Is the distinction between "elephant + tiny" and "exampledon" primarily about the things the model does downstream? E.g. if none of the fifty dimensions of our subspace represent "has a bright purple spleen" but exampledons do, then the model might need to instead produce a "purple" vector as an output from an MLP whenever "exampledon" and "spleen" are present together.

Just to clarify, do you mean something like "elephant = grey + big + trunk + ears + African + mammal + wise" so to encode a tiny elephant you would  have "grey + tiny + trunk + ears + African + mammal + wise" which the model could still read off as 0.86 $\times$ elephant when relevant, but also tiny when relevant.

I think you should pay in Counterfactual Mugging, and this is one of the newcomblike problem classes that is most common in real life.

Example: you find a wallet on the ground. You can, from least to most pro social:

1. Take it and steal the money from it
2. Leave it where it is
3. Take it and make an effort to return it to its owner

Let's ignore the first option (suppose we're not THAT evil). The universe has randomly selected you today to be in the position where your only options are to spend some resources to no personal gain, or not. In a parallel universe, perhaps your pocket had the hole in it, and a random person has come across your wallet.

Firstly, what they might be thinking is "Would this person do the same for me?"

Secondly, in a society which wins, people return each others' wallets.

You might object that this is different from the Mugging, because you're directly helping someone else in this case. But I would counter that the Mugging is the true version of this problem, one where you have no crutch of empathy to help you, so your decision theory alone is tested.

I have added a link to the report now.

As to your point: this is one of the better arguments I've heard that welfare ranges might be similar between animals. Still I don't think it squares well with the actual nature of the brain. Saying there's a single suffering computation would make sense if the brain was like a CPU, where one core did the thinking, but actually all of the neurons in the brain are firing at once and doing computations in at the same time. So it makes much more sense to me to think that the more neurons are computing some sort of suffering, the greater the intensity of suffering.

Good point, edited a link to the Google Doc into the post.

From Rethink Priorities:

1. We used Monte Carlo simulations to estimate, for various sentience models and across eighteen organisms, the distribution of plausible probabilities of sentience.
2. We used a similar simulation procedure to estimate the distribution of welfare ranges for eleven of these eighteen organisms, taking into account uncertainty in model choice, the presence of proxies relevant to welfare capacity, and the organisms’ probabilities of sentience (equating this probability with the probability of moral patienthood)

Now with the disclaimer that I do think that RP are doing good and important work and are one of the few organizations seriously thinking about animal welfare priorities...

Their epistemics led them to do a Monte Carlo simulation to determine if organisms are capable of suffering (and if so, how much) then got a value of 5 shrimp = 1 human and then not bat an eye at this number.

Neither a physicalist nor a functionalist theory of consciousness can reasonably justify a number like this. Shrimp have 5 orders of magnitude fewer neurons than humans, so whether suffering is the result of a physical process or an information processing one, this implies that shrimp neurons do 4 orders of magnitude more of this process per second than human neurons. The authors get around this by refusing to stake themselves on any theory of consciousness.

The overall structure of the RP welfare range report, does not cut to the truth, instead the core mental motion seems to be to engage with as many existing piece of work as possible; credence is doled out to different schools of thought and pieces of evidence in a way which seems more like appeasement, lip-service, or a "well these guys have done some work, who are we disrespect them by ignoring it" attitude. Removal of noise is one of the most important functions of meta-analysis, and it is largely absent.

The result of this is an epistemology where the accuracy of a piece of work is a monotonically increasing function of the number of sources, theories, and lines of argument. Which is fine if your desired output is a very long Google doc, and a disclaimer to yourself (and, more cynically, your funders) that "No no, we did everything right, we reviewed all the evidence and took it all into account." but it's pretty bad if you want to actually be correct.

I grow increasingly convinced that the epistemics of EA are not especially good, worsening, and already insufficient to work on the relatively low-stakes and easy issue of animal welfare (as compared to AI x-risk).

If we approximate an MLP layer with a bilinear layer, then the effect of residual stream features on the MLP output can be expressed as a second order polynomial over the feature coefficients $f_i$. This will contain, for each feature, an $f_i^2 v_i+ f_i w_i$ term, which is "baked into" the residual stream after the MLP acts. Just looking at the linear term, this could be the source of Anthropic's observations of features growing, shrinking, and rotating in their original crosscoder paper. https://transformer-circuits.pub/2024/crosscoders/index.html

That might be true but I'm not sure it matters. For an AI to learn an abstraction it will have a finite amount of training time, context length, search space width (if we're doing parallel search like with o3) etc. and it's not clear how the abstraction height will scale with those.

Empirically, I think lots of people feel the experience of "hitting a wall" where they can learn abstraction level n-1 easily from class; abstraction level n takes significant study/help; abstraction level n+1 is not achievable for them within reasonable time. So it seems like the time requirement may scale quite rapidly with abstraction level?

I second this, it could easily be things which we might describe as "amount of information that can be processed at once, including abstractions" which is some combination of residual stream width and context length.

Imagine an AI can do a task that takes 1 hour. To remain coherent over 2 hours, it could either use twice as much working memory, or compress it into a higher level of abstraction. Humans seem to struggle with abstraction in a fairly continuous way (some people get stuck at algebra; some cs students make it all the way to recursion then hit a wall; some physics students can handle first quantization but not second quantization) which sorta implies there's a maximum abstraction stack height which a mind can handle, which varies continuously.

Only partially relevant, but it's exciting to hear a new John/David paper is forthcoming!