Recent advances in quantum computing and artificial intelligence have created a rich two-way interaction between the two fields. On the one hand, quantum systems provide new perspectives on representation, optimization, and sampling in machine learning. On the other hand, classical AI methods are becoming increasingly useful for addressing central problems in quantum technologies. In this talk, I will present an overview of this emerging interface from the perspectives of Quantum for AI and AI for Quantum. I will highlight how quantum models may offer new possibilities for prediction and generative modeling, how AI can support large-scale quantum simulation and computation through improved design, inference, and error decoding, and how mathematical tools can provide insight into some of these models. I will also discuss some of the main challenges that remain, including trainability, generalization, scalability, and sample complexity.