Why neural networks are like black holes: a lesson from 'bald' models ⚡ экспресс
Original: "Artificial Entanglement in the Fine-Tuning of Large Language Models"
By measuring the 'entanglement' within neural networks, scientists realized they behave like black holes—externally identical no matter what changes inside.
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Inside a black hole, any information 'goes bald'—only mass and charge are visible from outside. Language models are similar: lean training setups create whimsical entanglement patterns inside, but the output is pattern-independent. That's the key to swift AI adaptation.
🎯 The 'no-hair' theorem: a black hole is described solely by mass, charge, and spin. All other information about consumed matter is erased.