Researchers have developed photonic computing chips that overcome key limitations for a type of neural network known as a ...

Researchers report building photonic computing chips that use light pulses to train spiking neural networks on robotic-control-style benchmark tasks, aiming to shift more of the learning workload from ...

Continual learning in neural networks addresses the challenge of adapting to new information accumulated over time while retaining previously acquired knowledge. A central obstacle to this process is ...

A two-chip photonic neuromorphic system performs real time spiking reinforcement learning using only light, achieving GPU-class energy efficiency.

MicroCloud Hologram Inc. (NASDAQ: HOLO), ("HOLO" or the "Company"), a technology service provider, released a core quantum machine learning technology oriented toward sequential learning tasks—the ...

People's decisions are known to be influenced by past experiences, including the outcomes of earlier choices. For over a century, psychologists have been trying to shed light on the processes ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

Researchers develop TweetyBERT, an AI model that automatically decodes canary songs to help neuroscientists understand the neural basis of speech.

Researchers generated images from noise, using orders of magnitude less energy than current generative AI models require.