The use of Machine Learning algorithms has been successfully demonstrated in numerous scientific applications–including particle physics and cosmology. Many of these applications conform to a broad theme of building parametric and nonparametric statistical surrogates for classification, regression or surrogate modeling. We identify key avenues for developments in the field of Artificial Intelligence, that not only comply with current uncertainty quantification requirements, but also provide opportunities for future explorations of physical laws in a scalable manner.