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The video tutorial from Cohere AI discusses word and sentence embeddings, essential components of large language models. It explains that language models aim to enable computers to understand and process language, which is inherently made of words, while computers operate using numbers. To bridge this gap, word embeddings convert words into numerical representations, and sentence embeddings do the same for sentences. This process is automated through a neural network, which analyzes context; words that frequently appear together in similar contexts are positioned closer in the numerical space. The result is an effective method for capturing semantic relationships in language.
