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    Embeddings

    Embeddings are numerical representations of real-world concepts that artificial intelligence (AI) solutions use to find patterns, make meaning, and assess relationships.

    What Are Embeddings?

    The complexity of human language poses challenges for AI solutions, but when concepts are broken down into numerical terms, it’s far easier for those solutions to learn. Embeddings simplify real-world concepts by representing semantic meaning in numerical values. Embeddings — also known as vectors — also streamline the organizational process that underpins an AI solution. By translating a word, image, or video into a number, the AI can more easily organize and store that concept, then pull it back out to understand relationships with other similar concepts. AI solutions create embeddings via their neural network. Information (in the form of words or images) gets input into the AI, the network translates that into a numerical value, and then classifies it based on semantic characteristics. In this way, embeddings power AI’s deep learning process.

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