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 ...

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

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 ...

SHANNON, CLARE, IRELAND, February 5, 2026 /EINPresswire.com/ -- A new publication from Opto-Electronic Technology; DOI ...

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

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 ...

Ineffable Intelligence Ltd., a startup led by former Google DeepMind principal research scientist David Silver, is reportedly raising $1 billion in funding.

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 ...