A new generation of automatic theorem provers eliminate bugs in software and mathematics.
Theory
Advertisement
Disputed Google research is receiving further scrutiny, despite the company's inaction in open-sourcing its data or source code as promised.
Serendipity: When Research in One Area Leads to a Positive Result in Another
A problem of the extremely optimal family of circulant graphs: when research in one scientific field gives an unexpected result in another.
Considering the Impact of Technology on Society
Moshe Vardi on having the difficult conversation about technology and social responsibility.
Virtual Influencers in the Real World
We are increasingly engaging with these digital personalities, online and offline.
Metrics for Success: Why and How to Evaluate Privacy Choice Usability
Assessing the usability of choice and consent mechanisms.
Management and technology challenges of AI-enabled application projects.
We show how to employ functional programming techniques to solve with elegance the challenge of using a high-level language to describe functionality and a separate schedule to specify how the program should be optimized.
Technical Perspective: Reconsidering the Design of User-Schedulable Languages
The breakthrough of "Achieving High Performance the Functional Way," by Bastian Hagedorn et al., is in fundamentally rethinking the design of user-schedulable languages so that decades of wisdom from traditional programming languages can be brought to bear.
(Re)Use of Research Results (Is Rampant)
Prior pessimism about reuse in software engineering research may have been a result of using the wrong methods to measure the wrong things.
Proving Data-Poisoning Robustness in Decision Trees
We present a sound verification technique based on abstract interpretation and implement it in a tool called Antidote, which abstractly trains decision trees for an intractably large space of possible poisoned datasets.
Building Machine Learning Models Like Open Source Software
Proposing a community-based system for model development.
Software Engineering of Machine Learning Systems
Seeking to make machine learning more dependable.
Technical Perspective: Beautiful Symbolic Abstractions for Safe and Secure Machine Learning
"Proving Data-Poisoning Robustness in Decision Trees," by Samuel Drews et al., addresses the challenge of processing an intractably large set of trained models when enumeration is infeasible in a clean, beautiful, and elegant manner.
Why the Simplex Method, at Age 75, is Still the Go-To Algorithm
Daniel A. Spielman and colleague Shang-hua Teng solved the mystery of the Simplex Method using a technique called smoothed analysis.
Evaluating Research Results for Practical Applications
The role of the application of research results in assessing the achievements of scientists.
ACE: Toward Application-Centric, Edge-Cloud, Collaborative Intelligence
Constructing a unified platform for the scalable, reliable, robust, and efficient development and deployment of ECCI applications.
Democratizing Domain-Specific Computing
Creating a programming environment and compilation flow that empowers programmers to create their own DSAs efficiently and affordably on FPGAs.
A Linearizability-based Hierarchy for Concurrent Specifications
Two linearizability-style correctness conditions that can be used to argue safety properties of progressively more concurrent behaviors of objects.
Shape the Future of Computing
ACM encourages its members to take a direct hand in shaping the future of the association. There are more ways than ever to get involved.
Get InvolvedCommunications of the ACM (CACM) is now a fully Open Access publication.
By opening CACM to the world, we hope to increase engagement among the broader computer science community and encourage non-members to discover the rich resources ACM has to offer.
Learn More