Overview: Clear problem definitions prevent wasted effort and keep machine learning work focused.Clean, well-understood data ...

The strong role of socioeconomic factors underscores the limits of purely spatial or technical solutions. While predictive models can identify where risk concentrates, addressing why it does so ...

Traditional rule-based systems, once sufficient for detecting simple patterns of fraud, have been overwhelmed by the scale, ...

From Human Judgment to Algorithmic Intelligence Traditional driving relies on human perception, reaction time, and experience ...

That’s the aim of predictive cyber resilience (PCR)—an emerging approach to security built on intelligence, automation and ...

As AI is embedded inside systems, teams must design APIs with governance, observability and scalability in mind.

Overview:  AI is transforming finance, but ethical challenges around bias and fairness remain unresolved across institutions.Algorithmic decisions in lendi ...

Based Detection, Linguistic Biomarkers, Machine Learning, Explainable AI, Cognitive Decline Monitoring Share and Cite: de Filippis, R. and Al Foysal, A. (2025) Early Alzheimer’s Disease Detection from ...

As enterprises accelerate the rollout of generative AI and autonomous agents, many are finding that traditional governance models are failing to keep pace with the speed, scale, and risk of modern AI.

AI could accelerate the energy transition, but widespread adoption is stalled by unresolved trust, risk and governance concerns across sectors.

The institutions recognized with Global Finance’s AI in Finance Awards 2026 have not merely adopted AI; it is now a ...