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 } }
Let denote the activation vector in layer on input , with the input layer being at index , so . Let be the weight matrix after activation layer . Let be the function that maps from the th activation layer to the output. Then their Deep Neural Feature Ansatz says that (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.)
Letting be a SVD of , we note that this is equivalent to i.e., that the eigenvectors of the matrix 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 are the highest orthonormal directions for the matrix on the RHS. Plugging in the definition of , this is equivalent to saying that the right singular vectors are the sequence of highest-variance directions of the data set of gradients .
(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.))
Assuming there isn't an off-by-one error in the paper, we can pull some term out of the RHS maybe? This is because applying the chain rule to the Jacobians of the transitions gives , so
Wait, so the claim is just which, assuming is invertible, should be the same as . But also, they claim that it is ? 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 , i.e., it should really say We now instead get This should be the same as which, with denoting preactivations in layer and denoting the function from these preactivations to the output, is the same as 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 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 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 clearly implies that the mass in any direction is the same, but also the 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 , the above is , where we used the fact that ).
indexing error in the first displaymath in Sec 2: it probably should say '', not ''
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:
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/