Big Math and the One-Brain Barrier A Position Paper and Architecture Proposal
This addresses the problem of scaling mathematical research for mathematicians and AI developers, but it is incremental as it builds on existing ideas in mathematical software.
The paper tackles the challenge of 'big mathematics', where complex proofs require extensive human and computational effort, by proposing an information model that integrates inference, computation, tabulation, and narration around mathematical knowledge.
Over the last decades, a class of important mathematical results have required an ever increasing amount of human effort to carry out. For some, the help of computers is now indispensable. We analyze the implications of this trend towards "big mathematics", its relation to human cognition, and how machine support for big math can be organized. The central contribution of this position paper is an information model for "doing mathematics", which posits that humans very efficiently integrate four aspects: inference, computation, tabulation, and narration around a well-organized core of mathematical knowledge. The challenge for mathematical software systems is that these four aspects need to be integrated as well. We briefly survey the state of the art.