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The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general, priority inversion occurs in computing systems when computations that are "less important" are performed together with or ahead of those that are "more important." Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system's ability to react to critical inputs, while at the same time reducing platform cost. [ABSTRACT FROM AUTHOR]

The article discusses bias found in AI algorithmic systems -- known as Algorithmic Bias -- which returned results with errors related to skin color or gender. One paper by researchers Joy Buolamwini and Timnit Gebru documented substantial discrepancies across skin types and the reliability with which three commercial face recognition systems could classify gender. A followup study by the same researchers found that those companies that were originally audited released new classifiers with reduced error rates while other companies had much higher error rates.

The article focuses on a collaboration between researchers at Germany's Karlsruhe Institute of Technology (KIT) and pen-maker Stabilo to create an artificial intelligence (AI)-based pen to teach schoolchildren cursive handwriting. Topics include the connection between handwriting and cognitive development and the development of a digital stylus by consumer electronics company Logitech.