Fixing the forgetting problem in artificial neural networks
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. Carlson Center for Imaging Science, received $500,000 in funding to create multi-modal brain-inspired algorithms capable of learning immediately without excess forgetting.
Today, artificial neural networks—computing systems inspired by the biological neural networks that make up human brains—are used everywhere. They power everything from autonomous cars to facial recognition systems.
But because these systems differ in important ways from human brains, they have significant limitations.