A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from ...

In 1982 physicist John Hopfield translated this theoretical neuroscience concept into the artificial intelligence realm, with the formulation of the Hopfield network. In doing so, not only did he ...

From the smallest fragment of brain tissue, the intricate blueprint of the entire brain is beginning to emerge. Researchers at Baylor College of Medicine are making several time-consuming aspects of ...

Deep learning (DL) has had unprecedented success and is now entering scientific computing with full force. However, current DL methods typically suffer from instability, even when universal ...

A look at the curious history of Venn diagrams and the way they blend logic with geometry ...

Neural networks, from GPT-4 to Stable Diffusion, are built by wiring together perceptrons, which are highly simplified simulations of the neurons in our brains.

Inspired by microscopic worms, Liquid AI’s founders developed a more adaptive, less energy-hungry kind of neural network. Now the MIT spin-off is revealing several new ultraefficient models.

A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher. Artificial neurons—the fundamental building blocks of deep neural networks—have survived almost ...

Researchers from Massachusetts Institute of Technology, California Institute of Technology, and Northeastern University created a new type of neural network: Kolmogorov–Arnold Networks (KAN ...

A neural network is a series of algorithms that seek to identify relationships in a data set via a process that mimics how the human brain works.