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.

Lab-grown brain tissue learned to balance a virtual pole with 46% accuracy, revealing how living neural networks adapt and ...

A Queen’s research team has developed a new way to train AI systems so they focus on the bigger picture instead of specific, ...

The human brain begins learning through spontaneous random activities even before it receives sensory information from the external world. The technology developed by the KAIST research team enables ...

Researchers generated images from noise, using orders of magnitude less energy than current generative AI models require.

Generative artificial intelligence (AI) — such as ChatGPT and Dalle-2 — is undoubtedly one of the most groundbreaking and discussed technologies in recent history. Its applications and related issues ...

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known ...

AI became powerful because of interacting mechanisms: neural networks, backpropagation and reinforcement learning, attention, training on databases, and special computer chips.