At their core, vectors are a set of numbers used to represent information. They can describe positions, relationships, or attributes—such as the coordinates of a point or the features of an object. Vectors appear in both graphic design and machine learning, but they serve different purposes. Here's a deeper look into how they compare:
In Adobe Illustrator, vectors represent graphical elements like shapes, lines, and colors using mathematical descriptions. For example:
A red circle might be defined by:
- Position: [x, y] (coordinates on the canvas)
- Radius: 50 pixels
- Color: [255, 0, 0] (RGB values for red)
These numbers form a vector that encodes all the necessary information about the circle. Unlike pixel-based images, vector graphics can be scaled infinitely without losing quality because they are defined mathematically.
While they serve different purposes, Illustrator vectors and machine learning embeddings share key similarities:
- Illustrator: Vectors describe visual and geometric properties (e.g., shape, position, color).
- ML Embeddings: Vectors describe abstract relationships (e.g., a word's similarity to other words).
- Illustrator: A vector describes the relationship between graphical elements (e.g., overlapping shapes, gradients).
- ML Embeddings: A vector describes relationships between data points (e.g., "dog" is closer to "cat" than "car").
Both rely on the concept of a vector: a set of numbers that define a position or set of properties in a space.
One compelling analogy is how computers interpret colors and words:
- Colors: A color like red is stored as an RGB vector [255, 0, 0]. To a computer, this is just a series of numbers, even though humans perceive it as "red."
- Words: A word like "dog" might be represented as a vector [0.3, 0.7, -0.2, ...]. To a computer, this is also just numbers, but it encodes relationships humans interpret as meaning.
Understanding Illustrator vectors helps demystify ML embeddings:
- Both encode complex information into numbers.
- Both rely on the concept of vectors to describe relationships or attributes.
- The principles are transferable, showing how mathematics powers diverse applications from graphic design to artificial intelligence.