Neural networks are distributed computing structures inspired by the structure of a biological brain and aim to achieve cognitive performance comparable to that of humans but in a much shorter time.

Optical illusions, quantum mechanics and neural networks might seem to be quite unrelated topics at first glance. However, in new research published in APL Machine Learning, I have used a phenomenon ...

Artificial intelligence systems that are designed with a biologically inspired architecture can simulate human brain activity before ever being trained on any data, according to new research from ...

Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Researchers applied the mathematical theory of synchronization to clarify how recurrent neural networks (RNNs) generate predictions, revealing a certain map, based on the generalized synchronization, ...

Physics-informed neural networks are faster and more accurate at predicting space junk trajectories than conventional methods, says Sierra Space. Credit: Alamy Stock Photo Sierra Space says it can ...

A deep neural network was trained using quantum tunneling to mimic the human ability to view optical illusions. When you purchase through links on our site, we may earn an affiliate commission. Here’s ...

AI applications like ChatGPT are based on artificial neural networks that, in many respects, imitate the nerve cells in our brains. They are trained with vast quantities of data on high-performance ...

The interaction of machine learning and optics/photonics is transforming the way we design new photonic structures, unearth latent physical laws, and develop intelligent photonic devices. Despite ...