A more important difference - and a more genuine criticism of this analogy - is that mathematical physics is of course applied to the real, natural world. And perhaps there really is something about nature that makes it fundamentally amenable to mathematical description in a way that just won't apply to a large neural network trained by some sort of gradient descent? Indeed one does have the feeling that the endeavour we are focussing on would have to be underpinned by a hope that there is something sufficiently 'natural' about deep learning systems that will ultimately make at least some higher-level aspects of them amenable to mathematical analysis. Right now I cannot say how big of a problem this is.

There is no difference between natural phenomena and DNNs (LLMs, whatever). DNNs are 100% natural, don't you seriously believe there is something supernatural in their working? Hence, the criticism is invalid and the problem is non-existent.

See "AI as physical systems" for more on this. And in the same vein: "DNNs, GPUs, and their technoevolutionary lineages are agents".

I think that a lot of AI safety and AGI capability researchers are confused about this. They see information and computing as mathematical rather than physical. The physicalism of information and computation is a very important ontological commitment one has to make to deconfuse oneself about AI safety. If you wish to "take this pill", see Fields et al. (2022a), section 2.

I think the above confusion of the study of AI as mathematics (rather than physics and cognitive science -- natural sciences) leads you and some other people that new mathematics has to be developed to understand AI (I may misinterpret you, but it definitely seems from the post that you think this is true, e. g. from your example of algebraic topology). It might be that we will need new mathematics, but it's far from certain. As an example, take "A mathematical framework for transformer circuits": it doesn't develop new mathematics. It just uses existing mathematics: tensor algebra.

I think the research agenda of "weaving together theories of cognition and cognitive development, ML, deep learning, and interpretability through the abstraction-grounding stack" could plausibly lead to the sufficiently robust and versatile understanding of DNNs that we want^[1], without the need to develop much or any new mathematics along the way.

Here's what the "abstraction-grounding stack" looks like:

Many of the connections between theories of cognition and cognitive development at different levels of specificity are not established yet, and therefore present a lot of opportunities to verify the specific mechanistic interpretability theories:

• I haven’t heard of any research attempting to connect FEP/Active Inference (Fields et al. 2022a; Friston et al. 2022) with many theories of DNNs and deep learning, such as Balestriero (2018), Balestriero et al. (2019), or Roberts, Yaida & Hanin (2021). Note that Marciano et al. (2022) make such a connection between their own theory of DNNs and Active Inference. Apart from Active Inference, some other theories of intelligence and cognition (Boyd et al. 2022; Ma et al. 2022) are “ML-first” and thus cover both “general cognition” and “ML” levels of description at once.
• On the next level, interpretability theories (at least those authored by Anthropic) are not yet connected to any general theories of cognition, ML, DNNs, or deep learning. In particular, I think it would be very interesting to connect theories of polysemanticity (Elhage et al. 2022; Scherlis et al. 2022) with general theories of contextual/quantum cognition (see Basieva et al. (2021), Pothos & Busemeyer (2022), and Fields & Glazebrook (2022) for some recent reviews, and Fields et al. (2022a) and Tanaka et al. (2022) for examples of recent work).
• I make a hypothesis about a “skip-level” connection between quantum FEP (Fields et al. 2022a) and the circuits theory (Olah et al. 2020), identifying quantum reference frames with features in DNNs. This connection should be checked and compared with Marciano et al. (2022).
• All the theories of polysemanticity (Elhage et al. 2022; Scherlis et al. 2022) and grokking (Liu et al. 2022; Nanda et al. 2023) make associative connections to phase transitions in physics, but, as far as I can tell, none of these theories has yet been connected with physical theories of dynamical systems, criticality, and emergence more rigorously and attempted to propose some falsifiable predictions about the behaviour of NNs that would follow from the physical theories.
• Fields et al. (2022b) propose topological quantum neural networks (TQNNs) as a general framework of neuromorphic computation, and some theory of their development. Marciano et al. (2022) establish that DNNs are a semi-classical limit of TQNNs. To close the “developmental” arc, we should identify how the general theories of neuromorphic development, evolution, and selection map on the theories of feature and circuit development, evolution, and selection inside DNNs or, specifically, transformers.
• I don’t yet understand, how, but perhaps the connections between ML, fractional dynamics, and renormalisation group, identified by Niu et al. (2021), could help to better understand, verify, and contextualise some mechanistic interpretability theories as well.

1. ^{^}

   I'd restrain from saying "general theory" because general theories of DNNs already exist, and in large numbers. I'm not sure these theories are insufficient for our purposes and new general theories should be developed. However, what indeed is definitely lacking are the connections between the theories throughout the "abstraction-grounding stack". This is explained in more detail in the description of the agenda. See also the quote from here: "We (general intelligences) use science (or, generally speaking, construct any models of any phenomena) for the pragmatic purpose of being able to understand, predict and control it. Thus, none of the disciplinary perspectives on any phenomena should be regarded as the “primary” or “most correct” one for some metaphysical or ontological reasons. Also, this implies that if we can reach a sufficient level of understanding of some phenomenon (such as AGI) by opportunistically applying several existing theories then we don’t need to devise a new theory dedicated to this phenomenon specifically: we already solved our task without doing this step."

I'm skeptical, but I'd love to be convinced. I'm not sure that it's necessary to make interpretability scale, but it definitely strikes me as a potential trump card that would allow interpretability research to keep pace with capabilities research.

Here are a couple relatively unsorted thoughts (Keep in mind that I'm not a mathematician!):

• Deep learning as a field isn't exactly known for its rigor. I don't know of any rigorous theory that isn't as you say purely 'reactive', with none of it leading to any significant 'real world' results. As far as I can tell this isn't for a lack of trying either. This has made me doubt its mathematical tractability, whether it's because our current mathematical understanding is lacking or something else (DL not being as 'reductionist' as other fields?). How do you lean in this regard? You mentioned that you're not sure when it comes to how amenable interpretability itself is, but would you guess that it's more or less amenable than deep learning as a whole?
• How would success of this relate to capabilities research? It's a general criticism of interpretability research that it also leads to heightened capabilities, would this fare better/worse in that regard? I would have assumed that a developed rigorous theory of interpretability would probably also entail significant development of a rigorous theory of deep learning.
• How likely is it that the direction one may proceed in would be correct? You mention an example in mathematical physics, but note that it's perhaps relatively unimportant that this work was done for 'pure' reasons. This is surprising to me, as I thought that a major motivation for pure math research, like other blue sky research, is that it's often not apparent whether something will be useful until it's well developed. I think this is the similar to you mentioning that the small scale problems will not like the larger problem. You mention that this involves following one's nose mathematically, do you think this is possible in general or only for this case? If it's the latter, why do you think interpretability is specifically amenable to it?

Hey Spencer,

I realize this is a few months late and I'll preface with the caveat that this is a little outside my comfort area. I generally agree with your post and am interested in practical forward steps towards making this happen.

Apologies for how long this comment turned out to be. I'm excited about people working on this and based on my own niche interests have takes on what could be done/interesting in the future with this. I don't think any of this is info hazardy in theory but significant success in this work might be best kept on the down low.

Some questions:

• Why call this a theory of interpretability as opposed to a theory of neural networks? It seems to me that we want a theory of neural networks. To the extent this theory succeeds, we would expect various outcomes like the ability a priori to predict properties or details of neural networks. One such property we would expect, as with physical systems, might be to take measurements of some properties of a system and map them to other properties (such as the ideal gas equation). If you thought it was possible to develop a more specific theory only dealing with something like interpreting interventions in neural networks (eg causal tracing) then this might be better called a theory of interpretability. To the extent Mendel understood much of inheritance prior to us understanding the structure of DNA, there might be insights to be gained without building a theory from the ground up, but my sense is that's the kind of theory you want.
• have you made any progress on this topic or do you know anyone who would describe this explicitly as their research agenda? If so what areas are they working in.

Some points:

• One thing I struggle to understand, and might bet against is that this won't involve studying toy models. To my mind, Neel's grokking work, Toy Models of Superposition, Bhillal's "A Toy Model of Universality: Reverse Engineering How Networks Learn Group Operations" all seems to be contributing towards important factors that no comprehensive theory of Neural Networks could ignore. Specifically, the toy models of superposition work shows concepts like importance, sparsity and correlation of features being critical to representations in neural networks. Neel's progress measures work (and I think at least one other anthropic paper) address memorisation and generalization. Bhillal's work shows composition of features into actual algorithms. I emphasize all of these because even if you thought an eventual comprehensive mathematical theory would use different terminology or reinterpret those studied networks differently, it seems highly likely that it would use many of those ideas and essential that it explain similar phenomena. I think other people have already taughted heavily the successes of these works so I'll stop there and summarize this point as: if I were a mathematician, I'd start with these concepts and build them up to explain more complex systems. I'd focus on what is a feature, how to measure it's importance/sparsity efficiently, and then deal correlation and structure in simpler networks first and try to find a mathematical theory that started making good predictions in larger systems based on those terms. I definitely wouldn't start elsewhere.
• In retrospect, maybe I agree that following one's nose is also very important, but I'd want people to have these toy systems and various insights associated with them in mind. I'd also like to see these theoreticians work with empiricists to find and test ways of measuring quantities which would be the subject of the theorizing.
• "And perhaps there really is something about nature that makes it fundamentally amenable to mathematical description in a way that just won't apply to a large neural network trained by some sort of gradient descent? " I am tempted to take the view that nature is amenable in a very similar way to neural networks, but I believe that nature includes many domains which we can't/don't/struggle to make precise predictions or relations about. Specifically, I used to work in molecular systems biology which I think we understand way better than we used to but just isn't accessible in the way Newtonian physics is. Another example might be ecology where you might be able to predict migrations or other large effects but not specific details about ecosystems like specofic species population numbers more than 6 months in advance (this is totally unqualified conjecture to illustrate a point). So if Neural Networks are amenable to a mathematical description but only to the same extent molecular systems biology then we might be in for a hard time. The only recourse I think we have is to consider that we are have a few advantages with neural networks we don't have in other systems we want to model (see Chris Olahs blog post here https://colah.github.io/notes/interp-v-neuro/).

I should add that part of the value I see in my own work is to create slightly more complicated transformers than those which are studied the MI literature because rather than solving algorithmic tasks they solve spatial tasks based on instructions. These systems are certainly messier and may be a good challenge for a theory of neural network mechanisms/interpretability to make detailed predictions about. I think going straight from toy models to LLMs would be like going from studying viruses/virus replication to ecosystems.

Suggestions for next steps if you agreed with me about the value of existing work and building predictions of small systems first and trying to expand predictions to larger neural networks;

• Publishing/crowdsourcing/curating a literature review of anything that might be required to start building this theory (I'm guessing my knowledge of MI related work is just one small subsection of the relevant work and If I saw it as my job to create a broader theory I'd be reading way more widely.
• Identifying key goals for such a theory such as expanding or identifying quantitaties which such a theory would relate to each other, reliably retropredicting details of previously studied phenomena such as polysemanticity, grokking, memorization, generalization, which features get represented and then also making novel predictions such as about how mechanisms compose in neural networks ( not sure if anyone's published on this yet).

The two phenomena/concepts I am most interested in seeing either good empirical or theoretical work on are:

• Composition of mechanisms in neural networks (analogous to gene regulation). Something like explaining how Bhillal's extends to systems that solve more than one task at once where components can be reused and making predictions about how that gets structured in different architectures and as function of different details of training methods.
• Prediction centralization of processing (eg: the human prefrontal cortex). How when why would such a module arise and possibly how do we avoid it happening in LLMs if it hasn't started happening already.

+1 to making a concerted effort to have a mathematical theory +1 on using the term not killeveryonism