Biological neural networks are immensely complex systems underlying all aspects of cognition and behavior. Despite significant advances in neuroscience, a ...

The Nobel Prize in Physics was awarded to two scientists for discoveries that laid the groundwork for the artificial intelligence. British-Canadian Geoffrey Hinton ...

Neural architecture search (NAS) and machine learning optimisation represent rapidly advancing fields that are reshaping the way modern systems are designed and deployed. By automating the process of ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...

Binary digits and circuit patterns forming a silhouette of a head. Neural networks and deep learning are closely related artificial intelligence technologies. While they are often used in tandem, ...

In the machine learning world, the sizes of artificial neural networks — and their outsize successes — are creating conceptual conundrums. When a network named AlexNet won an annual image recognition ...

Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language ...

As you begin your hybrid quantum approach, here are the advantages, use cases and limitations to keep in mind.

With artificial intelligence and machine learning (AI/ML) processors and coprocessors roaring across the embedded edge product landscape, the quest continues for high-performance technology that can ...

An AI-driven computational toolkit, Gcoupler, integrates ligand design, statistical modeling, and graph neural networks to predict endogenous metabolites that allosterically modulate the GPCR–Gα ...