Deep neural networks (DNNs), which power modern artificial intelligence (AI) models, are machine learning systems that learn ...

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

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 ...

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 ...

Using spatial transcriptomics and AI, researchers redefined the mouse brain’s geography, uncovering hundreds of new ...

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 ...

Spiking Neural Networks (SNNs) are a cutting-edge approach to artificial intelligence, designed to emulate the brain's architecture and functionality. Their ...

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 ...

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Will deep learning really live up to its ...

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, ...