AI doesn't see images—it processes them as grids of millions of numbers.

Digital images are fundamentally grids of pixels. For a standard color image, each pixel is represented by three numbers corresponding to Red, Green, and Blue (RGB) values, typically ranging from 0 to 255. A 12-megapixel photo contains 12 million pixels, which translates into 36 million individual data points for an AI to process.Computer vision models, such as Convolutional Neural Networks (CNNs), use mathematical filters called kernels to scan these grids. These kernels perform matrix multiplication to detect edges, gradients, and textures. This approach traces back to Yann LeCun's groundbreaking work in 1989 at Bell Labs, where he developed the first CNN to recognize handwritten zip codes for the U.S. Postal Service.Modern AI systems from OpenAI and Google are trained on massive datasets like ImageNet, which contains over 14 million labeled images. During training, the AI adjusts millions of internal parameters to minimize the error between its predictions and actual labels. Through this process, the machine learns to recognize complex objects by identifying hierarchical patterns in numerical data—a fundamentally different approach than how the human eye perceives the visual world.