Neural Concept is helping launch products at 2X the speed. It does this by capturing past knowledge into AI-based ...

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

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

Patient digital twins aim to create computational replicas of an individual’s physiology that can predict disease trajectories and treatment response.

Key advances in the development of artificial neural networks came from psychologists seeking to understand how the human mind works.

In an increasingly interconnected world, understanding the behavior and structure of complex networks has become essential ...

Sustainability advocate and AI engineer Sathya Kannan has recently unveiled a framework that claims to be capable of reducing global carbon dioxide (CO₂) emissions leveraging AI neural networks. In ...

In the rapidly evolving artificial intelligence landscape, one of the most persistent challenges has been the resource-intensive process of optimizing neural networks for deployment. While AI tools ...

Explore the parallels and differences between AI architectures and the human brain's design and functionality in processing ...

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

What if AI could keep learning like a human brain, in new conditions even after it was used, deployed & put to use in real life? A Liquid Neural Network (LNN) is a new type of artificial intelligence ...

A hunk of material bustles with electrons, one tickling another as they bop around. Quantifying how one particle jostles others in that scrum is so complicated that, beginning in the 1990s, physicists ...