The ability to predict outcomes and trends can mean the difference between thriving and merely surviving. Enter artificial ...

The field of AI is notoriously filled with jargon. To better understand what is going on, here is a series of explainers, ...

“When you write code to build an artificial neural network, you're basically defining this architecture,” explained Grace Lindsay, a computational neuroscientist at New York University. She uses ANNs ...

Breakthrough innovations, bullish investments, and increasing adoption across industries are catalysts fueling robust growth.

Artificial Neural Networks (ANNs) are commonly used for machine ... Defined as the uncentered covariance matrix of the ANN’s input-output gradients averaged over the training dataset, this ...

The weights in any ANN are always just real numbers and the learning problem boils down to choosing the best value for each weight in the network. This means there are two important decisions to make ...

Artificial neural networks are inspired by the early models of sensory processing by the brain. An artificial neural network can be created by simulating a network of model neurons in a computer.

AI models like artificial neural networks and language models help scientists solve ... to the next layer of nodes based on a threshold value. Scientists train the ANN using datasets that have known ...

More information: Spyridon Chavlis et al, Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning, Nature Communications (2025). DOI: 10.1038/s41467-025 ...

Coral reefs are some of the world's most diverse ecosystems. Despite making up less than 1% of the world's oceans, one ...