In recent years artificial intelligence methods have found multiple applications in cosmology. These methods are particularly well suited for the analysis of large scale structure, as they are capable of creating rich and complex models of non-linear data. In this talk I will present the first cosmology constraints derived using deep convolutional neural networks, using the KiDS-450 dataset, achieving more constraining power than the equivalent analysis with conventional methods. This analysis relies on the training sets consisting of grids of precise simulations. Generative AI models can also be used to create simulations of various observables, considerably speeding up simulation time. I will discuss the current and future directions in the AI-oriented cosmological analysis.