History may soon repeat itself with a novel new platform: networks of AI agents carrying out instructions from prompts and sharing them with other AI agents, which could spread the instructions ...

Quantum computing and its threat to current encryption and the unknown threat of powerful quantum automated by advanced AI.

Researchers from OpenAI, Anthropic, and Google DeepMind found that adaptive attacks bypassed 12 AI defenses that claimed near ...

In the domain of metamaterials, the push toward automated design has been accelerated by advances in generative machine learning. The advent of deep ...

Cisco's AI Security and Safety Framework includes a unified taxonomy that aims to classify a range of AI safety threats, such as content safety failures, agentic risks, and supply chain threats. Cisco ...

Introduction: The study addresses adversarial threats in Autonomous Vehicle Platooning (AVP) using machine learning. Methods: A novel method integrating active learning with RF, GB, XGB, KNN, LR, and ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...

Member of the ICMAT, AXA-ICMAT Chair in Adversarial Risk Analysis and Member of the Spanish Royal Academy of Sciences, Instituto de Ciencias Matemáticas (ICMAT-CSIC) David Rios Insua has received ...

Abstract: This research evaluates a cognitive AI model for unmanned aerial vehicles (UAV) detection using adversarial machine learning (AML) techniques. We test the model using the VisDrone dataset ...