Machine learning, for all its benevolent potential to detect cancers and create collision-proof self-driving cars, also threatens to upend our notions of what's visible and hidden. It can, for ...

Louise Matsakis covers cybersecurity, internet law, and online culture for WIRED. Now, a leading group of researchers from MIT have found a different answer, in a paper that was presented earlier this ...

In recent years, the media have been paying increasing attention to adversarial examples, input data such as images and audio that have been modified to manipulate the behavior of machine learning ...

Did you know Neural is taking the stage this fall? Together with an amazing line-up of experts, we will explore the future of AI during TNW Conference 2021. Secure your ticket now! There’s growing ...

Deep learning has come a long way since the days when it could only recognize handwritten characters on checks and envelopes. Today, deep neural networks have become a key component of many computer ...

The patch only fools a specific algorithm, but researchers are working on more flexible solutions The patch only fools a specific algorithm, but researchers are working on more flexible solutions is a ...

The algorithms that computers use to determine what objects are–a cat, a dog, or a toaster, for instance–have a vulnerability. This vulnerability is called an adversarial example. It’s an image or ...

Adversarial attacks are an increasingly worrisome threat to the performance of artificial intelligence applications. If an attacker can introduce nearly invisible alterations to image, video, speech, ...

An autonomous train is barreling down the tracks, its cameras constantly scanning for signs that indicate things like how fast it should be going. It sees one that appears to require the train to ...

We’ve touched previously on the concept of adversarial examples—the class of tiny changes that, when fed into a deep-learning model, cause it to misbehave. In March, we covered UC Berkeley professor ...