Any biological system that is described by quantities that evolve in time and space is naturally formalized as a dynamical system. For decades dynamical systems theory played a pivotal role in theoretical and computational neuroscience, as it links biophysical and biochemical processes to neural computation. In fact, dynamical systems are computationally universal. Rather than hand-crafting computational theories of neural function based on dynamical systems, recent developments in scientific machine learning (ML) and AI suggest that we may be able to infer such dynamical-computational models directly from neurophysiological and behavioral observations. In my talk I will cover recent ML/AI architectures, training algorithms, and validation procedures toward this goal. I will discuss specifically how recent AI architectures can integrate neuroscience data from multiple modalities (like multiple single-unit recordings and behavioral choices), across diverse time scales, and across many different animals and task designs, into a joint dynamical systems model, providing steps into the direction of AI foundation models for neuroscience.