Where do Fourier transforms appear in AI and machine learning?

Fourier ideas appear in AI whenever frequency information matters.

Examples include:

• audio processing, where signals are studied by frequency,
• image processing, where high and low frequencies correspond to edges, texture, and smooth regions,
• convolution, because Fourier transforms turn convolution into multiplication,
• spectral features for time-series models,
• positional encodings and Fourier features in some neural networks.

Not every neural network explicitly computes a Fourier transform, but the idea of decomposing a signal into frequency components is everywhere in modern data processing.

For images, a model may not say "Fourier" on the surface, but filters often learn low-frequency and high-frequency patterns. Fourier analysis gives a language for understanding those patterns.