Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

Networks are systems comprised of two or more connected devices, biological organisms or other components, which typically ...

To make accurate predictions and reliably complete desired tasks, most artificial intelligence (AI) systems need to rapidly ...

Google LLC today detailed RigL, an algorithm developed by its researchers that makes artificial intelligence models more hardware-efficient by shrinking them. Neural networks are made up of so-called ...

Learning to code doesn’t require new brain systems—it builds on the ones we already use for logic and reasoning.

A new technical paper titled “Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware” was published by researchers at Purdue University, Pennsylvania State ...

Early detection of ovarian cancer, the deadliest gynecologic cancer, is crucial for reducing mortality. Current noninvasive risk assessment measures include protein biomarkers in combination with ...

The learning algorithm that enables the runaway success of deep neural networks doesn’t work in biological brains, but researchers are finding alternatives that could. In 2007, some of the leading ...

More than two decades ago, neural networks were widely seen as the next generation of computing, one that would finally allow computers to think for themselves. Now, the ideas around the technology, ...