I will present 3 learning algorithms fusing scientific computing and AI for the prediction and control of complex physical systems. The algorithms are: (i) a multiscale approach to Learning the Effective Dynamics (LED) of complex systems (ii) the Remember and Forget Experience Replay (ReFer) algorithm for reinforcement learning,and (iii) a fusion of scientific computing and multi-agent reinforcement learning (SciMARL) for developing closures for unresolved dynamics of complex systems. I will describe the application of these algorithms to systems ranging from models in the AI gym to simulations of molecular systems and fish schooling. I will discuss successes and failures and hope for a dialogue on how the integration of AI and Computational science may help new discoveries in both.