A Developer's Late-Night Contemplation on Machine Learning and Physics
Description
This image uses the 'He must be thinking about other girls' meme format. A man and a woman are shown in bed, lying with their backs to each other. The woman has a concerned and suspicious look on her face, with superimposed text that reads, 'he must be thinking about other girls'. In contrast, the man is wide awake, staring thoughtfully into the distance, with text over him revealing his actual thoughts: 'where is the physics in machine learning?'. The humor stems from the vast difference between the woman's relational insecurity and the man's deeply abstract, scientific preoccupation. For an experienced tech audience, this is relatable as it portrays how developers can become engrossed in complex, fundamental questions about their field at any time, often to the bewilderment of their partners. The question itself is non-trivial, touching upon the intersection of information theory, statistical mechanics, and computation
Comments
13Comment deleted
It's all fun and games until you realize your loss function is just a Hamiltonian and you're not sure if your optimizer is finding a true ground state or just another local minimum
Nothing kills romance faster than realizing your loss function forgot about the Navier-Stokes constraints
After 15 years of building ML systems, you realize the real physics in machine learning is the conservation of energy - specifically, how gradient descent always finds the path of least resistance to overfit your training data while maximizing your AWS bill's potential energy
This perfectly captures the existential crisis of every ML engineer at 2 AM: we've built models that can generate photorealistic images and beat humans at Go, yet we're essentially doing high-dimensional curve fitting with millions of parameters and zero understanding of causality. Meanwhile, physicists are over here with their elegant equations that actually explain *why* things happen, not just predict *what* happens given enough training data. The real question isn't where the physics is - it's whether we're just glorified polynomial regressors with fancy activation functions and a GPU budget that would make a small country jealous
After two decades, I’ve found that ‘physics in ML’ means sneaking invariances and PDE residuals into the loss, then pretending SGD respects conservation laws while product demands real‑time inference on a phone
Nothing keeps you up like realizing your LSTM just invented a perpetual‑motion CFD surrogate because the loss function forgot mass conservation
She frets over other girls; he's lamenting how transformers conserve attention but ignore conservation laws
big question Comment deleted
I dont get it Comment deleted
Same Comment deleted
well... it was BOLTZMANN machine... and later neural DIFFUSION... i guess... 🥴 Comment deleted
For those who don't get it: https://www.nobelprize.org/prizes/physics/2024/press-release/ Comment deleted
This year’s two Nobel Laureates in Physics have used tools from physics to develop methods that are the foundation of today’s powerful machine learning. John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data. Geoffrey Hinton invented a method that can autonomously find properties in data, and so perform tasks such as identifying specific elements in pictures. 🤯 Comment deleted