Vectorization
Vectorization refers to the process of transforming raw data such as text, images, or audio into numerical vectors that can be processed by machine learning models. These vectors act as mathematical representations of data, enabling algorithms to perform computations, detect patterns, and make predictions. Without vectorization, AI systems would be unable to understand real-world data.
Background and origins
Vectorization became essential as AI systems moved from structured data to unstructured inputs like language and vision. Early methods, such as one-hot encoding, were simple but limited. The field advanced significantly with distributed word embeddings like Word2Vec and GloVe, which capture semantic similarities between words. Today, contextual embeddings from transformer models (e.g., BERT, GPT) dominate, allowing richer and more accurate vector representations.
Practical applications
- Natural Language Processing (NLP): vectorizing words, sentences, or documents for tasks like translation, question answering, and sentiment analysis.
- Computer Vision: turning images into feature vectors to classify objects or detect anomalies.
- Recommender systems: embedding users and products into a shared vector space to calculate similarity and generate recommendations.
- Vector databases & semantic search: indexing data as vectors for efficient similarity search, which underpins modern retrieval-augmented generation (RAG) systems.
Challenges and debates
Choosing the right vectorization technique is critical. High-dimensional vectors can lead to the curse of dimensionality, slowing down training and inference. Conversely, low-dimensional representations may miss essential details. Another challenge is bias: if embeddings are trained on biased data, the resulting vectors can encode and amplify stereotypes or errors, raising ethical concerns in AI deployment.
