Image recognition algorithms are nothing like our eyes, and here is blobby static proof. These images fooled an algorithm into seeing a gorilla, bikini, ...

New research from MIT is shedding light on the mysterious inner workings of Generative Adversarial Networks (GANs), and can reveal how the algorithms make chillingly human-like decisions about the ...

Mohamad Hassoun, author of Fundamentals of Artificial Neural Networks (MIT Press, 1995) and a professor of electrical and computer engineering at Wayne State University, adapts an introductory ...

In addition to pure deep neural networks (DNNs), sometimes people use hybrid vision models, which combine deep learning with classical machine learning algorithms that perform specific sub-tasks.

Researchers used computer vision techniques and machine learning and neural network algorithms to see if artificial intelligence properly classified images properly as spiders, wasps, praying mantises ...

Most current AI is built on neural networks, which are series of algorithms that recognize patterns in massive datasets, similar to how a human brain works. Our brains use billions of cells called ...

The basic algorithms that drive these systems aren’t that different than what we had in the ’80s. But now, thanks to people like Dean, we have the computing power they need to thrive.

Neural networks could also be used to analyze vast amounts of data. In 2009, a group of researchers used neural network techniques to win the Netflix Grand Prize.. At the time, Netflix was holding ...

The process uses neural networks to apply the look and feel of one image to another, and appears in apps like Prisma and Facebook. These style transfers, however, are stylistic, not photorealistic.