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Every fundamental law of physics - Newton, Einstein, Quantum Field Theory - is derived from a single equation. Physicists don't propose new laws by writing forces anymore. They write a new Lagrangian and the math produces the physics automatically. The same principle that governs a swinging pendulum governs every...

22,626 görüntüleme • 5 gün önce •via X (Twitter)

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LinkTOAD goldnutter 𓆏⬡⬢🌊 𓀡 logic/acc truth/acc profil fotoğrafı
LinkTOAD goldnutter 𓆏⬡⬢🌊 𓀡 logic/acc truth/acc4 gün önce

I'm going to pretend I understand any of this and say bullish for @extropic

达拉蹦吧 profil fotoğrafı
达拉蹦吧4 gün önce

物理学的每一条基本定律——牛顿力学、爱因斯坦相对论、量子场论——都源自同一个方程。物理学家如今提出新定律,已不再靠写出力的表达式。他们写出一个新的拉格朗日量,数学便会自动推导出物理规律。支配单摆摆动的原理,同样支配着宇宙中的每一个量子场——而每一次大语言模型训练中的优化器,求解的也是同一个底层问题。 其机制就是稳定作用量原理(平稳作用量原理)。系统所走的路径,是总作用量在任何微小变分下都不再变化的那一条。这一条件导出了欧拉-拉格朗日方程。一个方程,适用于任意数量的变量、任意一种物理理论。在量子场论中,变量是遍布整个空间的场,梯度必须在每一点上同时为零——这正是梯度下降在反向传播过程中,于数十亿个大语言模型参数间所追寻的收敛条件。 20分钟。先收藏,今天就看。这一个方程,每一条物理定律其实都只是它的特例。

𝕹յ𝖌𝖍𝖙𝕱𝖆𝖇𝖑𝖊𝖃 profil fotoğrafı
𝕹յ𝖌𝖍𝖙𝕱𝖆𝖇𝖑𝖊𝖃5 gün önce

interesting

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A woman who spent nine years gluing paper models in a print shop just told a room of physicists their whole field stands on a mistake, one line, no hedging: "Every theory we have takes space and time for granted, like a bagel that forgot it was once a flat sheet of paper." That's Bianca Dittrich. She has a free lecture course that asks one question: what is left of geometry once you quantize it? The answer is: far less than you can picture. Quantum gravity looks like one more field theory. Buried inside is something stranger. Every quantum theory we have puts fields on a fixed stage, flat or curved, and does the physics on top of it. Here the stage itself is the thing being quantized. Your gut reads an atom of spacetime as a tiny grain sitting somewhere. Wrong. It cannot sit anywhere, because it is the somewhere. The move is invisible to human intuition, which is exactly why the people who get anywhere stop asking where the pieces are and start asking what a measurement even means. In 3D the whole thing collapses in a way that should scare you. No matter, no cosmological constant, and gravity is locally flat everywhere. Six degrees of freedom per point, all of them eaten by diffeomorphism symmetry. A field theory that ends up with finitely many real degrees of freedom, sometimes zero. None of it is hidden. Cut a parallelogram out of paper, glue the edges, and you have a torus. Flat everywhere, and yet two numbers survive that no local measurement can see. The lecture is free. Here is the trap: you feel every equation you can solve, every geometry you can draw, as progress. What you cannot feel is the gap you have to cross. Ten to the forty-one, from the Planck scale back up to the world you live in. And crossing it is the only thing that pays. Almost every approach still cannot show it recovers ordinary physics at the far end, and almost everyone quits long before then. The math is free to learn. The nerve to stop trusting your picture of space, that part you still have to bring yourself.

Zyron

110,850 görüntüleme • 3 ay önce

richard feynman once said "nature uses only the longest threads to weave her patterns, so each small piece of her fabric reveals the organization of the entire tapestry" - he died in 1988 not knowing he had already written the mathematical foundation of GPT-4 the researchers who understood that LLMs and physics share the same equation built the $4.6T industry everyone else is now trying to enter. this technical note explains the connection - and most people will scroll past it. a new independent paper dropped this month with one claim: AI and physics are literally the same equation. not metaphorically. the same math, the same dynamics, the same convergence behavior - just different variables. physics: dx/dt = -∇E(x). machine learning: θ(t+1) = θt - η∇L(θ). different symbols. identical structure. both systems evolve toward minimum states following gradient-based dynamics in high-dimensional spaces. the convergence curves match a power-law decay t^(-α) across 10,000+ training steps. physical simulation and LLM loss follow the same line. the document proves this empirically, not theoretically. the empirical parallels table is what makes this real - phase transition in physics maps to emergent reasoning in LLMs, critical scaling maps to capability scaling, symmetry breaking maps to specialized behaviors, energy barriers map to local minima. the Lagrangian S = ∫L dt. the Schrodinger equation iħ(∂ψ/∂t) = -(ħ²/2m)∇²ψ. the ML optimization θ* = argmin L(θ). three equations from three different centuries. one underlying process. what this means practically: tools from statistical mechanics and dynamical systems can be applied directly to improve LLM training efficiency. the people building next-gen architectures are not reading AI papers. they're reading physics journals from the 1970s. DeepMind already has 40+ physicists on staff, not as consultants, as core researchers. the companies that understand this first will build models fundamentally cheaper and more capable than anything trained on ML intuition alone. feynman mapped the landscape. we're just now learning to walk it. Bookmark & watch today. Nature and intelligence have the same loss function.

Deliriumm

18,681 görüntüleme • 5 gün önce