  We use the latest techniques in machine-learning to study whether from the
landscape of Calabi-Yau manifolds one can distinguish elliptically fibred ones.
Using the dataset of complete intersections in products of projective spaces
(CICY3 and CICY4, totalling about a million manifolds) as a concrete
playground, we find that a relatively simple neural network with
forward-feeding multi-layers can very efficiently distinguish the elliptic
fibrations, much more so than using the traditional methods of manipulating the
defining equations. We cross-check with control cases to ensure that the AI is
not randomly guessing and is indeed identifying an inherent structure. Our
result should prove useful in F-theory and string model building as well as in
pure algebraic geometry.
