Learning to Predict the Global Risks Interconnections from the Web (Round 3)
We envision a future where journalists will no longer be limited to report past or current affairs, but they will be empowered by Artificial Intelligence to write about future events with a fair degree of certainty. Perhaps we will not accomplish this ambitious goal in this exploratory project, but we want to get closer to this end.
We propose to build a prototype based on Artificial Intelligence and Machine Learning that mines the Web and predicts the (non-obvious) interconnections of global risks that will be at the core of tomorrow's news.
Everything is connected and there are clear historical signs and cycles that produce very similar consequences. The understanding of such interconnections and causality is fundamental for a comprehensive news coverage. However, connecting the dots and discovering the multiple relationships among events, entities, and global risks are not trivial tasks for journalists. An understanding of the causes and consequences of a chain of global events, and their impact in local communities, is necessary for planning, prevention, and decision processes. Our tool seeks to help achieve this understanding. If successful, this project has the potential to achieve a significant outcome not only for the digital news room, but for society in general.