It's simple enough for AI to seem to comprehend data, but devising a true test of a machine's knowledge has proved difficult
From ACM Opinion | December 17, 2021
Attempting to mitigate problems associated with the trend toward massive dataset scaling.
Vincent J. Hellendoorn, Anand Ashok Sawant From Communications of the ACM | January 1, 2022
John Martinis, former chief architect of Google Sycamore, offers a measured perspective on quantum's progress
IEEE Spectrum From ACM Opinion | December 9, 2021
While helpful in comparing AI performance, benchmarks are often taken out of context, sometimes to harmful results
TechTalks From ACM Opinion | December 7, 2021
Charging computer scientists to develop the science needed to best achieve the performance and cost goals of accelerator-level parallelism hardware and software...Mark D. Hill, Vijay Janapa Reddi From Communications of the ACM | December 1, 2021
Improving on data portability.
Marshall W. Van Alstyne, Georgios Petropoulos, Geoffrey Parker, Bertin Martens From Communications of the ACM | December 1, 2021
Stanford PhD student discusses recent research on understanding, building, and controlling pre-trained models
The Gradient From ACM Opinion | November 16, 2021
Computational biologist brings the power of machine learning to researchers seeking answers in mountains of cell images
Quanta Magazine From ACM Opinion | November 5, 2021
Data science expert talks about how trustworthy AI and causal reasoning can help society solve real-world problems
MIT Technology Review From ACM Opinion | November 2, 2021
Privacy engineers are essential to both preventing and responding to organizational privacy problems.
Lea Kissner, Lorrie Cranor From Communications of the ACM | November 1, 2021
Reporting machine learning-based research can help to improve transparency and reproducibility
Nature Computational Science From ACM Opinion | October 22, 2021