A string fixed at both ends vibrates in standing waves sin(nπx/L), with frequencies fₙ = n·f₁ = (n/2L)√(T/μ).
On the A string, f₁ = 110 Hz, the first eight are A2, A3, E4, A4, C#5, E5, G5, A5. Six sit within 2 cents of a piano key; the 5th is 14 cents flat and the 7th, 770 Hz, is 31 cents flat of G5.
A pluck at p = 1/5 is the sum of all modes with bₙ = 2h sin(nπp)/(n²π²p(1−p)). Since sin(π) = 0, it has no 5th harmonic.
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.
Meet Morpho, a unique inspection drone developed by Elythor, an EPFL spinoff company.
It uses wind and adaptive wings to extend flight time and enable vertical and horizontal flight.
It can inspect power plants, power lines, turbines, pipelines, and offshore platforms.
New paper! Designing quantum circuits using Lie group diffusion models: https://t.co/dLztSi2lGE
Instead of doing just boring normal diffusion, I encode the physical structure of quantum gates, which live in the Lie group SU(2), by doing diffusion natively on SU(2)!
I also show how this produces diverse circuits for interesting Hamiltonians like the Transverse-Field Ising Model (TFIM) and the Heisenberg-XXZ Model.
Crucially, the generated circuits are subject to realistic hardware constraints from actual quantum processors so that they're not just AI slop. 🧵
The Helmholtz decomposition theorem states that any sufficiently smooth rapidly decaying vector field can be uniquely resolved into the sum of a divergence-free rotational solenoidal component F_curl obeying ∇ · F_curl = 0 and a curl-free irrotational gradient component F_div obeying ∇ × F_div = 0.
🇮🇹🏛️
La prochaine fois que vous irez à Rome, il vous faudra impérativement visiter la salle Mariano Rossi, située dans la Galerie Borghese.
Croyez-moi, elle est tellement magnifique qu'elle doit être vue au minimum une fois sans sa vie...👍🤩
(🎥Ensar Karaca/IG)
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