AI became powerful because of interacting mechanisms: neural networks, backpropagation and reinforcement learning, attention, training on databases, and special computer chips.
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.
SHANNON, CLARE, IRELAND, February 5, 2026 /EINPresswire.com/ -- A new publication from Opto-Electronic Technology; DOI ...
Researchers use compressed AI models to discover "dot-detecting" neurons in the macaque visual cortex, offering a new path for Alzheimer’s therapy.
OpenAI experiment finds that sparse models could give AI builders the tools to debug neural networks
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 ...
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'Thermodynamic computer' can mimic AI neural networks — using orders of magnitude less energy to generate images
Researchers generated images from noise, using orders of magnitude less energy than current generative AI models require.
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 ...
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