1. What Is an Embedding? An embedding is the “translator” that converts language into numbers, enabling AI models to understand and process human language. AI doesn’t comprehend words, sentences, or syntax—it only works with numbers. Embeddings assign a unique numerical representation (a vector) to words, phrases, or sentences. Think of an embedding as a language map: each word is a point on the map, and its position reflects its relationship with other words. For example: “cat” and “dog” might be close together on the map, while “cat” and “car” are far apart. 2. Why Do We Need Embeddings? Human language is rich and abstract, but AI models need to translate it into something mathematical to work with. Embeddings solve several key challenges: (1) Vectorizing Language Words are converted into vectors (lists of numbers). For example: “cat” → [0.1, 0.3, 0.5] “dog” → [0.1, 0.32, 0.51] These vectors make it possible for models to perform mathematical operations like comparing, clustering, or predicting relationships. (2) Capturing Semantic Relationships The true power of embeddings lies in capturing semantic relationships between words. For example: “king - man + woman ≈ queen” This demonstrates how embeddings encode complex relationships in a numerical format. (3) Addressing Data Sparsity Instead of assigning a unique index to every word (which can lead to sparse data), embeddings compress language into a limited number of dimensions (e.g., 100 or 300), making computations much more efficient. 3. How Are Embeddings Created? Embeddings are generated through machine learning models trained on large datasets. Here are some popular methods: (1) Word2Vec One of…