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Machine learning has become a popular tool across the sciences. In this talk I discuss research at the interface of machine learning and theoretical physics. After a more general introduction, I will focus on diffusion models, currently the leading generative AI approach used for image generation used in e.g. DALL-E and Stable Diffusion. I will relate diffusion models to stochastic quantisation in quantum field theory and employ it to generate configurations for scalar quantum fields on a two-dimensional lattice. We end with some speculations on possible applications.