Science

As artificial intelligence increasingly integrates with critical infrastructure, a holistic, lifecycle-based security approach is essential to mitigate emerging threats.

A new framework leverages large language models to unify control and reasoning across radio access and core networks, promising more adaptable and efficient future networks.

Researchers have developed a method to distill complex neural networks into simpler, more understandable models by actively testing and removing redundant components.

A new framework combines the strengths of statistical learning and symbolic reasoning to move medical AI beyond simple prediction and towards robust, interpretable decision support.

New research explores the dynamics of artificial intelligence agents interacting on networks, revealing how their behaviors and the nature of shared information shape collective outcomes.

Effective fraud detection in blockchain relies heavily on the performance of Graph Neural Networks, but achieving optimal results requires careful attention to initialization and normalization techniques.

New research reveals how to trace the origins of large language model responses – whether they stem from learned knowledge or provided context.

Researchers are applying the principles of natural language processing to electrocardiogram (ECG) data, creating models that ‘understand’ heart rhythms and improve disease detection.

A new approach combines data-driven deep learning with established epidemiological principles to improve the accuracy of outbreak predictions.

As artificial intelligence systems become increasingly complex, effectively managing inherent uncertainty is crucial for safe and dependable operation.