Neurosymbolic AI combines the learning of LLMs with teaching the machine formal rules that should make them more reliable and ...

The main problem with big tech's experiment with artificial intelligence (AI) is not that it could take over humanity. It's that large language models ...

AI was an elite technology—expensive, experimental, and largely confined to Silicon Valley giants. Today, it’s quietly ...

Listen to the first notes of an old, beloved song. Can you name that tune? If you can, congratulations — it’s a triumph of your associative memory, in ...

Understanding neural network dynamics is a cornerstone of systems neuroscience, bridging the gap between biological neural networks and artificial neural ...

A new concept of kinetic modules in biochemical networks could revolutionize the understanding of how these networks function ...

It works well as a standalone AI chatbot, but Gemini's true value comes from its bundled cloud storage and deep integration ...

Ophthalmology Times connects eye care professionals with surgery, imaging, gene therapy, & diagnostic advances to enhance ...

By upgrading to the 3-nm process, Marvell is positioning the new Ara DSP to be a key building block of 1.6-Tb/s optical ...

The complex interactions between the nervous and immune systems are pivotal in both health and disease, but the cellular and molecular mechanisms underlying ...

However, the best-known computational intelligence approaches with such characteristics, namely, deep neural networks, are often criticized for lacking transparency. In this article, a novel ...

In this paper, we strive to learn jointly optimized user scheduling and hybrid precoding policy with graph neural network (GNN), which has emerged as a powerful tool for optimizing resource allocation ...