Ghideon

Your conclusion seems reasonable.

That may not work so well if it contains text; searching, quoting or highlighting does not work well in the forum.

As I said earlier: look up ontological bootstrapping

You have not provided any coherent model for the emergence of spacetime. Building new speculations (for instance about expansion) on top of that does not work.

Today I learned about the concept ”non-algorithmic” in relation to computability, Turing machines and Gödel. I don’t remember seeing that concept when studying the ”opposite” (algorithms as part of computer science); my focus was more on software engineering. I think this new knowledge will help me navigate when reading AI related material. Especially philosophical and more speculative stuff. (As always thanks @joigus for using words, concepts and examples that trigger searching, reading and learning)

Quick example: the post where the quote above is taken from. (If you understood “ontological bootstrapping problem” you would have spotted it before posting?)

To better understand the clear critiques other members have posted you may want to look up and read about “ontological bootstrapping problem” (using tools that can’t logically exist at the model’s starting point). That may give you valuable insight about the overall problems with the explanations you post. Also, there is a risk that AI usage reinforces this. The LLM does not contain any non-spacetime model about ”chortons” and will likely fall back on spacetime terms, even if they logically does not apply.

Layman question: Without units in mathematics, how do you distinguish between radians and degrees in geometry?

Testing an editor \[ \frac{\pi}{4} = \sum_{k=0}^{\infty} \frac{(-1)^k}{2k+1} = 1 - \frac{1}{3} + \frac{1}{5} - \frac{1}{7} + \cdots \] \[ 4* \sum_{k=0}^{\infty} \frac{(-1)^k}{2k+1} \]

I use the sandbox section in this forum to test mathematical formulas and latex.

That is absolutely a possibility! @swansont commented on that and a similar scenario in a recent thread https://scienceforums.net/topic/136269-flood-of-spam-12th-july-2025-why-would-someone-do-that/#findComment-1293102

I do not think that, sorry if you got that impression from my post. If you share what you have got so far I may comment on AI aspects of the progress.

I agree! The comment triggered my curiosity (even if not directly related): Can moderators see if forum software blocked any spam associated with the same burst of activity? I'm wondering if the reason for the spam is to stress the forum software, for instance Reverse engineer thresholds: tweak link count, pictures vs text to see what just slips through. Leverage shared platforms: a bypass on one forum could works unchanged on others. ML probing: make tiny edits to find variants that evade the same spam filter model.

It seems there may be a misunderstanding about how generative AI works? It does not infer; it predicts tokens based on statistical correlation, not on causal understanding. Adding a phrase like “be brutally honest” may change the tone of the response but it doesn’t make the output more scientifically valid or rigorous. An analogy may help to illustrate: Imagine someone is unaware of the laws of thermodynamics and they interact with ChatGPT to develop equations for usage in physics. The resulting equations may be mathematically correct but when applied to physics the equations could describe perpetual-motion machine ("over-unity" device). Since the user has not studied thermodynamics they can neither include relevant constraints in their prompts nor identify issues in the output. ChatGPT, in turn, is not guaranteed to flag such issues even if it has seen training material stating that perpetual motion is impossible. Prompting it to be “brutally honest” won’t fix this. Now, scale up: "quantum gravity" or "Photon Collapse as the Origin of Gravitons" is orders of magnitude harder. Neither you nor ChatGPT have any real understanding of what a valid graviton theory would look like. ChatGPT may produce superficially plausible equations but you can't tell if they're meaningful or nonsense. (That said, using generative AI in combination with other metods and sources can probably be helpful when studying, but locating scientific papers and discussing such aspects of AI usage is probably better in a separate thread)

Regarding AI* in context of your idea What "AI" did you use? What method do you apply to distinguish nonsense from useful scientific progress? If you use generative AI maybe you could share your prompting strategies for feedback. Since you seem to develop a novel idea, how does the model get trained on these new, and to the model, unknown concepts? Note that a "correct" output from an established language model, if it was trained on established physics, would be scepticism. Depending on the prompts the model could, similar to the experts here, question the basis of the idea. But it could also generate nonsense physics that statistically matches your input and possibly guide you in the wrong direction. If the model is a state of the art mathematics capable model it is quite possible to get mathematically sensible output that makes no sense in physics. My point: Using "AI" nowadays is easy. Correctly applying AI in the context you have described is hard; you would need to study both physics and AI. *) which is closer to my area of knowledge than particle physics

Just a quick note from someone who's been around a while: I tend to approach comments like that a bit differently: with curiosity. When someone knowledgeable, like @joigus, casually sets something aside within their area of expertise, and I can’t immediately follow why, I take it as a sign that they’re drawing on insights or theories I might not yet be familiar with. That usually prompts me to ask: What am I missing since this seems straightforward to them? How could I go about understanding that better given that I already have a basic grasp of the topic? And from there: Am I curious enough about this particular subject to explore it further?

Hello! I am unable to follow your logic. Please explain what happens if someone creates a paradox by time travel from some other identical universe and arrives in this universe where we have this conversation. They prevent you from posting the opening post above*. What will happen in this universe according to your idea? Your idea seems to say the future is “predetermined” but also says your arrival instantly changes that future. Those two statements cannot both be true as far as I can tell. *) for instance by creating the grand father paradox in this universe

? The isotope deuterium has one proton, one neutron and one electron as far as I know.

Yes, many times! I'll see if I find something interesting to add. But first I got curious @joigus: I did a quick test* on a few models and got different answers: ChatGPT, 4.1 mini - No, ChatGPT 4.o - Yes, DeepSeek-R1-Distill-Qwen-14B - Yes Is there is any reason for the different answers?. Is the "No" answer always wrong or is there a context where "No" is correct? To check if the answer, according to the model, depends on context I used the following prompt (ChatGPT 4.o): The output provides some insight into how a model may produce "yes" or "no" to the original question depending on the context: Final test for today: since the model ChatGPT 4.1 mini generated the answer "no", what happens with a slightly modified prompt? Answer: Further tests could use for instance : Note: this is not an attempt to defend generative AI models or criticism against the question @joigus posted. I just got curious since this specific topic is closely related to a project I'm involved in. *) If interesting I can upload the full conversations; it gets quite long after a few interactions.

@iNow on my computer and phone browser (both Safari) I click my avatar picture close to the top right corner of the forum, and the Mark all read is in the menu that opens:

I asked, a Japanese car, A Toyota’s a Toyota, right? Response: No! Fiat; if on race track. Go hang a salami, I'm a lasagna hog!

In case it helps, just an idea; While the product [math] f = d* m * t [/math] is dimensionally wrong, you can still say that force depends on distance, mass, and time by introducing an unknown function [math]f  =  F(d,m,t), F:D \subset\Re^{3}   \rightarrow \Re[/math] where D is the set of allowed triples (d,m,t). The explicit form of F is not yet known, but whatever it is, F(d,m,t) must have the units of newtons.

Testing after forum update Sentence with three definitions [math]f(x^{2})=\frac{y}{2} [/math] \(f(x^{2})=\frac{y}{2}\) [latex]f(x^{2})=\frac{y}{2}[/latex] end of sentence. Sentence with no math. New sentence [math]f(x^{2})=\frac{y}{2}[/math] \[f(x^{2})=\frac{y}{2}\] [latex]f(x^{2})=\frac{y}{2}[/latex] end of sentence.

Note: In my opinion the title of the tread "Simulating Physics with AI" does not relay match the AI related questions in the opening post and this possibly causes some confusion. I try to address the content of the post rather than what the title could imply. I found a video closely related to the question in the opening post where the presenter is using ideas from computer vision problems (convolutional neural network) to work on temperature fields; closely related to the specific section: The video, titled "Can AI Uncover the Laws of Physics by Observing Apples Falling from Trees?" also briefly discusses the general question about AI capabilities to (re)-discover Lawes of physics (stated in the opening post). Here is the summary of the video, from YouTube (emphasis mine): Moving beyond mere pattern recognition, machines are now capable of extracting new insights from hidden trends and patterns, generating lifelike images and coherent text, and making complex decisions in intricate environments. As these advancements progress at an astonishing pace, it begs the question of whether artificial intelligence (AI) will eventually attain the level of intelligence required to delve into highly intellectual pursuits, such as comprehending the fundamental laws of physics in nature. In this presentation, Dr. Baek will highlight some of the recent frontiers in physics-aware deep learning and demonstrate their application in solving complex mechanical engineering problems, ranging from designing composite materials to predicting quantum spin dynamics. The video is a recorded presentation from University of Virginia, link https://www.youtube.com/watch?v=qhSkX7DjvSM

As far as I know the answer is yes. But the output from the AI* will not be a neat equation. Some short, more technical, notes: Neural networks are powerful function approximators, yet their "black-box" nature often renders them opaque and difficult to interpret**. The model will be able to predict how a physical system evolves. The result of the machine learning is weights in a neural network, not compact equations. some models struggle with discovering conservation laws. The following paper gives an overview of some challenges, including "black box" Lagrangians: Source: https://arxiv.org/abs/2408.14780 AI used in those softwares is for other purposes, not related to what OP is asking about. *) "AI" in this case are machine learning and various neural network architectures such as Physics-Informed Neural Networks (PINNs). **) paper that may be of interest.https://ar5iv.labs.arxiv.org/html/2003.04630