When you show a cat to a human, we instantly recognise what we see: a living, breathing creature, complete with fur, whiskers, and a certain unmistakable look. Show the same image to an artificial intelligence model, however, and it perceives something very different — a huge grid of numbers.
At its core, a digital image is nothing but pixels: tiny points arranged in a neat grid, with each pixel carrying values for colour and brightness. In a grayscale image, a pixel’s brightness is typically represented by a number between 0 (pure black) and 255 (pure white). Colour images, meanwhile, assign three numbers per pixel — corresponding to red, green, and blue intensities. But regardless of the palette, the principle remains the same: an image, to a machine, is pure data.
When a deep learning model is trained to recognise objects — such as a cat — it does not grasp “catness” in any conceptual sense. Instead, it is trained to detect statistical patterns in these pixel values, across millions of examples.
It is crucial to understand that the model never knows what a cat is. It optimises only for pattern recognition — neither for understanding or meaning.
This training process happens inside artificial neural networks, particularly within convolutional neural networks (CNNs), which are specially designed for analysing images. During training, the CNN applies small mathematical operations called convolutions across the pixel grid. These operations are designed to highlight local patterns — small clusters where brightness or colour values change noticeably. A convolution might, for instance, detect an edge where brightness shifts sharply, or a corner where two edges meet.
The AI does not “look” for a cat’s ear or “search” for triangles in any conscious sense.
Instead, it identifies low-level features — such as lines, curves, and regions where pixel values change in consistent, measurable ways.
Higher layers of the network combine these simpler patterns into increasingly complex arrangements. From changes in brightness and texture, the AI builds statistical associations between these feature groupings and known labels, such as “cat”.
If, across countless training examples, certain combinations of edges, curves, and textures frequently correspond with the label “cat”, the model gradually adjusts its internal settings — called weights — to reinforce that association.
It is crucial to understand that the model never knows what a cat is. It optimises only for pattern recognition based on mathematical relationships in the pixel data — neither for understanding or meaning.
When presented with a new image, the AI processes the pixel patterns through these trained filters and predicts, with some probability, whether the arrangement resembles what its experience says is probably a “cat”.
For AI, a cat is not a furry creature. It is a constellation of pixel arrangements, filtered, weighted, and ultimately categorised according to the cold logic of mathematics and probability.
