the right singular vectors are the sequence of highest-variance directions of the data set of gradients ∇fi(hi(x)).

(I have assumed here that the linearity is precise, whereas really it is approximate. It’s probably true though that with some assumptions, the approximate initial statement implies an approximate conclusion too? Getting approx the same vecs out probably requires some assumption about gaps in singular values being big enough, because the vecs are unstable around equality. But if we’re happy getting a sequence of orthogonal vectors that gets variances which are nearly optimal, we should also be fine without this kind of assumption. (This is guessing atm.))

## Getting rid of the Wi dependence on the RHS?

Assuming there isn’t an off-by-one error in the paper, we can pull some Wi term out of the RHS maybe? This is because applying the chain rule to the Jacobians of the transitions i→i+1→end gives ∇fi(hi(x))T=∇fi+1(hi+1(x))TWi, so 1|D|∑x∈D∇fi(hi(x))∇fi(hi(x))T=1|D|∑x∈DWTi∇fi+1(hi+1(x))∇fi+1(hi+1(x))TWi.

Wait, so the claim is just WTiWi∝∼WTi(∑x∈D∇fi+1(hi+1(x))∇fi+1(hi+1(x))T)Wi which, assuming Wi is invertible, should be the same as ∑x∈D∇fi+1(hi+1(x))∇fi+1(hi+1(x))T∝∼I. But also, they claim that it is WTi+1Wi+1? Are they secretly approximating everything with identity matrices?? This doesn’t seem to be the case from their Figure 2 though.

Oh oops I guess I forgot about activation functions here! There should be extra diagonal terms for jacobians of preactivations->activations in ∇fi(hi(x))T=∇fi+1(hi+1(x))TWi, i.e., it should really say ∇fi(hi(x))T=∇fi+1(hi+1(x))TDi+1(x)Wi. We now instead get WTiWi∝∼WTi(∑x∈DDi+1(x)∇fi+1(hi+1(x))∇fi+1(hi+1(x))TDi+1(x))Wi. This should be the same as ∑x∈DDi+1(x)∇fi+1(hi+1(x))∇fi+1(hi+1(x))TDi+1(x)∝∼I which, with pi denoting preactivations in layer i and fp,i denoting the function from these preactivations to the output, is the same as ∑x∈D∇fp,i+1(pi+1(x))∇fp,i+1(pi+1(x))T∝∼I. This last thing also totally works with activation functions other than ReLU — one can get this directly from the Jacobian calculation. I made the ReLU assumption earlier because I thought for a bit that one can get something further in that case; I no longer think this, but I won’t go back and clean up the presentation atm.

Anyway, a takeaway is that the Deep Neural Feature Ansatz is equivalent to the (imo cleaner) ansatz that the set of gradients of the output wrt the pre-activations of any layer is close to being a tight frame (in other words, the gradients are in isotropic position; in other words still, the data matrix of the gradients is a constant times a semi-orthogonal matrix). (Note that the closeness one immediately gets isn’t in L2 to a tight frame, it’s just in the quantity defining the tightness of a frame, but I’d guess that if it matters, one can also conclude some kind of closeness in L2 from this (related).) This seems like a nicer fundamental condition because (1) we’ve intuitively canceled terms and (2) it now looks like a generic-ish condition, looks less mysterious, though idk how to argue for this beyond some handwaving about genericness, about other stuff being independent, sth like that.

proof of the tight frame claim from the previous condition: Note that ∑x∈D∇fp,i+1(pi+1(x))∇fp,i+1(pi+1(x))T∝∼Iclearly implies that the L2 mass in any direction is the same, but also the L2 mass being the same in any direction implies the above (because then, letting the SVD of the matrix with these gradients in its columns be U′Σ′V′T, the above is U′Σ′Σ′TU′T=σ2I, where we used the fact that Σ=σI).

## Some questions

Can one come up with some similar ansatz identity for the left singular vectors of Wi? One point of tension/interest here is that an ansatz identity for WiWTi would constrain the left singular vectors of Wi together with its singular values, but the singular values are constrained already by the deep neural feature ansatz. So if there were another identity for WiWTi in terms of some gradients, we’d get a derived identity from equality between the singular values defined in terms of those gradients and the singular values defined in terms of the Deep Neural Feature Ansatz. Or actually, there probably won’t be an interesting identity here since given the cancellation above, it now feels like nothing about Wi is really pinned down by ‘gradients independent of Wi’ by the DNFA? Of course, some Wi-dependence remains even in the i+1 gradients because the preactivations at which further gradients get evaluated are somewhat Wi-dependent, so I guess it’s not ruled out that the DNFA constrains something interesting about Wi? But anyway, all this seems to undermine the interestingness of the DNFA, as well as the chance of there being an interesting similar ansatz for the left singular vectors of Wi.

Can one heuristically motivate that the preactivation gradients above should indeed be close to being in isotropic position? Can one use this reduction to provide simpler proofs of some of the propositions in the paper which say that the DNFA is exactly true in certain very toy cases?

The authors claim that the DNFA is supposed to somehow elucidate feature learning (indeed, they claim it is a mechanism of feature learning?). I take ‘feature learning’ to mean something like which neuronal functions (from the input) are created or which functions are computed in a layer in some broader sense (maybe which things are made linearly readable?) or which directions in an activation space to amplify or maybe less precisely just the process of some internal functions (from the input to internal activations) being learned of something like that, which happens in finite networks apparently in contrast to infinitely wide networks or NTK models or something like that which I haven’t yet understood? I understand that their heuristic identity on the surface connects something about a weight matrix to something about gradients, but assuming I’ve not made some index-off-by-one error or something, it seems to probably not really be about that at all, since the weight matrix sorta cancels out — if it’s true for one Wi, it would maybe also be true with any other Wi replacing it, so it doesn’t really pin down Wi? (This might turn out to be false if the isotropy of preactivation gradients is only true for a very particular choice of Wi.) But like, ignoring that counter, I guess their point is that the directions which get stretched most by the weight matrix in a layer are the directions along which it would be the best to move locally in that activation space to affect the output? (They don’t explain it this way though — maybe I’m ignorant of some other meaning having been attributed to WTiWi in previous literature or something.) But they say “Informally, this mechanism corresponds to the approach of progressively re-weighting features in proportion to the influence they have on the predictions.”. I guess maybe this is an appropriate description of the math if they are talking about reweighting in the purely linear sense, and they take features in the input layer to be scaleless objects or something? (Like, if we take features in the input activation space to each have some associated scale, then the right singular vector identity no longer says that most influential features get stretched the most.) I wish they were much more precise here, or if there isn’t a precise interesting philosophical thing to be deduced from their math, much more honest about that, much less PR-y.

So, in brief, instead of “informally, this mechanism corresponds to the approach of progressively re-weighting features in proportion to the influence they have on the predictions,” it seems to me that what the math warrants would be sth more like “The weight matrix reweights stuff; after reweighting, the activation space is roughly isotropic wrt affecting the prediction (ansatz); so, the stuff that got the highest weight has most effect on the prediction now.” I’m not that happy with this last statement either, but atm it seems much more appropriate than their claim.

I guess if I’m not confused about something major here (plausibly I am), one could probably add 1000 experiments (e.g. checking that the isotropic version of the ansatz indeed equally holds in a bunch of models) and write a paper responding to them. If you’re reading this and this seems interesting to you, feel free to do that — I’m also probably happy to talk to you about the paper.

## typos in the paper

indexing error in the first displaymath in Sec 2: it probably should say WL″, not WL+1″

## An attempt at a specification of virtue ethics

I will be appropriating terminology from the Waluigi post. I hereby put forward the hypothesis that virtue ethics endorses an action iff it is what the better one of Luigi and Waluigi would do, where Luigi and Waluigi are the ones given by the posterior semiotic measure in the given situation, and “better” is defined according to what some [possibly vaguely specified] consequentialist theory thinks about the long-term expected effects of this particular Luigi vs the long-term effects of this particular Waluigi. One intuition here is that a vague specification could be more fine if we are not optimizing for it very hard, instead just obtaining a small amount of information from it per decision.

In this sense, virtue ethics literally equals continuously choosing actions as if coming from a good character. Furthermore, considering the new posterior semiotic measure after a decision, in this sense, virtue ethics is about cultivating a virtuous character in oneself. Virtue ethics is about rising to the occasion (i.e. the situation, the context). It’s about constantly choosing the Luigi in oneself over the Waluigi in oneself (or maybe the Waluigi over the Luigi if we define “Luigi” as the more likely of the two and one has previously acted badly in similar cases or if the posterior semiotic measure is otherwise malign). I currently find this very funny, and, if even approximately correct, also quite cool.

Here are some issues/considerations/questions that I intend to think more about:

What’s a situation? For instance, does it encompass the agent’s entire life history, or are we to make it more local?

Are we to use the agent’s own semiotic measure, or some objective semiotic measure?

This grounds virtue ethics in consequentialism. Can we get rid of that? Even if not, I think this might be useful for designing safe agents though.

Does this collapse into cultivating a vanilla consequentialist over many choices? Can we think of examples of prompting regimes such that collapse does not occur? The vague motivating hope I have here is that in the trolley problem case with the massive man, the Waluigi pushing the man is a corrupt psycho, and not a conflicted utilitarian.

Even if this doesn’t collapse into consequentialism from these kinds of decisions, I’m worried about it being stable under reflection, I guess because I’m worried about the likelihood of virtue ethics being part of an agent in reflective equilibrium. It would be sad if the only way to make this work would be to only ever give high semiotic measure to agents that don’t reflect much on values.

Wait, how exactly do we get Luigi and Waluigi from the posterior semiotic measure? Can we just replace this with picking the best character from the most probable few options according to the semiotic measure? Wait, is this just quantilization but funnier? I think there might be some crucial differences. And regardless, it’s interesting if virtue ethics turns out to be quantilization-but-funnier.

More generally, has all this been said already?

Is there a nice restatement of this in shard theory language?

## A small observation about the AI arms race in conditions of good infosec and collaboration

Suppose we are in a world where most top AI capabilities organizations are refraining from publishing their work (this could be the case because of safety concerns, or because of profit motives) + have strong infosec which prevents them from leaking insights about capabilities in other ways. In this world, it seems sort of plausible that the union of the capabilities insights of people at top labs would allow one to train significantly more capable models than the insights possessed by any single lab alone would allow one to train. In such a world, if the labs decide to cooperate once AGI is nigh, this could lead to a significantly faster increase in capabilities than one might have expected otherwise.

(I doubt this is a novel thought. I did not perform an extensive search of the AI strategy/governance literature before writing this.)

I’m updating my estimate of the return on investment into culture wars from being an epsilon fraction compared to canonical EA cause areas to epsilon+delta. This has to do with cases where AI locks in current values extrapolated “correctly” except with too much weight put on the practical (as opposed to the abstract) layer of current preferences. What follows is a somewhat more detailed status report on this change.

For me (and I’d guess for a large fraction of

~~autistic altruistics~~multipliers), the general feels regarding [being a culture war combatant in one’s professional capacity] seem to be that while the questions fought over have some importance, the welfare-produced-per-hour-worked from doing direct work is at least an order of magnitude smaller than the same quantities for any canonical cause area (also true for welfare/USD). I’m fairly certain one can reach this conclusion from direct object-level estimates, as I imagine e.g. OpenPhil has done, although I admit I haven’t carried out such calculations with much care myself. Considering the incentives of various people involved should also support this being a lower welfare-per-hour-worked cause area (whether an argument along these lines gives substantive support to the conclusion that there is an order-of-magnitude difference appears less clear).So anyway, until today part of my vague cloud of justification for these feels was that “and anyway, it’s fine if this culture war stuff is fixed in 30 years, after we have dealt with surviving AGI”. The small realization I had today was that maybe a significant fraction of the surviving worlds are those where something like corrigibility wasn’t attainable but AI value extrapolation sort of worked out fine, i.e. with the values that got locked in being sort of fine, but the relative weights of object-level intuitions/preferences was kinda high compared to the weight on simplicity/[meta-level intuitions], like in particular maybe the AI training did some Bayesian-ethics-evidential-double-counting of object-level intuitions about 10^10 similar cases (I realize it’s quite possible that this last clause won’t make sense to many readers, but unfortunately I won’t provide an explanation here; I intend to write about a few ideas on this picture of Bayesian ethics at some later time, but I want to read Beckstead’s thesis first, which I haven’t done yet; anyway the best I can offer is that I estimate a 75% of you understanding the rough idea I have in mind (which does not necessarily imply that the idea can actually be unfolded into a detailed picture that makes sense), conditional on understanding my writing in general and conditional on not having understood this clause yet, after reading Beckstead’s thesis; also: woke: Bayesian ethics, bespoke: INFRABAYESIAN ETHICS, am I right folks).

So anyway, finally getting to the point of all this at the end of the tunnel, in such worlds we actually can’t fix this stuff later on, because all the current opinions on culture war issues got locked in.

(One could argue that we can anyway be quite sure that this consideration matters little, because most expected value is not in such kinda-okay worlds, because even if these were 99% percent of the surviving worlds, assuming fun theory makes sense or simulated value-bearing minds are possible, there will be amazingly more value in each world where AGI worked out really well, as compared to a world tiled with Earth society 2030. But then again, this counterargument could be iffy to some, in sort of the same way in which fanaticism (in Bostrom’s sense) or the St. Petersburg paradox feel iffy to some, or perhaps in another way. I won’t be taking a further position on this at the moment.)

I proposed a method for detecting cheating in chess; cross-posting it here in the hopes of maybe getting better feedback than on reddit: https://www.reddit.com/r/chess/comments/xrs31z/a_proposal_for_an_experiment_well_data_analysis/