Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

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

Each algorithm is built for a different type of problem, and they all engage in subtly different kinds of machine learning. Here, we'll discuss four major subtypes of software neural networks: ...

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, ...

Training AIs remains very processor-intensive, in part because traditional processing architectures are poor matches for the sorts of neural networks that are widely used. This has led to the ...

Every day, various types of sensory information fromthe external environment are transferred to the brainthrough different modalities and then processed to generate a series of coping behaviors. Among ...

Peter van der Made is the founder and CTO of BrainChip Ltd. BrainChip produces advanced AI processors in digital neuromorphic technologies. The artificial intelligence (AI) revolution is upon us, and ...

Past psychology and behavioral science studies have identified various ways in which people's acquisition of new knowledge ...

We talk a lot about AI, machine learning, and neural nets, but what's a neural net in the first place? Share on Facebook (opens in a new window) Share on X (opens in a new window) Share on Reddit ...