Machine learning's transformative shift mirrors the MapReduce moment, revolutionizing efficiency with decentralized consensus ...

Deep learning has emerged as a transformative paradigm in modern computational science, leveraging neural networks to approximate complex functions across a variety of domains. Central to this ...

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

Two new research efforts are offering deeper insight into how artificial intelligence can be made safer and more effective. Harvard physicists have developed a simplified, physics-inspired model to ...

The future of conflict prediction relies on combining technical ability, institutional governance and ethical responsibility.

During my first semester as a computer science graduate student at Princeton, I took COS 402: Artificial Intelligence. Toward the end of the semester, there was a lecture about neural networks. This ...

Spiking Neural Networks (SNNs) represent the "third generation" of neural models, capturing the discrete, asynchronous, and energy-efficient nature of ...

Harvard physicists have developed a simplified mathematical model to better understand how neural networks learn, likening the work to Kepler’s early laws of planetary motion. The model could help ...

AI thrives on data but feeding it the right data is harder than it seems. As enterprises scale their AI initiatives, they face the challenge of managing diverse data pipelines, ensuring proximity to ...

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...