Artificial Intelligence and Machine Learning
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The foundational work of the first AI for Good Summit allows the AI community to work together to create an infrastructure for responsible communication, development, and trust.
Making Machine Learning Robust Against Adversarial Inputs
Such inputs distort how machine-learning-based systems are able to function in the world as it is.
Many applications are surfacing for neural network systems, including reliable identification of diseases (for example, diabetic retinopathy, cancerous cells), situational awareness for self-driving cars, and a raft of other hard recognition and optimization problems.
Teach the Law (and the AI) ‘Foreseeability’
AI is a sort of idiot savant that can be unpredictably, and potentially, dangerously literal.
Bringing the Internet to the (Developing) World
A growing number of low-cost (and free!) solutions aim to open the Internet to developing regions.
ACM A.M. Turing Award recipients David Patterson and John Hennessy developed the "dangerous" idea that software should be simpler so it can be executed more quickly, which evolved into the Reduced Instruction Set Computer architecture.
Deep Learning Hunts for Signals Among the Noise
Neural networks can deliver surprising, and sometimes unwanted, results.
By focusing on users' abilities rather than disabilities, designers can create interactive systems better matched to those abilities.
Technical Perspective: Measuring Optimization Potential with Coz
In "Coz: Finding Code that Counts with Causal Profiling," Curtsinger and Berger describe causal profiling, which tell programmers exactly how much speed-up bang to expect for their optimization buck.
ACM A.M. Turing award recipients John Hennessy and David Patterson have introduced generations of students to reduced instruction set computing.
Coz: Finding Code that Counts with Causal Profiling
This paper introduces causal profiling. Unlike past profiling approaches, causal profiling indicates exactly where programmers should focus their optimization efforts, and quantifies their potential impact.
Articulation of Decision Responsibility
Can we establish a locution for the results of a program that does not ascribe decision-making power?
Automated canarying quickens development, improves production safety, and helps prevent outages.
Data Acquisition in Vehicular Ad Hoc Networks
The data comes from multiple optimal sources in parallel, helping reduce addressing and data-acquisition latency.
Speech Emotion Recognition: Two Decades in a Nutshell, Benchmarks, and Ongoing Trends
Tracing 20 years of progress in making machines hear our emotions based on speech signal properties.
In this paper we define more precisely the never-ending learning paradigm for machine learning, and present one case study: the Never-Ending Language Learner (NELL), which achieves a number of the desired properties of a never-ending learner.
Technical Perspective: Breaking the Mold of Machine Learning
"Never-Ending Learning" is the latest and one of the most compelling incarnations of Tom Mitchell and his collaborators' research investigating how to broaden the machine learning field.
Computer programs are being used to emulate humans to fool less-sophisticated programs into treating computer-generated actions as if they originate from a human. This is an important practical problem.
Commenting on Code, Considering Data’s Bottleneck
Edwin Torres considers the enduring value of code comments, while Walid Saba wonders if we have overreacted to the knowledge acquisition bottleneck.
Shrinking Machines, Cellular Computers
Scientists are using DNA and RNA to build the world's tiniest robots and computing devices.
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.
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