During the past, well, let's think, 10+ years, I've been involved in Machine Learning projects (okay, they call it "AI" now, but that's an argument for a different time) that require lots of data to be ingested / used to train the Machine Learning model. For instance, if it is text, we need lots of snippets of text that can then be categorised, so that the model "knows" that the 10 words grouped together in that context have a "meaning" and that meaning is labelled - this helps with questioning later on. It also helped to extract out the entities and relationships between the wording to give more context. Here's a simple example using the spaCy tooling to give you an example: https://www.labellerr.com/blog/image-annotation-services-and-data-labeling-for-ai-models/ If it is imagery, we need lots of images with the segmented parts identified, usually a bounding box(?) to identify the elements / objects inside the image, so, again, this can b...
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