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Mathematical Practice, Crowdsourcing, and Social Machines

机译:数学实践,众包和社交机

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The highest level of mathematics has traditionally been seen as a solitary endeavour, to produce a proof for review and acceptance by research peers. Mathematics is now at a remarkable inflexion point, with new technology radically extending the power and limits of individuals. Crowdsourcing pulls together diverse experts to solve problems; symbolic computation tackles huge routine calculations; and computers check proofs too long and complicated for humans to comprehend. The Study of Mathematical Practice is an emerging interdisciplinary field which draws on philosophy and social science to understand how mathematics is produced. Online mathematical activity provides a novel and rich source of data for empirical investigation of mathematical practice - for example the community question-answering system mathover-flow contains around 40,000 mathematical conversations, and polymath collaborations provide transcripts of the process of discovering proofs. Our preliminary investigations have demonstrated the importance of "soft" aspects such as analogy and creativity, alongside deduction and proof, in the production of mathematics, and have given us new ways to think about the roles of people and machines in creating new mathematical knowledge. We discuss further investigation of these resources and what it might reveal. Crowdsourced mathematical activity is an example of a "social machine" , a new paradigm, identified by Berners-Lee, for viewing a combination of people and computers as a single problem-solving entity, and the subject of major international research endeavours. We outline a future research agenda for mathematics social machines, a combination of people, computers, and mathematical archives to create and apply mathematics, with the potential to change the way people do mathematics, and to transform the reach, pace, and impact of mathematics research.
机译:传统上,最高水平的数学被认为是一种孤独的尝试,可以为研究同行们的审查和接受提供证据。随着新技术从根本上扩展了个人的力量和局限性,数学正处于一个显着的拐点。众包吸引了各种各样的专家来解决问题;符号计算可解决庞大的常规计算;并且计算机检查证明的时间过长且难以让人理解。数学实践研究是一个新兴的跨学科领域,它依靠哲学和社会科学来理解数学是如何产生的。在线数学活动为对数学实践进行实证研究提供了新颖而丰富的数据源-例如,社区问答系统mathover-flow包含大约40,000个数学对话,而多学科合作则提供了发现证据过程的笔录。我们的初步研究证明了“软”方面(例如类比和创造力以及演绎和证明)在数学生成中的重要性,并为我们提供了思考人和机器在创建新的数学知识中的作用的新方法。我们讨论了对这些资源的进一步调查及其可能揭示的内容。众包数学活动是“社会机器”(Berners-Lee)确定的新范式的一个示例,该范式将人和计算机的组合视为一个解决问题的实体,并且是重大国际研究工作的主题。我们概述了数学社会机器的未来研究议程,该研究将人,计算机和数学档案库结合起来以创建和应用数学,并有可能改变人们做数学的方式,并改变数学的范围,速度和影响力研究。

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