COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |

University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > Mathematical Knowledge at Scale

## Mathematical Knowledge at ScaleAdd to your list(s) Download to your calendar using vCal - Stephen Watt (University of Waterloo)
- Monday 10 July 2017, 16:00-17:00
- Seminar Room 1, Newton Institute.
If you have a question about this talk, please contact info@newton.ac.uk. BPRW01 - Computer-aided mathematical proof The world's largest organism is a clonal colony of quaking aspen in Utah, with some 40,000 trunks spanning 43 hectares and massing an estimated 6,000 metric tons. This is not a forest of individuals, but a single, living organism. We may think of mathematical knowledge in the same way. It is the goal of the International Mathematical Knowledge Trust (IMKT) to develop a global digital mathematics library, not as a comprehensive collection of individual articles, but as an integrated knowledge base, both for human readers and machine services. This talk presents the goals of the IMKT , the direction of its first steps, challenges to be overcome, and a long-term picture of scalable mathematical knowledge integration. This talk is part of the Isaac Newton Institute Seminar Series series. ## This talk is included in these lists:- All CMS events
- Featured lists
- INI info aggregator
- Isaac Newton Institute Seminar Series
- School of Physical Sciences
- Seminar Room 1, Newton Institute
Note that ex-directory lists are not shown. |
## Other listsThe obesity epidemic: Discussing the global health crisis NEWCOM# Emerging Topics Workshop Medical Genetics Graduate Student Meeting## Other talksImaging surfaces with atoms Child Kingship from a Comparative Perspective: Boy Kings in England, Scotland, France, and Germany, 1050-1250 Propagation of Very Low Frequency Emissions from Lightning Psychology and Suicidal Behaviour How to Design a 21st Century Economy - with Kate Raworth Recovery conditions of compressed sensing approach to uncertainty quantification |