Conjectures hold a special status in mathematics. Good conjectures epitomise milestones in mathematical discovery, and have historically inspired new mathematics and shaped progress in theoretical physics. Hilbert’s list of 23 problems and André Weil’s conjectures oversaw major developments in mathematics for decades. Crafting conjectures can often be understood as a problem in pattern recognition, for which Machine Learning is tailor-made. In this talk, I will propose a framework that allows a principled study of a space of mathematical conjectures. Using this framework and exploiting domain knowledge and machine learning, we generate a number of conjectures in number theory and group theory. I will present evidence in support of some of the resulting conjectures and present a new theorem. I will lay out a vision for this endeavour, and conclude by posing some general questions about the pipeline.