In artificial intelligence, particularly in machine learning and natural language processing (NLP), dimensions refer to the number of numerical values (features) in a vector embedding.
In artificial intelligence (AI) and machine learning, dimensions refer to the number of elements (features) in a vector representation of data. Each element in this vector captures a different aspect or attribute of the data, such as semantic meaning, syntactic structure, or visual characteristics. Dimensionality is crucial because it determines how much information a vector can encode.
For instance, in natural language processing (NLP), embeddings like BERT or Word2Vec convert words or sentences into high-dimensional vectors — typically with 300, 768, or even over 1,000 dimensions. In image recognition, embeddings from models like CLIP may have hundreds to thousands of dimensions to represent visual features like shapes, textures, or colors.
Example: A 3-dimensional vector: [0.1, 0.3, 0.9] — simple and used for toy examples. A 1,536-dimensional vector: [0.23, -0.11, …, 0.45] — used in advanced AI models to represent complex data.
Higher-dimensional vectors can capture more nuances but may lead to higher computational costs and overfitting in some cases — a challenge known as the “curse of dimensionality.” Reducing or optimizing dimensions (e.g., with PCA or dimensionality reduction techniques) is essential for efficient AI applications.