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

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

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

We study deep neural networks and their use in semiparametric inference. We establish novel rates of convergence for deep feedforward neural nets. Our new rates are sufficiently fast (in some cases ...

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

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.

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 ...

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 ...

“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 ...

Building neural networks from scratch in Python with NumPy is one of the most effective ways to internalize deep learning fundamentals. By coding forward and backward propagation yourself, you see how ...