Neuroscientists want to understand how individual neurons encode information that allows us to distinguish objects, like ...

Steven Brightfield, Chief Marketing Officer at BrainChip, about neuromorphic computing and its Akida spiking neural network ...

Key considerations for discovery in AI-focused intellectual property (IP) litigation, including an examination of a ...

How reliable is artificial intelligence, really? An interdisciplinary research team at TU Wien has developed a method that ...

Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a ...

Artist Terence Broad makes AI produce images without any training data at all.

Recently, convolutional neural networks (CNN) have been widely used in image denoising. But with most CNN denoising methods, all the channels are treated equally and the relationship between spatial ...

AI is now a part of our everyday lives. From the massive popularity of ChatGPT to Google cramming AI summaries at the top of ...

This function detects SIFT corner points in a grayscale image, computes their centroid, and finds the pixels farthest and nearest to it. Input: 2D matrix I of grayscale image, scalar ...

Discover the basics of artificial intelligence, including neural networks and machine learning, to navigate the AI-powered world with confidence and curiosity.

A neural network’s input numbers can also represent other kinds of data. For example, a number between 0 and 1 can stand for the gray scale value of a single pixel. That means any pair of pixels can ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric ...