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Embeddings Explained

Most people think AI understands language. It doesn’t. It maps it. This video explains embeddings — the mathematical structure that allows large language models to convert words, sentences, and ideas into coordinates inside high-dimensional space. Instead of storing definitions, models learn spatial relationships. Meaning becomes proximity. Similarity becomes distance. Geometry becomes cognition. We examine how embeddings power modern systems like large language models, semantic search, recommendation engines, and retrieval-augmented generation. You’ll see how vectors, cosine similarity, and distributional learning replace symbolic definitions — and why this design enables fluency while quietly limiting true understanding. If you want to understand how AI actually works internally — beyond interfaces, prompts, and surface outputs — this is where the structure begins. This is not a tutorial on tools. It is an examination of structure. #aiembeddings #machinelearning #deeplearning #vectorspace #neuralnetworks #aiexplained #llm #insidethemodel