neural networks tagged posts

Edge computing is an emerging IT architecture that enables the processing of data locally by smartphones, autonomous vehicles, local servers, and other IoT devices instead of sending it to be processed at a centralized large data center. This approach could allow artificial intelligence (AI) models and other computational systems to perform tasks rapidly, while consuming less power.

Despite the potential of this approach, typically local devices have a limited battery capacity and restricted computing capabilities. This means they often need to send data to remote cloud servers via the internet to complete complex calculations...

Machine learning models called convolutional neural networks (CNNs) power technologies like image recognition and language translation. A quantum counterpart—known as a quantum convolutional neural network (QCNN)—could process information more efficiently by using quantum states instead of classical bits.

Photons are fast, stable, and easy to manipulate on chips, making photonic systems a promising platform for QCNNs. However, photonic circuits typically behave linearly, limiting the flexible operations that neural networks need.

Adaptive state injection in photonic QCNNs
In a study published in Advanced Photonic...

Long-term musical training may mitigate the age-related decline in speech perception by enhancing cognitive reserve, according to a study published in PLOS Biology by Claude Alain from the Baycrest Academy for Research and Education, Canada, and Yi Du from the Chinese Academy of Sciences.

Normal aging is typically associated with declines in sensory and cognitive functions...

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light instead of electricity to process information—promise faster speeds and lower energy use than traditional electronics.

But despite their potential, these systems have struggled to match the accuracy of digital neural networks. A key reason: most photonic systems still mimic the structure and training methods of digital models, introducing errors when translating from software to hardware.

Now, a research team from Northwestern Polytechnical University and Southeast University in China has developed a new kind of photonic neural network tha...