This article explores the potential of large language models (LLMs) in reliability systems engineering, highlighting their ...

The paper addresses the AI shutdown problem, a long-standing challenge in AI safety. The shutdown problem asks how to design AI systems that will shut down when instructed, will not try to prevent ...

This study examined the relationship between the Monetary Policy Rate (MPR) and inflation across five continents from 2014 to 2023 using both Frequentist and Bayesian Linear Mixed Models (LMM). It ...

What is the neuropsychological basis for the brain's ever-changing contextualized goals? I explore this question from the perspective of the Affect Management Framework (AMF).

Bayesian inference is a statistical method of inductive reasoning based on the reassessment of competing hypotheses in the presence of new evidence. Conceptually similar to the scientific method ...

Are you a social savant who easily reads people's emotions? Or are you someone who leaves an interaction with an unclear understanding of another ...

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 ...

This important study reports three experiments examining how the subjective experience of task regularities influences perceptual decision-making. Although the evidence linking subjective ratings to ...

In 2025, large language models moved beyond benchmarks to efficiency, reliability, and integration, reshaping how AI is ...

We consider the usual proportional hazards model in the case where the baseline hazard, the covariate link, and the covariate coefficients are all unknown. Both the baseline hazard and the covariate ...

What’s often misunderstood about Google’s incrementality testing and how Bayesian models use probability to guide better decisions.