Some ML papers suffer from flaws that could mislead the public and stymie future research.
Zachary C. Lipton, Jacob Steinhardt From Communications of the ACM | June 2019
Achieving consistency where distributed transactions have failed.
Martin Kleppmann, Alastair R. Beresford, Boerge Svingen From Communications of the ACM | May 2019
Cloud-delivery networks could dramatically improve blockchains' scalability, but clouds must be provably neutral first.
Aleksandar Kuzmanovic From Communications of the ACM | May 2019
A discussion with Jacek Czerwonka, Michaela Greiler, Christian Bird, Lucas Panjer, and Terry Coatta
CACM Staff From Communications of the ACM | February 2019
Automation and a little discipline allow better testing, shorter release cycles, and reduced business risk.
Thomas A. Limoncelli From Communications of the ACM | January 2019
How Google moved its virtual desktops to the cloud.
Matt Fata, Philippe-Joseph Arida, Patrick Hahn, Betsy Beyer From Communications of the ACM | November 2018
Three critical design points: Joint learning, weak supervision, and new representations.
Alex Ratner, Chris Ré, Peter Bailis From Communications of the ACM | November 2018
What happens when we wish to actually deploy a machine learning model to production?
Dan Crankshaw, Joseph Gonzalez, Peter Bailis From Communications of the ACM | August 2018