An RIT scientist has been tapped by the National Science Foundation to solve a fundamental problem that plagues artificial neural networks. Christopher Kanan, an assistant professor in the Chester F.

An RIT scientist has been tapped by the National Science Foundation to solve a fundamental problem that plagues artificial neural networks. Christopher Kanan, an assistant professor in the Chester F.

Mark R. Anderson of Strategic News Service, the Future in Review Conferences and Pattern Computer. Popular methods of artificial intelligence have an “explainability problem” — the inability ...

The development of neural networks to create artificial intelligence in computers was originally inspired by how biological systems work. These 'neuromorphic' networks, however, run on hardware ...

AI models like artificial neural networks and language models help scientists solve a variety of problems, from predicting the 3D structure of proteins to designing novel antibiotics from scratch.

We introduce a variational representation of quantum states based on artificial neural networks with a variable number of hidden neurons. A reinforcement-learning scheme we demonstrate is capable of ...

Artificial neural networks can also filter huge amounts of data through connected layers to make predictions and recognize patterns, following rules they taught themselves. By now, people treat neural ...