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 understand how these systems produce their responses, a group of physicists at Harvard University has developed a simplified mathematical model of learning in neural networks that can be analyzed mathematically using the tools of statistical physics.
Artificial intelligence, once the realm of science fiction, claimed its place at the pinnacle of scientific achievement Monday in Sweden. In a historic ceremony at Stockholm’s iconic Konserthuset, John Hopfield and Geoffrey Hinton received the Nobel Prize in Physics for their pioneering work on neural networks — systems that mimic…
Prof. Liu Yang from the University of Chinese Academy of Sciences (UCAS), in collaboration with her colleagues from Renmin University of China and Massachusetts Institute of Technology, has proposed a novel network, namely, the physics-encoded recurrent convolutional neural network (PeRCNN), for modeling and discovery of nonlinear spatio-temporal dynamical systems based…
The Nobel Prize in Physics was awarded to two scientists on Tuesday for discoveries that laid the groundwork for the artificial intelligence used by hugely popular tools such as ChatGPT.