The rapid ascent of large-scale artificial intelligence has provided neuroscience with a new set of powerful tools for modeling complex cognitive functions.

Researchers use statistical physics and "toy models" to explain how neural networks avoid overfitting and stabilize learning in high-dimensional spaces.

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

Now, artificial intelligence (AI) tools are providing powerful new ways to address long-standing problems in physics. “The ...

Harvard University physicists have created a simplified mathematical model to study how neural networks learn, using statistical physics to uncover underlying patterns. The approach, likened to early ...

Contemporary artificial intelligence (AI) systems, such as the models underpinning the functioning of ChatGPT, image ...

Artificial intelligence systems based on neural networks—such as ChatGPT, Claude, DeepSeek or Gemini—are extraordinarily ...

The researchers stress that this scale of work was made possible by a coordinated ecosystem of computational services: CyVerse for data storage, OSG OS Pool for high-throughput computing, Pegasus for ...

Harvard physicists have developed a simplified mathematical model to better understand neural networks, potentially informing the design of AI study tools. At the same time, platforms like ChatGPT are ...