That would be really sad..
I think you're going too far with this. Most people understand scientific theories to be an approximation. F=ma is approximately true, in the sense that it's only accurate within the newtonian regime and each of those terms includes so many asterisks that you will only ever measure it approximately.
The latter is the jokes about the physicists "assuming a perfectly spherical cow."
In fact that's kinda the whole point of the "unreasonable effectiveness of mathematics" essay. It is unreasonable that mathematical approximations are so good at describing our world.
Not to detract from your point at all, but I only ever heard this joke about mathematicians!
Biology is incredibly well oiled!
The parent is talking more about elegant simplicity vs. sprawling, seemingly haphazard complexity, and you're talking more about durability to failure points and 'completeness'.
Likewise, in code, a lot of the most durable, battle tested software looks extremely inelegant and duct taped, as 90% of the code is dedicated to handling one-off patches and weird edge cases.
That's the layman's idea of physics theories. They are beautiful and elegant only on the surface, that's why they're technically models and approximations of the real world. The standard model renormalization techniques are a mess of patches and ad-hoc heuristics, pretty far from the "this lagrangian literally contains all physics". Generally you just _ignore_ higher order terms and just call it a day. The famous E=mc^2 it's just the first term of a Taylor expansion. The beautiful form of physics it's what you would call "good enough" and often just a pedagogical tool.
Is this actually true? My understanding was that E=mc^2 is exact for a particle at rest.
Up until the present it has been a nearly uniform march of revealed symmetries, collapsed privileged frames of reference, and other such (in the deepest sense) simplifications in our model of reality that has improved its fidelity to the measurable.
I hang qualifier about these developments being simplifying because the result isn't simple in the details: quantum chromodynamics is a daunting subject! But it's not just an enumeration of details and contradictions, the particle zoo that preceded the Eightfold Way looked like line noise, now in indexed notation the Lagrangian of the entire Standard Model fits on a page (or so I've been told I've never actually seen the page).
It's almost tautological that the frontier where it's still messy involves an unrevealed symmetry or a persistent privileged frame of reference, that's what frontier means, we don't see past it to the seam where it folds up.
Personally I suspect AI systems will be a great deal more inclined to discard the parochial axioms that have every point placed human ego above simplicity.
It doesn't resolve all of the open problems in physics if you amputate consciousness, free will, agency persistent identity, and an unambiguous arrow of time.
But it starts looking possible to make progress.
Occam's Razor is a useful heuristic, but it biases us towards simpler explanations.
I think of elegance as not having to add epicycles, not that everything in the system has to be simple.
Also, without a working theory the, the space of possible solutions is near infinite. LLMs manage to pluck out the space of comprehensible English strings from n-dimensional hell. Even if this is done with a black box of billions of parameters, it’s still elegance in the sense that such a space even exists and was found
Gaining expertise is always the hard part and our new LLM overlords are making that much harder. So the simple “pure” functions as a teaching aid have never been more important.
End users have never cared about how the sausage is made though.
LLMs can explain complex things to humans with tons of specific context that you don’t find in textbooks or even a google search.
It’s probably never been easier to grasp a large codebase than it is today for example. You can probe and ask specific questions without going through a maze of imports and relationships and config files yourself.
Learning things will always be up to the person, it’s still a choice and dedication to a craft can still be taught.
I keep meeting people who think this and have enormous understanding gaps in the topics they've had an LLM teach them.
The absolute worst judge of how well someone understands a complex topic is the novice themselves.
When gaining mastery is not a requirement to doing novice-level work, many fewer people will get there. It takes more dedication than it did before.
https://en.wikipedia.org/wiki/Solomonoff%27s_theory_of_induc...
Imagine that it's maybe the 1800's and you're asking why somebody who has already survived smallpox is not susceptible to becoming infected again. If you offered an explanation involving tiny detectives wandering around and collecting evidence which they present to each other and decide whether to multiply... one in which the tolerance comes from the detectives from the previous fight still hanging around in your lymph nodes ready to spring into action if they run across the right kind of evidence. Well that would probably be a more complicated explanation that anybody at the time would offer, and it would also be correct.
When you attempt to hyper-optimize, even with humans in the loop, you end up a mess. You're lucky if you can find clean guiding principles anywhere. If you can hyper-optimize hyper quickly, you end up with an extra layer of mess.
I think your point is more that we might be able to initially describe complex phenomena as messy, horrible complex equations, that doesn’t mean we shouldn’t work to simplify them and make them more understandable to us.
Some are useful.
Having theories that only give answers, but you can't reason about is not as useful. Having a theory where you don't know the limits of it's applicability, can be very dangerous.
At least in the physical realm there is not yet anything that combines relativity with QM so they can only be approximations. Even in math so far there seem to be similar challenges using programatic and "AI" driven solutions and proofs.
Still, I know that LLMs will be useful for Verilog/VHDL and particularly with verification, where they are already heavily used. Defined outputs and complete test coverage is already such a big part digital/asic design, I'd be surprised if it isn't used a lot more. Many software people would say that hardware is badly written copy-pasta, as it is. That said, higher velocity slop and hardware "technical debt" isn't something you can fix with an update. And no matter how fast you "ship", you won't get parts back in less than a few months. Poorly used, it will lead to expensive failures.
OPs argument is that reality is expressed by very complex equations and interactions; by definition this is outside of Solomonoff induction because it’s easy to imagine this accurate model by definition is the shortest algorithmic explanation, it’s just orders of magnitude more complex than our current approximations.
I guess the argument from OP would look like: "Yes, now imagine we poke and extend our universe as far as we can. How much bigger do you think our final 'shortest description' would be? I imagine it may be orders of magnitude more complex."
Well, I can imagine a squared circle... doesn't mean the math checks out. I would reply that you do not have to imagine, you can go about looking at different mathematically possible universes in Tegmark IV and find the expected number of bits for the one you actually exist in. Which is ~0 bits more complex than the shortest description based on the data you currently have.
Also, note that Newtonian mechanics is not actually a very short theory for building a universe, because you have to instantiate every object in the universe. You actually get a lot more of the structure for free with general relativity (re: Wigner's classification of the particles). An observer in a presumed-Newtonian universe calling it a simple theory would be like saying, "I compressed Wikipedia to one byte, just by putting it all in the decompiler!"