Turing Award and CACM
Reviewed April 2018
One of the major highlights at ACM is the annual announcement of the recipient(s) of the Turing Award. This announcement is followed in short order by significant media coverage by ACM’s publications as well as the global press.
The Award recipient is feature prominently each year in ACM’s flagship publication, Communications of the ACM. Editorial coverage typically includes a brief biography, a profile, a Q&A, and several photographs of the recipient(s).
Every Turing recipient is expected to present a Turing lecture on a topic of their choice at a forum of their choice. This highly anticipated lecture is often videotaped in its entirety and available for viewing in the ACM Digital Library. The lecture is often included in the Proceedings of the conference at which it was presented, also available in the ACM Digital Library. The video and lecture will also be uploaded to the Turing website (http://amturing.acm.org/), which presents a rich collection of Turing information, including citations, bibliographic material, videos, lectures, photos, and more.
For maximum exposure to a worldwide audience, each Turing recipient is strongly encouraged to submit his/her lecture for publication in CACM. This might involved a bit of editorial rework, as space limitations for a magazine venue often requires a lengthy lecture to be tightened or repositioned for a general audience. Lectures published in CACM receive global recognition and appreciation.
ACM Queue’s “Research for Practice” is your number one resource for keeping up with emerging developments in the world of theory and applying them to the challenges you face on a daily basis. In this installment, Dan Crankshaw and Joey Gonzalez provide an overview of machine learning server systems. What happens when we wish to actually deploy a machine learning model to production, and how do we serve predictions with high accuracy and high computational efficiency? Dan and Joey’s curated research selection presents cutting-edge techniques spanning database-level integration, video processing, and prediction middleware. Given the explosion of interest in machine learning and its increasing impact on seemingly every application vertical, it's possible that systems such as these will become as commonplace as relational databases are today.
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