Modern natural language processing (NLP) systems face challenges in adaptability, privacy, and require immense computational resources, limiting real-world deplorability beyond the cloud. A new comprehensive review analyzes how recent paradigms of federated learning, reinforcement learning and their integration into NLP can address these issues and become the foundation of emerging intelligent systems (EIS), making AI more reliable, safer and deployable on grounded devices.

Spin waves, or magnons, have attracted attention as information carriers with potential applications in logic circuits, memory devices, and related technologies. Magnonic crystals (MCs) are important platforms for controlling magnon propagation and can be tailored for specific applications. However, general design rules for optimizing targeted properties in MCs remain unclear. In a new study, researchers developed an inverse-design framework to discover optimized MC structures based on target properties and help establish more effective design rules.

Presentation to feature first-in-human results for ISM6331, a novel, AI-designed pan-TEAD inhibitor targeting the Hippo pathway

Global Phase 1 multicenter study evaluates safety, tolerability, and preliminary efficacy in patients with mesothelioma and other advanced solid tumors

Researchers have developed a low-cost, semi-automated, AI-driven method that uses remote cameras to survey bumblebees and potentially other insects.

Researchers have developed a new artificial intelligence-based method that could significantly improve the speed and accuracy of  brain tumor detection and classification from MRI scans.

Infants use eye contact with speakers to work out what to pay attention to and synchronise their brainwaves, helping them learn language, scientists in Cambridge and Singapore have shown.