Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.
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A simple physics-inspired model sheds light on how AI learns
Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily powerful, yet their internal workings remain largely a "black box." To better ...
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How AI is reshaping physics learning
Artificial intelligence is revolutionizing physics by making complex concepts more intuitive, interactive, and personalized. From physics-informed neural networks to AI-powered simulations, these ...
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AI Researchers Are Confronting the Gap Between Neural Network Power and True Generalization
In 2026, neural networks are achieving unprecedented capabilities across industries, yet large-scale tests reveal persistent struggles with generalization. Researchers are exploring adaptive ...
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