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 ...

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 ...

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 ...

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 ...

The field of adversarial attacks in natural language processing (NLP) concerns the deliberate introduction of subtle perturbations into textual inputs with the aim of misleading deep learning models, ...

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 ...

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 ...

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 ...

HealthTree Cure Hub: A Patient-Derived, Patient-Driven Clinical Cancer Information Platform Used to Overcome Hurdles and Accelerate Research in Multiple Myeloma Adversarial images represent a ...

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 ...