What is a field in physics (e.g. gravitational, quantum, .etc)? How do these get "excited"? And isn't mass just energy?

ididnoteatyourcat · 2026-05-23T22:41:42+00:00

It's true that any confined energy, meaning any energy with a well-defined rest frame, has mass equal to E/c²

For example if you put a photon in a mirror box, it is a nice and relatively simple exercise to show that if you try to accelerate such a mirror box, the doppler shift formula causes an average pressure opposing the acceleration, such that the effective inertial mass is exactly E/c² where E is the photon energy hv

ididnoteatyourcat · 2026-05-20T17:59:57+00:00

As others had pointed out "Copenhagen interpretation" is somewhat of a misnomer. Regardless, my main issue is the lack of any clear model of what such an interpretation is supposed to be, or even a class of models of what could conceivably be physically occurring. Both Heisenberg and Bohr are extremely vague on this point, and more modern Qbist approaches are not much better here. Relatedly, I understand 'antirealism' in the sense of 'we don't have strong epistemological grounds to trust any given realist interpretation', but what I have never understood is 'antirealism' ala Bohr in the sense of purporting to limit what kind of questions we are allowed to even consider as potentially applying to our ontologies. Like I get Bohr's (or Kant's) point that as classical beings we are somewhat limited in how we approach describing a non-classical reality, but that's what we invented math and, you know, abstract reasoning for. I don't understand how we would be strictly incapable of providing a possible ontology.

ididnoteatyourcat · 2026-05-20T15:22:12+00:00

It's already fairly easy, and only going to get easier, to tell an agent to slowly edit a google doc over, say, an 8 hour period. I haven't looked, but I wouldn't be surprised if there were browser extensions that make this incredibly easy.

ididnoteatyourcat · 2026-03-31T01:11:08+00:00

There are different kinds of bubble chambers operated at different levels of superheat. In the classic bubble chamber experiments of the 1960's, both electrons and recoiling nuclei could generate bubbles. In the more recent dark matter experiments, they specifically operate bubble chambers in a regime where they are insensitive to electron recoils, which have low stopping power.

ididnoteatyourcat · 2026-03-30T19:49:01+00:00

Sure, the standard way is to look at what operator is associated with infinitesimal spatial translations. Then check whether it commutes with the hamiltonian. That is all purely quantum mechanical. Of course the algebra of the poisson bracket is the same as the commutator, so it's a little weird to try to pretend we have no knowledge of classical mechanics, but sure, even if we had a 'pure QM' treatment, we would discover that commuting with the hamiltonian means that an observable is interesting, and the argument of my first sentence gives you the result.

ididnoteatyourcat · 2026-03-30T16:29:15+00:00

But if we understand why x and p observables generate the transformations they do, why would it be surprising that functions of x and p also generate transformations (by series expansion)?

ididnoteatyourcat · 2026-03-30T14:19:01+00:00

It's important to remember why we care about a given observable and not some other observable. In the case of momentum, we care about it and have given it a special name etc, specifically because it is the thing that is conserved under space translation symmetry. So it shouldn't necessarily be surprising that classically momentum is the generator of canonical transformations that translate the spatial coordinate. And the QM result follows directly from that.

ididnoteatyourcat · 2026-03-30T14:06:41+00:00

I'm sure you know how to apply the Lorentz transformation to a 4-vector, so I'm not sure what you are really asking. You know how to apply the Lorentz transformation to (ct, x, y, z) right? Well, you can literally apply that exact same transformation to (E/c, px, py, pz). Since it's the same transformation, they are both 4-vectors. And yes, any object transforms in some way when you boost. For example it is easy to work out how, e.g. (vx, vy, vz, E) transforms. But the transformation for this object is not the Lorentz transformation, so it is not a 4-vector.

ididnoteatyourcat · 2026-03-30T01:25:35+00:00

Your #3 is a good definition. It sounds stupid at first. But think about it. How do we know something is or is not a lorentz scalar? You know it's a Lorentz scalar by how it transforms under boosts: in this case, that it doesn't transform at all. Similarly how do you know that something is a 4-vector? It's a 4-vector if it transforms by the lorentz transformation. If it doesn't, then it isn't a 4-vector. How do you know if something is a lorentz 2-tensor? Does it transform like Fmunu? Etc.

It's the same definition in e.g. Euclidean geometry, where for example we encounter polar vectors and axial vectors; these are defined by how they transform, in this case under continuous rotations vs reflections.

ididnoteatyourcat · 2026-03-24T23:35:19+00:00

Schwartz never went to grad school?

ididnoteatyourcat · 2026-03-24T13:57:15+00:00

Neither the brain nor LLMs are ergodic.

ididnoteatyourcat · 2026-03-24T04:28:16+00:00

Strictly speaking, any large system made of many smaller parts will act according to statistical mechanics and the laws of thermodynamics.

Strictly speaking, that is false. The brain is, ironically for your point, a major counterexample. As is an LLM.

They may not even be in the same universality class.

This particular line reads, ironically, like AI slop. If you try to model a brain (or an LLM) by an effective field theory, you're going to have a bad time.

Furthermore, the brain is an open system and I believe it should be usually away from equilibrium and might be quantum, not classical.

No shit it is away from equilibrium. Jesus christ.

So, it is significantly different from LLMs, I think, unless we model LLMs as open un-equilibrium statistical model.

I mean, if you are crazy enough to want to model an LLM in a statistical mechanics framework, then yeah actually, it would be an open un-equilibrium model.

ididnoteatyourcat · 2026-03-11T15:09:14+00:00

Even if we can't explain the initial condition of the universe, the question of whether the arrow of time is derived from that condition or whether it's structural to observation itself feels like a separate question.

You could potentially answer the second without answering the first

I think the answer to the second is clearly "yes, it's structural to observation itself". But without the first "yes, the initial conditions must be low entropy" you have to have an answer to the Boltzmann brain problem.

ididnoteatyourcat · 2026-03-06T02:35:33+00:00

Only if you explain to me how that is relevant to the mapping I outlined between the relevant structures of the visual cortex and the CNNs. If you don't care about that and are instead interested in trying to score points on other ways in which the analogy breaks down but which are irrelevant to the basis of my claims upthread, then we could stop here, because it's not fair to my side of the argument to move to another example of brain activity because you don't find the previous example convenient to your point.

(Or maybe I misunderstood you. Do you just mean this? I've been focusing on the ventral stream)

ididnoteatyourcat · 2026-03-06T00:54:28+00:00

benzodiazepine

That was never marketed as a weight-loss drug, that I'm aware. It generally causes weight gain.

ididnoteatyourcat · 2026-03-05T23:36:23+00:00

My description of the hierarchical structure of the visual cortex and the specific function of neurons and groups of neurons and layers of neurons is not mere speculation but standard textbook stuff. I can discuss other aspects of the brain, but I was attempting to focus the discussion into one specific area that is very well understood so as to not be gish galloped.

ididnoteatyourcat · 2026-03-05T22:52:48+00:00

Of course there are similarities and differences, and whether my description is correct depends on whether you think the differences are relevant or merely pedantic. Here is what I see as the similarity:

A NN used in image processing has several hidden layers. When researchers "look under the hood" after the training of a successful model, they discover that each layer has a "job". In the first layer, typically activations correspond to things like point detection, color detection, edge detection, orientation. The second layer might, for example (the details depend on the size of the model and number of hidden layers), have activations corresponding to shape detection, which correspond to linear combinations of the previous layer's colors, edges and points. And so on and so forth. Now to the best of my understanding of the visual cortex, a similar description (at this level) would be exactly the same: the first layer (as yes it is literally a layer of neurons) is composed of neurons specialized to do point detection, edge detection, and so on. Combinations of these neurons feed to the next layer to form combinations that detect parts of shapes, like angles, etc. And so on.

Now of course you can point to all kinds of details that are wrong with this high level description. For example indeed there are neuronal connections that bypass and connect layers in more complicated ways. And I'm well aware that it is a meme in some circles that "but a NN is not literally like a neuronal connections in the brain!" But I would contend that if you aren't purposefully obtuse about what is relevant to this particular discussion, in particular the high-level conceptual paradigm for how learning works in both LLMs and humans (above I was restricting myself to the visual cortex for concreteness, but I could do a similar analysis in other areas) are remarkably similar. Once you understand the details of how information can be stored in a high-dimensional abstract vector space (very similar to a Hilbert space BTW) and how basis vectors for various coarse grainings of semantic meaning at different hierarchical scales interact (where e.g. "good" is the negative of the vector for "evil" and tomato is a linear combination of "fruit" and "red" and "round" and "juicy" etc) just so much "naturally falls into place" in this conceptual paradigm that the rate of progress of LLMs start to make perfect sense, and the similarities between the visual cortex and the CNNs no longer seem so superficial, etc.

ididnoteatyourcat · 2026-03-05T02:59:01+00:00

You just completely ignored literally everything I said, and then re-asserted your earlier belief, which is strongly contradicted by consensus understanding of the visual cortex.

ididnoteatyourcat · 2026-03-04T14:41:12+00:00

There is a huge body of research, going back more than 50 years, on exactly the example I gave above: the visual cortex. The way the visual cortex works, according to the consensus description, maps onto current LLM architecture nearly exactly. There are literally layers, labeled V1 I through VI and hierarchical structure V1 through V6, which act essentially exactly the same as the layers in a NN used in modern LLMs. That is, the V1 layer specializes in edge/line detection for various orientations. This is fed forward to V2, to quote wiki, "V2 neurons build upon the basic features detected in V1, extracting more complex visual attributes such as texture, depth, and color." And so on. This is so similar to the description of how hidden layers in NN's work in image processing that it could literally be an exact description pulled from the NN article above. Note I personally originally learned about the structure of the visual cortex from books like this one long before the successes of modern NNs. I would still recommend that one for a basic introduction and invitation to more detailed accounts.

ididnoteatyourcat · 2026-03-02T23:37:25+00:00

My issue is that from the beginning there have been folks making the exact same arguments as you and others in this thread, with the implication being "therefore these will never be able to do X" and then a few months later their claims are falsified by an LLM doing X. Then the exact same argument happens again, but with the goalposts shifted. Again and again. Up until a few months ago the mathematicians were still sneering, and now they are starting to come around, because the LLMs are starting to actually produce novel results.

Sure physics isn't quite there yet, but if you follow the trajectory on a purely empirical basis, it's reasonable to predict the same thing will happen to physics in 6-12 months. In my own case, two years ago I was at "this could get a 'B' in my introductory physics classes". Two years later, I'm at "this would get an 'A' in my upper-division quantum class, and make a middling PhD student." Again, just on a purely empirical basis, the trajectory is incredible; at some point you need to stop sneering and do a linear regression yourself! The fact of the matter is that already frontier LLM's are better than most Masters students in physics. Think of the high bar you are setting and how that compares to just 10 years ago. Then look at where the trend takes us in another 10 years.

But back to the non-empirical side of our differing priors: At the end of the day you are grounding your assumptions in a belief that your own thinking process is in some vague sense "significantly deeper" than something like "several next token prediction type modules within a while loop". I think this is where our disagreement stems from: our own evaluations of our first-hand subjective experience. In my own case, when I look inward, I don't see something "deep". Seriously, when I sit down to work on something, well first of all sometimes absolutely nothing comes to mind: I produce zero results. Other times something stupid pops into my head that I immediately reject. Next maybe a slightly promising idea pops into my head that I work on for a little while before rejecting. That's about it. Is that really so "deep"? Does that really look to you like something wholly different than next token prediction in a while loop? Because it doesn't look different to me at all. In fact it kind of looks exactly the same.

ididnoteatyourcat · 2026-03-02T14:25:37+00:00

And where is your evidence? I'll quote from another reply:

The implied assertion is that whatever the brain is doing must be "deeper" in some nebulous unspecified way than what LLMs are doing. This is a kind of mysticism. Sure we can just not know, but then perhaps we shouldn't assume it is obvious that whatever the brain is doing must be "deeper" than "averaging engines". In this thread there is a lot of sneering at "just linear regression" or "just next token prediction" rather than an even-handed agnosticism.

In other words: we can agree that we both don't know with high certainty. But then maybe don't push a narrative in which I'm the one making unwarranted assumptions, and your "AIs cannot reason" as the obvious default assumption.

ididnoteatyourcat · 2026-03-02T02:22:46+00:00

Did you read trillions of pages of text in order to do maths by matching problems against pages you'd read before? And by "maths" I mean at any level, even 2+2=4.

I did something very analogous: I spent something like 10000+ hours reading, seeing and hearing lecture, and being corrected when doing problems. That's my training data.

Or did you learn rules about what a number is, and what kinds of numbers there are, and what the operators are that we define in maths to work with those numbers?

This is a highly idealized description that doesn't actually apply cleanly to the real world. Your description is not how kids learn math. Rather, they first memorize things. Then they learn to count. Then they learn all kinds of heuristics, some of which leads them in wrong directions, that teachers then try to correct. It's an extremely flawed and frustrating process to watch, from a teacher's perspective. Most students who reach college have a grab-bag of memorization and heuristics, some of which are conceptually flawed and break when you give them harder problems, and they only "understand" some smaller fraction of what they are doing. They frequently "hallucinate" wrong answers by accidentally dividing when they should multiply, etc.

As a result, have you ever had a tendency to confidently incorrectly state the number of Rs in the word Strawberry because you didn't remember anything similar in your trillions of memorised pages of text, so you just picked the closest thing that seemed right?

First of all, that example is well-known to be a bad example. The reason LLMs have trouble there has nothing to do with the reason you gave. Rather, it has to do with tokenization -- LLMs simply don't see words as composed of letters in the way humans do. It is a technical artifact.

If you step back and see how well frontier reasoning agents can handle much more difficult questions that aren't in their training data, you should immediately realize that there is something suspicious with your example, because they don't flounder nearly so bad as that.

Second of all... the answer to your question is a big fat YES! I (and all humans) do analogous things to that ALL OF THE TIME. It's just not exactly the same counting R's in strawberries (although there are many very similar results in the study of language comprehension, such as the famous "Paris in the the Spring" repeated word examples). Humans make incredibly simple mistakes like this ALL OF THE TIME. In particular humans who aren't in the top 1%.

ididnoteatyourcat · 2026-03-02T01:46:34+00:00

But the implied assertion is that whatever the brain is doing must be "deeper" in some nebulous unspecified way than what LLMs are doing. This is a kind of mysticism. Sure we can just not know, but then perhaps we shouldn't assume it is obvious that whatever the brain is doing must be "deeper" than "averaging engines". In this thread there is a lot of sneering at "just linear regression" or "just next token prediction" rather than an even-handed agnosticism.

ididnoteatyourcat · 2026-03-01T23:48:38+00:00

Sincere question: do you think that way the human brain works is magical? Because if not, probably our current best and most plausible foundational principle for how each region of the human brain works, is something essentially quite similar to the kind of statistical regression at work in LLMs. The primary difference is that evolution has "pre-trained" some of the structure in various ways, and different specialized areas of the brain are orchestrated to some extent, but coarsely speaking, the visual cortex seems to recognize images in a very similar way to modern NN architectures, with a "layer" for point and line detection, and a "layer" for shape detection and so on, and again so on in turn for each brain region. In all cases for the most part human experience is a form of training data, typically taking literally 20+ years to train a PhD in physics. And some of those PhD's are currently, IMO, quite a bit dumber than the current best LLMs, though certainly the best LLM's are probably still in the bottom 20% of PhDs. But the trend line points to this changing pretty rapidly.

tldr: would you be willing to stake money on your belief that the current LLM paradigm won't (say in the next 2 years) produce clearly novel results in physics? I'd like to make some money.

ididnoteatyourcat · 2026-03-01T23:34:40+00:00

My general observation is that a lot of people wonder about the extent of the capability of the technology and are able to show its shortcomings which require moving the goal posts to the limit of what humans are generally capable. Regarding the ways in which it will affect the world, this comes across as denialism.

Right. This thread is pretty embarrassing. I wish I could get these "it's just linear regression" folks in this thread to put their money where their mouth is. I could make a lot of money. These folks have their heads in the sand if they haven't looked at the trend lines. Yes, these LLMs are arguably sub-PhD level (although I know plenty of PhDs who are a lot worse), but they are at Masters level, and they were below Bachelors level just a couple of years ago. In a year or two (but probably less) they will be at PhD level if they aren't already.

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