From the inside, it feels like I want to know what's going on as a terminal value. I have often compared my desire to study physics to my desire to understand how computers work. I was never satisfied by the "it's just ones and zeros" explanation, which is not incorrect, but also doesn't help me understand why this object is able to turn code into programs. I needed to have examples of how you can build logic gates into adders and so on and have the tiers of abstraction that go from adders, etc to CPU instructions to compilers to applications, and I had a nagging confusion about using computers for years until I understood that chain at least a little bit. There is a satisfaction which comes with the dissolution of that nagging confusion which I refer to as joy.
There's a lot to complain about when it comes to public education in the United States, but I at least felt like I got a good set of abstractions with which to explain my existence, which was a chain that went roughly Newtonian mechanics on top of organs on top of cells on top of proteins on top of DNA on top of chemistry on top of electromagnetism and quantum mechanics, the latter of which wasn't explained at all. I studied physics in college, and the only things I got out of it were a new toolset and an intuitive understanding for how magnets work. In graduate school, I actually completed the chain of atoms on top of standard model on top of field theory on top of quantum mechanics in a way that felt satisfying. Now I have a few hanging threads, which include that I understand how matter is built out of fields on top of spacetime, but I don't understand what spacetime actually is, and also the universe is full of dark matter which I don't have an explanation for.
I'm putting in my reaction to your original comment as I remember it in case it provides useful data for you. Please do not search for subtext or take this as a request for any sort of response; I'm just giving data at the risk of oversharing because I wonder if my reaction is at all indicative of the people downvoting.
I thought about downvoting because your comment seemed mean-spirited. I think the copypasta format and possibly the flippant use of an LLM made me defensive. I mostly decided I was mistaken about it being mean spirited because I don't think that you would post a mean comment on a post like this based on my limited-but-nonzero interaction with you. At that point, I either couldn't see what mixing epistemology in with the pale blue dot speech added to the discussion, or it didn't resonate with me, so I stopped thinking about it and left the comment alone.
I think I see what you're saying, let me try to restate it:
If the result you are predicting is course-grained enough, then there exist models which give a single prediction with probability so close to one that you might as well just take the model as truth.
I appreciate your link to your posts on Linear Diffusion of Sparse Lognormals. I'll take a look later. My responses to your other points are essentially reductionist arguments, so I suspect that's a crux.
That said, I'm using "quantum mechanics" to mean "some generalization of the standard model" in many places. In practice, the actual experimental predictions of the standard model are something like probability distributions over the starting and ending momentum states of particles before and after they interact at the same place at the same time, so I don't think you can actually run a raw standard model simulation of the solar system which makes sense at all. To make my argument more explicit, I think you could run a lattice simulation of the solar system far above the Planck scale and full of classical particles (with proper masses and proper charges under the standard model) which all interact via general relativity, so at each time slice you move each particle to a new lattice site based on its classical momentum and the gravitational field in the previous time slice. Then you run the standard model at each lattice site which has more than one particle on it to destroy all of the input particles and generate a new set of particles according to the probabilistic predictions of the standard model, and the identities and momenta of the output particles according to a sample of that probability distribution will be applied in the next time slice. I might be making an obvious particle physics mistake, but modulo my own carelessness, almost all lattice sites would have nothing on them, many would have photons, some would have three quarks, fewer would have an electron on them, and some tiny, tiny fraction would have anything else. If you interpreted sets of sites containing the right number of up and down quarks as nucleons, interpreted those nucleons as atoms, used nearby electrons to recognize molecules, interpreted those molecules as objects or substances doing whatever they do in higher levels of abstraction, and sort of ignored anything else until it reached a stable state, then I think you would get a familiar world out of it if you had the utterly unobtainable computing power to do so.
In what way? I find myself disagreeing vehemently, so I would appreciate an example.
Maps are territory in the sense that the territory is the substrate on which minds with maps run, but one of my main points here is that our experience is all map, and I don't think any human has ever had a map which remotely resembles the substrate on which we all run.
This is tangential to what I'm saying, but it points at something that inspired me to write this post. Eliezer Yudkowsky says things like the universe is just quarks, and people say "ah, but this one detail of the quark model is wrong/incomplete" as if it changes his argument when it doesn't. His point, so far as I understand it, is that the universe runs on a single layer somewhere, and higher-level abstractions are useful to the extent that they reflect reality. Maybe you change your theories later so that you need to replace all of his "quark" and "quantum mechanics" words with something else, but the point still stands about the relationship between higher-level abstractions and reality.
I'm not sure I understand your objection, but I will write a response that addresses it. I suspect we are in agreement about many things. The point of my quantum mechanics model is not to model the world, it is to model the rules of reality which the world runs on. Quantum mechanics isn't computationally intractable, but making quantum mechanical systems at large scales is. That is a statement about the amount of compute we have, not about quantum mechanics. We have every reason to believe that if we simulated a spacetime background which ran on general relativity and threw a bunch of quarks and electrons into it which run on the standard model and start in a (somehow) known state of the Earth, Moon, and Sun, then we would end up with a simulation which gives a plausible world-line for Earth. The history would diverge from reality due to things we left out (some things rely on navigation by starlight, cosmic rays from beyond the solar system cause bit flips which affect history, asteroid collisions have notable effects on Earth, gravitational effects from other planets probably have some effect on the ocean, etc.) and we would have to either run every Everett branch or constantly keep only one of them at random and accept slight divergences due to that. In spite of that, the simulation should produce a totally plausible Earth, although people would wonder where all the starts went. There do not exist enough atoms on Earth to build a computer which could actually simulate that, but that isn't a weakness in the ability of the model to explain the base-level of reality.
It may be that generating horrible counterfactual lines of thought for the purpose of rejecting them is necessary for getting better outcomes. To the extent that you have a real dichotomy here, I would say that the input/output mapping is the thing that matters. I want all humans to not end up worse off for inventing AI.
That said, humans may end up worse off by our own metrics if we make AI that is itself suffering terribly based off of its internal computation or it is generating ancestor torture simulations or something. Technically that is an alignment issue, although I worry that most humans won't care if the AI is suffering if they don't have to look at it suffer and it generates outputs that humans like aside from that hidden detail.
I'm doing a physics PhD, and you're making me feel better about my coding practices. I appreciate your explicit example as well, as I'm interested in trying my hand at ML research and curious about what it looks like in terms of toolsets and typical sort-of-thing-one-works-on. I want to chime in down here in the comments to assure people that at least one horrible coder in a field which has nothing to do with machine learning (most of the time) thinks that the sentiment of this post is true. I admit that I'm biased by having very little formal CS training, so proper functional programming is more difficult for me than writing whatever has worked for me in the past writing ad-hoc Bash scripts. My sister is a professional software developer, and she winces horribly at my code. However, you point out that it is often the case that any particular piece of research code you are running has a particular linear set of tasks to achieve, and so:
As an example of the Good/Good-enough divide, here's a project I'm working on. I'm doing something which requires speed, so I'm using c++ code built on top of old code someone else wrote. I'm extremely happy that the previous researcher did not follow your advice, at least when they cleaned up the code for publishing, because it makes life easier for me to have most of the mechanics of my code hidden away out of view. Their code defines a bunch of custom types which rather intuitively match certain physical objects. They wrote a function which parses arg files so that you don't need to recompile the code to rerun a calculation with different physical parameters. Then there's my code which uses all of that machinery: My main function that I have written is sort of obviously a nest of loops over discrete tasks which could easily be separate functions, but I just throw them all together into one file, and I rewrite the whole file for different research questions so I have a pile of "main" files which reuse a ton of structure. As an example of a really ugly thing I did, I hard-code indices corresponding to momenta I want to study into the front of my program instead of making a function which parses momenta and providing an argument file listing the sets I want. I might have done that for the sake of prettiness, but I needed to provide a structure which lets me easily find momenta of opposite parity. Hard-coding the momenta let me keep the structure I was using at front of mind when I created the four other subtasks in the code which exploited that structure to let me construct subtasks which needed to easily find objects of opposite parity.
I'd agree with you, because I'm a full-time student, but I'm doing research part-time in practice because I'm losing half my time to working as a TA to pay my rent. Part of me wonders if I could find a real job and slow-roll the PhD.
The simulation is not reality, so it can have hidden variables, it just can't simulate in-system observers knowing about the hidden variables. I think quantum mechanics experiments should still have the same observed results within the system as long as you use the right probability distributions over on-site interactions. You could track Everett branches if you want to have many possible worlds, but the idea is just to get one plausible world, so it's not relevant to the thought experiment.
The point is that I have every reason to believe that a single-level ruleset could produce a map which all of our other maps could align with to the same degree as the actual territory. I agree that my approach is reductionist. I'm not ready to comment on LDSL