Recent research has employed chemical reaction networks (CRNs), which harness biochemical processes for computations that translate interactions involving biochemical species into graphical form.

By learning the relevant features of clinical images along with the relationships between them, the neural network can outperform more traditional methods.

Mechanistic interpretability is emerging as a strategic advantage for businesses looking to deploy AI responsibly.

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light instead of electricity to process information—promise faster speeds and ...

When developing a proposal, potential submitters are encouraged to consider compatibility with existing formats such as the ...

The starting point of a FlexOlmo project is a so-called anchor AI model. Every organization that participates in the project ...

Researchers have uncovered how primate brains transform flat, 2D visual inputs into rich, 3D mental representations of ...

A new study utilizes compact neural networks to uncover the underlying mechanisms behind human decision-making. Scientists have long studied how people and animals make decisions, often looking at how ...

Scientists at UCSF combined advanced brain-network modeling, genetics, and imaging to reveal how tau protein travels through ...

New research highlights how astrocytes, long considered mere support cells, actively shape brain network dynamics.

Using this information, the model can then tell us the probability of a drug-protein interaction that we did not previously ...

Learn how to build your own GPT-style AI model with this step-by-step guide. Demystify large language models and unlock their ...