Deep learning and neural networks are becoming increasingly accurate at performing complicated tasks. But are they robust as well. Researchers at MIT-IBM Watson AI Lab have developed methods to evaluate the robustness of neural networks against adversarial examples.
DARPA's XAI initiative aims to shed light inside the black box of artificial intelligence algorithms. Project Manager Dave Gunning explains how the agency is working to create explainable AI tools that will build trust and reliability into AI models.
Boston University's Kate Saenko discusses explainable AI and interpreting decisions made by deep learning algorithms and neural networks.
Regulating facial recognition technology will help prevent abuse of the technology by law enforcement. But is the big tech's support for regulation sincere?
The future is not some place we are going, but one we are creating. The paths are not to be found, but made. And the activity of making them changes both the maker and the destination
Researchers at IBM and MIT have developed a technique that helps understand generative adversarial networks (GAN), one of the most complicated artificial intelligence models that have been created in the past year. Their findings defy some of the general perceptions we have about AI complexity
Augmented reality, VR's younger sibling has moved beyond gaming and entertainment. Let's have a look at the current state of affairs.
AlphaStar, an AI developed by Alphabet subsidiary DeepMind, bested human players in famous real-time strategy game StarCraft II. Here's why it's a milestone achievement for the artificial intelligence industry.