Science

Researchers are boosting prediction accuracy by introducing a method that separates coarse-grained trends from fine-grained details in time-series data.

As AI systems become increasingly complex and permeate safety-critical applications, researchers are exploring innovative ways to ensure their reliability and prevent potentially catastrophic failures.

A novel approach to long-term financial modeling incorporates realistic volatility and drift uncertainty for improved forecasting accuracy.

New research reveals that commonly used AI models for predicting patient diagnoses can disproportionately fail vulnerable groups when faced with the messy reality of imperfect clinical data.

A new approach leverages the power of qudits to build interpretable quantum neural networks for improved credit risk assessment.

A new framework leverages causal inference to automatically pinpoint the root causes of performance issues in radio access networks.

New research shows how advanced language models can accurately gauge policy uncertainty from textual sources, offering a powerful upgrade to traditional methods.

This review explores how integrating artificial intelligence and formal knowledge representation is reshaping Failure Mode and Effects Analysis for more reliable and adaptable systems.

A new approach combines the pattern recognition of deep learning with the logical rigor of symbolic AI to build more trustworthy and ethical systems.

New research reveals how cultural differences impact the ethical reasoning of artificial intelligence and proposes a framework for building more globally responsible AI systems.