These sentence embeddings transform chunks of text into n-dimensional vectors. The key feature of these kinds of embeddings is that semantically similar pieces

These sentence embeddings transform chunks of text into n-dimensional vectors. The key feature of these kinds of embeddings is that semantically similar pieces of text produces similar vectors (even when the words themselves aren't the same!) Once you have that, you can do all kinds of things. To take a few of the use cases you listed, imagine that you had a corpus of documents (perhaps responses to a survey, or data you scraped/pulled from a service like Twitter or Reddit, etc.). You can now cluster those responses (find groups of vectors that are close to each other in n-dimensional space); you can find outliers (find the vectors that are far from others); build recommendation engines (find vectors mathematically similar to vectors representing a user's previous activity), etc.