Science

A new study explores whether traditional text summarization techniques can hold their own against the rising dominance of large language models in the financial news landscape.

A new machine learning framework analyzes international trade data to identify suspicious activity related to ozone-depleting substances and their replacements.

A new model combines the power of financial news analysis with historical stock data to deliver more accurate predictions than traditional methods.

Rapid advances in artificial intelligence are fueling a new arms race, shifting the dynamics of nuclear weapons proliferation and demanding a reassessment of global safeguards.

A new approach combines the strengths of large language models with human expertise to enhance the accuracy and nuance of text classification tasks.

A new study reveals that the success of graph neural networks in detecting fake news largely relies on readily available node features, not their ability to reason about complex relationships within the network.

A new approach leverages neural ordinary differential equations and temporal convolutional networks to achieve more accurate identification of power system dynamics.

A new architecture uses reinforcement learning to dynamically deploy and manage honeynets, providing richer threat intelligence and more effective cyber deception.

A new study rigorously evaluates seven neural network architectures to find the best balance between precision and diversity in e-commerce recommendation systems.

A new study explores whether framing AI evaluation as a prediction game can reveal more accurate confidence levels and accelerate learning.