Science

Researchers have developed a novel watermarking technique that embeds a hidden signature within deep neural networks, making it harder for malicious actors to steal or repurpose AI models.

A novel algorithm addresses the challenges of training graph neural networks on fragmented, diverse datasets without compromising performance or generalization.

Researchers have developed a new method leveraging the power of large language models to automatically identify and analyze the human values expressed within written content.

A novel architectural approach embeds societal values directly into AI design, enabling continuous oversight and adaptation of complex socio-technical behaviors.

Researchers have developed a highly effective phishing detection system leveraging the power of character-level analysis and advanced machine learning techniques.

A new hybrid modeling approach combines the accuracy of physics-based simulations with the adaptability of data-driven machine learning to overcome limitations in complex system modeling.

As disinformation becomes increasingly sophisticated and commercially available, a coordinated, multidisciplinary approach is crucial to defend against large-scale attacks on public trust.

As artificial intelligence increasingly steps into the role of mental health support, ensuring users understand how and why AI arrives at its conclusions is paramount.

Researchers have created a comprehensive platform to rigorously evaluate the capabilities of artificial intelligence agents in diagnosing and resolving complex network issues.

A new AI framework leverages patient data to forecast pain episodes in lung cancer patients, offering the potential for proactive and personalized pain management.