Issues such as abrupt changes in speed limits and incomplete lane markings are among the most influential factors that can predict road crashes, finds new research by University of Massachusetts Amherst engineers. The study then used machine learning to predict which roads may be the most dangerous based on these features.

Published in the journal Transportation Research Record, the study was a collaboration between UMass Amherst civil and environmental engineers Jimi Oke, assistant professor; Eleni Christofa, associate professor; and Simos Gerasimidis, associate professor; and civil engineers from Egnatia Odos, a publicly owned engineering firm in Greece.

The most influential features included road design issues (such as changes in speed limits that are too abrupt or guardrail issues), pavement damage (cracks that stretch across the road and webbed cracking referred to as “alligator” cracking), and incomplete signage and road markings.

According to OpenAI, its new video AI called Sora is a “world simulator,” that taught itself how to understand 3D spaces.

As a part of a multi-part series, TechCrunch is highlighting women innovators — from academics to policymakers — in the field of AI.

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Companies are using artificial intelligence tools to screen potential employees but they are screening out highly-qualified candidates.