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

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

The progress in AI over the past decade is beginning to suggest answers to some of our deepest questions about human intelligence. Below, Tom Griffiths shares five key insights from his new book, The ...

Explore how core mathematical concepts like linear algebra, probability, and optimization drive AI, revealing its ...

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

Researchers use compressed AI models to discover "dot-detecting" neurons in the macaque visual cortex, offering a new path for Alzheimer’s therapy.

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

OpenAI researchers are experimenting with a new approach to designing neural networks, with the aim of making AI models easier to understand, debug, and govern. Sparse models can provide enterprises ...

Blending logic systems with the neural networks that power large language models is one of the hottest trends in artificial intelligence. Now, however, the computer-science community is pushing hard ...

Stephen Whitelam, a researcher whose work spans thermodynamic theory and machine learning, has described a framework for generating images from pure noise by using the physics of heat and motion ...

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 ...