Science

Researchers are developing image filters that go beyond simple aesthetics, aiming to subtly reshape visuals to evoke specific emotional responses.

Researchers are developing techniques to move beyond obvious visual cues and identify AI-generated images with greater accuracy and reliability.

Researchers are exploring the application of quantum field theory – specifically a disordered ϕ⁴ model – as a novel machine learning approach to forecasting financial time series.

A new approach uses artificial intelligence to model long-term electricity markets and evaluate pathways to ambitious decarbonization goals.

Researchers are enhancing graph node classification by combining graph neural networks with large language models to identify both familiar and entirely new types of data points.

A new framework uses interconnected knowledge to significantly improve how large language models find and reason with relevant information.

Researchers have developed a novel model to identify and categorize hateful content and misinformation, even when it’s expressed in mixed languages.

Researchers have developed a generative model that leverages graph neural networks to automatically create functional and realistic architectural layouts.

A new pipeline harnesses the power of adaptable AI models to automatically extract crucial details from police announcements shared on social media platforms.

A new approach uses reinforcement learning to dynamically adjust the complexity of attention mechanisms within large language models, reducing computational cost without sacrificing performance.