Categories: AI/ML News

Using the brain as a model inspires a more robust AI

Most artificially intelligent systems are based on neural networks, algorithms inspired by biological neurons found in the brain. These networks can consist of multiple layers, with inputs coming in one side and outputs going out of the other. The outputs can be used to make automatic decisions, for example, in driverless cars.

Brain-inspired computing: Using noise to regulate information flow in neural networks

Researchers have developed a learning mechanism that uses the natural variability of neural activity—often dismissed as random "noise"—to understand how synapses buried deep inside brain networks—or brain-inspired devices—can adapt to improve the learning capabilities of the networks as a whole.

September 11, 2026

In "AI/ML News"

Introducing Fortuna: A library for uncertainty quantification

December 17, 2022

In "FAANG"

Building a Softmax Classifier for Images in PyTorch

Last Updated on January 9, 2023 Softmax classifier is a type of classifier in supervised learning. It is an important building block in deep learning networks and the most popular choice among deep learning practitioners. Softmax classifier is suitable for multiclass classification, which outputs the probability for each of the…

January 16, 2023

In "AI/ML Research"

AI Generated Robotic Content