{"id":17266,"date":"2026-10-05T08:42:05","date_gmt":"2026-10-05T15:42:05","guid":{"rendered":"https:\/\/mattfife.com\/?p=17266"},"modified":"2026-09-15T08:59:31","modified_gmt":"2026-09-15T15:59:31","slug":"ai-in-mathematics","status":"publish","type":"post","link":"https:\/\/mattfife.com\/?p=17266","title":{"rendered":"AI in mathematics"},"content":{"rendered":"\n
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The goal of mathematics has always been about human understanding of the ideas. When a mathematician solves a problem then they can explain to others the new concepts that lead them to this discovery, this proof, and how they came about it.<\/p>\n<\/blockquote>\n\n\n\n

This is a great application of the study of epistemology <\/em>– the philosophical discussion of knowing and understanding. It seeks to define the key points required to say we actually know something. In the case of this video – he is describing some of the things he believes are core principles of saying you actually understand a mathematical statement. This is a whole field of understanding and study – and one that I think is really coming to the forefront with the advent of AI.<\/p>\n\n\n\n

This isn’t new, since 2010 there have been systems that can mathematically describe phenomenon. The Eureka machine<\/a> was one of them that brought up this very same question over a decade ago. <\/p>\n\n\n\n

The question soon becomes: computers can generate models for us that describe phenomenon; but to say we really know something as humans – we seem to desire more. We want to understand the underlying principles and even purpose. While physical sciences have increasingly stick to answering the ‘what’ without answering purpose\/intent\/meaning – we absolutely rely on that when deciding the guilt\/innocence of human behavior. <\/p>\n\n\n\n

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