In the second installment of the Generative AI Foundations series, we delve into the mechanics of how AI models interpret raw input. The video breaks down three key concepts: tokens, tokenization, and embeddings.
- Tokens and Tokenization: Language models break text into smaller units called tokens, which can be words, subwords, or characters. Tokenization is the process of splitting input into these pieces, enabling the model to process language efficiently.
- Embeddings: Tokens are then converted into numerical vectors called embeddings. These vectors capture semantic meaning, allowing the model to understand relationships between words.
- Vector Representations and Semantic Similarity: Embeddings exist in a high-dimensional space where words with similar meanings are positioned closer together. This spatial arrangement powers tasks like semantic search and analogy completion.
This foundational knowledge is essential for anyone looking to grasp how generative AI models like GPT actually "understand" and generate human language.