Mathematics has been an important topic in artificial intelligence (AI) research already from the very beginning. In recent discussions, however, mathematics is not seen as part of the success stories in AI. While AI tools are used in mathematical practice, they are limited to rule-based systems with limited applications. In this paper, I explore two emerging machine-learning based approaches to developing an AI system that could prove mathematical theorems that are interesting to human mathematicians. In the top-down approach, the AI is trained with mathematical texts, as is done in the training of large language models. In the bottom-up approach, the AI is developed stage by stage to emulate human cognitive capacities for mathematics. I then analyse the two approaches in terms of their fit with philosophical theories of mathematical knowledge.