In real-world applications, node features in graphs often contain noise from various sources, leading to significant performance degradation in GNNs. Although several methods have been developed to enhance robustness, they rely on the unrealistic assumption that noise in node features is independent of the graph structure and node labels, thereby limiting their applicability. To this end, we introduce a more realistic noise scenario, dependency-aware noise on graphs (DANG), where noise in node features create a chain of noise dependencies that propagates to the graph structure and node labels. We propose a novel robust GNN, DA-GNN, which captures the causal relationships among variables in the data generating process (DGP) of DANG using variational inference. In addition, we present new benchmark datasets that simulate DANG in real-world applications, enabling more practical research on robust GNNs. Extensive experiments demonstrate that DA-GNN consistently outperforms existing baselines across various noise scenarios, including both DANG and conventional noise models commonly considered in this field.

Mage, an archaic term for a magician or someone who makes magic, is now also the name of a Silicon Valley startup that's demonstrating some magic of its own. The Santa Clara, California-based company today released to general availability its prize low-code tool for product developers to build AI ranking models. Year-old Mage has been in private beta for the last 12 months working closely with early paying customers to make its tool user-friendly, intuitive, and simple to use, the company said. After working with hundreds of product developers at Airbnb, CEO and cofounder Tommy Dang saw that those developers knew how AI could be used to improve their product, but that they also had to rely on data science resources to help implement their ideas. Data scientists do not come inexpensively anywhere in the world.

In recent years, many tools have been proposed to design interactive scenarios. The aim of these tools is to provide users with a framework in order to write a story with many branches which will be unfolded by an artificial intelligence application. However, the consistency and quality of the generated narratives are not guaranteed (deadlocks, flaws, etc.). Previous work has defined the properties of a valid interactive scenario and proposed an approach as well as a tool based on a formal model, Linear Logic, to validate these properties. Nevertheless, the application of this tool requires many special skills and so is not really suitable for normal users. In this paper, we present an authoring tool which allows modeling interactive scenarios and analysing them at the structural level using the deduction rules in Linear Logic. Thanks to our tool, normal users are able to create and validate interactive scenarios in comparison with a rich set of predefined properties/criteria of quality.

Anti-virus software has a hard time keeping up. Piles of new viruses come out each week, so cybersecurity unicorn Cylance is taking what it claims to be a completely new approach: artificial intelligence that learns to recognize malicious code based on an analysis of viruses from the past. It calls the new product CylancePROTECT. In an AMA on Reddit today, the company's head of reseach, Jon Miller, wrote: "Cylance was the first AI built to statically analyze and convict malware pre-execution. We definitely didn't invent AI, but we were the first to use it this way to deliver pre-execution protection. Many other products have been using machine learning, it's just that it was used to support legacy methodologies of protection/detection, using ML to identify trends so static signatures could be built, which in a world where attackers are creating individual pieces of malware to avoid signatures, results in a severe lack of efficacy, thats the problem Cylance was built to solve."