Orthographic variation is very common in Luxembourgish texts due to the absence of a fully-fledged standard variety. Additionally, developing NLP tools for Luxembourgish is a difficult task given the lack of annotated and parallel data, which is exacerbated by ongoing standardization. In this paper, we propose the first sequence-to-sequence normalization models using the ByT5 and mT5 architectures with training data obtained from word-level real-life variation data. We perform a fine-grained, linguistically-motivated evaluation to test byte-based, word-based and pipeline-based models for their strengths and weaknesses in text normalization. We show that our sequence model using real-life variation data is an effective approach for tailor-made normalization in Luxembourgish.

When the Tesla Model S was released a decade ago, it was unlike anything else on the market. And I'm not talking about the fact that it's electric -- I mean the interior design. Franz von Holzhausen, Tesla's lead designer, put an enormous 17-inch vertical touchscreen in the center of the car. It dominates the dash, and you control nearly everything in the car through it -- for better or worse. "Everything that we do at Tesla has to be beautiful," said von Holzhausen in July 2017 at the launch of the Tesla Model 3. "But beauty is only great if it's functional."

In this last talk of day 01 of Computer Vision DevCon 2020, Brandon Gilles, CEO at Luxonis, explained the world's first embedded spatial AI platform -- OpenCV AI Kit (OAK). Embedded spatial AI platform provides an immense capability to imitate human-level perception in applications. This is essential for specific perception and interaction tasks that could conventionally be only solved by a person can now be performed by embedded systems. This talk starts with the explanation of OAK-1 and OAK-D and further explains how the OpenCV AI kit has two eyes to bring out the depth perception, especially the distance. Case in point -- the system can now quickly identify a good onion from a bad one on a conveyor belt and throw it back into the field.