Optical character recognition (OCR), or the conversion of images of handwritten or printed text into machine-readable text, is a science that dates back to the early '70s. But algorithms have long struggled to make out characters that aren't parallel with horizontal planes, which is why researchers at Amazon developed what they call TextTubes. They're detectors for curved text in natural images that model said text as tubes around their medial (middle) axes. In a paper describing their work, the coauthors claim that their approach achieves state-of-the-art results on a popular OCR benchmark. As the researchers explain, scene text is typically broken down into two successive tasks: text detection and text recognition.

What do Google Assistant, Siri, Alexa, and Cortana have in common? They tell jokes of varying cleverness, most of which are the work of writing teams operating behind the scenes. They're entertaining, but preliminary research suggests they also play a part in making interactions with assistants engaging. In pursuit of assistants capable of tailoring jokes to individual users' tastes, Amazon researchers investigated joke selection methods that tap either a basic natural language processing model or a machine learning model. They say that when tested against production data, both approaches "positively" impacted user satisfaction and potentially improved joke-telling.