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.

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

Another theory held that the forces between two particles falls off exponentially in direct relationship to the distance between two particles and that the factor by which it drops is not dependent on ...

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 ...

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

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 ...

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

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

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

An artificial neural network, a type of artificial intelligence which can engage in machine learning, can be patented, the ...

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 ...