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This video tutorial from Cohere AI discusses word and sentence embeddings, which are essential for large language models. The purpose of these embeddings is to enable computers to understand and process language, which is inherently composed of words, through numerical representation. Word embeddings convert words into numerical arrays, while sentence embeddings do the same for sentences, ensuring their numerical representation is contextually meaningful. This conversion is typically achieved by a neural network that analyzes the context in which words and sentences appear together, bringing similar terms or phrases closer in their numerical space. The process results in effective language understanding by the model.