Science

A new study examines how generative AI is being realistically implemented within an energy company, revealing employee expectations and key adoption challenges.

New research reveals that telling AI systems why they’re generating information can subtly shift their results, boosting short-term performance at the expense of real-world reliability.

New research explains why likelihood-based anomaly detection, often unreliable with images, performs surprisingly well with tabular data when using deep generative models.

New research reveals that large language models exhibit surprisingly human-like biases in how they make choices, but surprisingly rational behavior when assessing beliefs.

Generating realistic financial datasets without revealing sensitive customer information is a growing challenge, and new research highlights the difficult tradeoffs involved.

This review explores how deep learning techniques are being deployed to forecast electricity prices across diverse market timescales, from day-ahead to real-time balancing.

A new analysis reveals how intelligently choosing data points during model training can dramatically improve both accuracy and energy efficiency.

A new technique allows large language models to improve their reasoning by recognizing and revisiting uncertain answers.

New research reveals how intense market sentiment amplifies uncertainty in cryptocurrency trading, leading to wider price gaps and increased costs for investors.

A new dynamic bootstrapping method improves the accuracy and efficiency of training machine learning interatomic potentials by actively filtering out noisy data during the learning process.