Extreme events with potential deadly outcomes, such as those organized by terror groups, are highly unpredictable in nature and an imminent threat to society. In particular, quantifying the likelihood of a terror attack occurring in an arbitrary space-time region and its relative societal risk, would facilitate informed measures that would strengthen national security. This paper introduces a novel self-exciting marked spatio-temporal model for attacks whose inhomogeneous baseline intensity is written as a function of covariates. Its triggering intensity is succinctly modeled with a Gaussian Process prior distribution to flexibly capture intricate spatio-temporal dependencies between an arbitrary attack and previous terror events. By inferring the parameters of this model, we highlight specific space-time areas in which attacks are likely to occur. Furthermore, by measuring the outcome of an attack in terms of the number of casualties it produces, we introduce a novel mixture distribution for the number of casualties. This distribution flexibly handles low and high number of casualties and the discrete nature of the data through a {\it Generalized ZipF} distribution. We rely on a customized Markov chain Monte Carlo (MCMC) method to estimate the model parameters. We illustrate the methodology with data from the open source Global Terrorism Database (GTD) that correspond to attacks in Afghanistan from 2013-2018. We show that our model is able to predict the intensity of future attacks for 2019-2021 while considering various covariates of interest such as population density, number of regional languages spoken, and the density of population supporting the opposing government.

AI is supposed to be the most powerful pattern detection and prediction technology in the world. It therefore begs the question: Can we use AI to predict future global pandemics far enough in advance to tamp them down or prevent them altogether? It's an enormously consequential question, because the answer is not only relevant to sounding the alarm on future pandemics; it also speaks to the potential of AI for businesses. The short answer is "yes-ish." "Yes" because AI, specifically machine learning (ML), analyzes historical data to find the key variables that are predictive of any event, such as a pandemic.

AI is supposed to be the most powerful pattern detection and prediction technology in the world. It, therefore, begs the question: Can we use AI to predict future global pandemicsfar enough in advance to tamp them down or prevent them altogether? It's an enormously consequential question because the answer is not only relevant to sounding the alarm on future pandemics; it also speaks to the potential of AI for businesses. From cancelled conferences to disrupted supply chains, not a corner of the global economy is immune to the spread of COVID-19. The short answer is "yes-ish."