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TuringsSolutions 
posted an update Nov 14
Post
1351
My Hypothesis:

Concepts like entropy, energy, and the second law of thermodynamics are not intrinsic to physical matter but are emergent properties of any sufficiently complex system where probabilistic decision-making, optimization, and information flow occur. These principles arise naturally in artificial environments that are structured with rules governing uncertainty, even without explicit definitions of physical thermodynamic laws.

Proven Via:

The Second Law of Thermodynamics
Geometric Langlands Program
Lagrangean Mechanics

TL;DR: When I create a simulated environment, I do not need to code entropy and energy into the simulated environment. I can utilize Entropy and the Second Law of Thermodynamics and I can use Conservation of Energy, but I do not need to explicitly code these into the environment. That is peculiar.

I made a video with a clickbait title but a bunch of code that breaks this observation down further. Would love for someone to prove my simple observation false: https://youtu.be/8n7SXLj7P1o
TuringsSolutions

I created a discrete probability space using Monte Carlo and Gaussian Probability. Then, I created a bunch of dots as agents. 3 big dots and thousands of small dots. Why does extreme clustering occur in this environment? Every single time. This behavior is not programmed in anywhere, I can show the full code to anyone. That graph on the right don't lie, I can reproduce it again and again. Why?

clustering.png

Chicorita976
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p3nGu1nZz

here is my proof that i did some months ago along the same principals you bring up.

https://gist.github.com/p3nGu1nZz/3acdf783cf14ec21d678db6fc04cc8ee

left and right click adds and removes bubs and tensors to the network

without programming nuclear forces directly, only by setting the attraction weights of the bub (dots), the system will exhibit and learn core physics and thermal dynamics properties, as emergent properties.

these are similar to "free lunch" in mathematics.

however, correlation does not equal causation, so it's important to develop a falsifiable experiment testing and measuring these phenomena.

thanx for sharing the brain candy and nice video.

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TuringsSolutions

"however, correlation does not equal causation, so it's important to develop a falsifiable experiment testing and measuring these phenomena." My goal is to prove it is true by training a model purely using geometry utilizing the Geometric Langlands Program. My first goal is to create a Q learning style algorithm that can be utilized within any sufficiently complex discrete environment to train a model on any sufficiently complex task. If it is all true, then the experiment will work, then I will go from there. If I end up with a model that actually works, then physics is not bound to the physical.

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